Name: Explore the World of Machine Learning with Basics Course
Price: 15 GBP
Availability: InStock

<p>Enrolling in a <strong>Healthcare Assistant</strong> course is crucial in today's modern world, where the demand for skilled healthcare professionals is ever-growing. <strong>This comprehensive program covers a wide array of essential topics, including legal and ethical aspects of healthcare, medical record maintenance, infection control, and patient rights</strong>.</p><p>With modules focusing on hygiene, mobility issues, and caregiver responsibilities, students gain practical knowledge vital for success in diverse healthcare settings. Moreover, understanding GDPR regulations ensures compliance with data protection laws, further enhancing professionalism. Job opportunities abound in the UK, with <strong>Healthcare Assistants commanding an average salary of £20,000 per year</strong>.</p><p>By undertaking this <strong>Healthcare Assistant</strong> course, individuals not only contribute to the healthcare sector but also unlock numerous benefits, including career advancement, job security, and the opportunity to make a meaningful difference in people's lives. Embrace the Healthcare Assistant course today to gain invaluable insights and skills in this vital sector.</p><p><strong>Learning Outcomes:</strong></p><p>By the end of this Healthcare Assistant course, learners will be able to:</p><ul><li>Get to know the roles and responsibilities of a Healthcare Assistant</li><li>Figure out the difficulties of working in this profession</li><li>Demonstrate knowledge of the health and safety responsibilities and the importance of hygiene in nursing</li><li>Familiarise themselves with infection control and the mobility and immobility issues of patients in nursing</li><li>Find out the rights, roles, and responsibilities of healthcare professionals</li></ul><p><strong>Key Features</strong></p><ul><li><strong>CPD Certified </strong></li><li>Instant e-certificate</li><li>Fully online, interactive course with audio voice over</li></ul><p><strong>Healthcare Assistant Bundle includes: </strong></p><ul><li><strong>Course 01: </strong>Healthcare Assistant</li><li><strong>Course 02: </strong>Infection Control for Healthcare</li><li><strong>Course 03: </strong>Healthcare GDPR</li></ul><p><strong>*** Additional Gifts ***</strong></p><ul><li><strong>Free Life Coaching Course</strong></li></ul><p><strong>*** (Offer Ends Soon) ***</strong></p><p><strong>Course Curriculum:</strong></p><p><strong>Healthcare Assistant</strong></p><ul><li>Module 01: Working in Different Healthcare Settings</li><li>Module 02: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1</li><li>Module 03: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2</li><li>Module 04: Maintaining Medical Records</li><li>Module 05: Confidentiality in a Medical Environment</li><li>Module 06: Health and Safety Responsibilities</li><li>Module 07: Hygiene in Nursing</li><li>Module 08: Infection Control</li><li>Module 09: Mobility and Immobility Issues of Patients in Nursing</li><li>Module 10: Rights and Responsibilities as a Health and Social Care Worker</li><li>Module 11: Role as A Caregiver and Healthcare Professional</li><li>Module 12: Providing Care or Treatment to People Who Lack Capacity</li><li>Module 13: Managing Service Delivery in Health and Social Care</li><li>Module 14: Medical Jargon and Terminology</li><li>Module 15: Effects of Covid-19 on Human Life</li><li>Module 16: Preventions and Social Measures to Be Taken</li><li>Module 17: Information Technology in Health Care</li><li>Module 18: Artificial Intelligence, Data Science and Technological Solutions against Covid-19</li></ul><p><strong>Infection Control for Healthcare</strong></p><ul><li>Module 1: Health and Safety in Nursing</li><li>Module 2: Hygiene in Nursing</li><li>Module 3: Infection Control</li><li>Module 4: Understanding Immune System in Nursing</li><li>Module 5: Managing for Health and Safety</li><li>Module 6: Asepsis in Nursing</li></ul><p><strong>Healthcare GDPR</strong></p><ul><li>Module 01: Introduction to GDPR</li><li>Module 02: GDPR and Healthcare Setting</li><li>Module 03: General Data Protection Regulations Explained</li><li>Module 04: Lawful Basis for Preparation</li><li>Module 05: Responsibilities and Obligations</li><li>Module 06: Electronic Medical Records</li><li>Module 07: Rights and Breaches</li></ul><p><strong>Accreditation</strong></p><p>All of our courses are fully accredited, including this Dog Training: Become A Dog Trainer Course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in Dog Training.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your Dog Training: Become A Dog Trainer Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our Dog Training: Become A Dog Trainer Course certification has no expiry dates, although we do recommend that you renew them every 12 months.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        30 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Healthcare Assistant Course can be taken by anyone who wants to understand more about the topic. With the aid of this course, you will be able to grasp the fundamental knowledge and ideas. Additionally, this Healthcare Assistant Course is ideal for:</p><ul><li>Individuals aspiring to work in healthcare settings.</li><li>Those seeking comprehensive knowledge of legal and ethical standards.</li><li>Professionals aiming to enhance their skills in infection control.</li><li>Anyone interested in healthcare data protection and GDPR compliance.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Learners do not require any prior qualifications to enrol on this Healthcare Assistant Course. You just need to have an interest in Healthcare Assistant Course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After completing this Healthcare Assistant Course you will have a variety of careers to choose from. The following job sectors of Healthcare Assistant Course are:</p><ul><li>Healthcare Assistant: £11.08 - £16.63 per hour</li><li>Support Worker: £10.50 - £20.00 per hour</li><li>Paediatric Nurse: £21.00 - £30.00 per hour</li><li>Child Support Worker: £75.00 - £95.00 per day</li><li>Child Youth Support Worker: £75.00 - £90.00 per day</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Once you've successfully completed your course, you will immediately be sent a FREE digital certificate.</p></div>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK).<br />For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10.<br />Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.</p></div>

                        </div>

Training Express

Healthcare Assistant Course

General Admission

<p><strong>*** Data Analyst Training ***</strong></p><p>Approximately <strong>3.5 million jobs</strong> are there for professionals with Data Analyst skills. Understanding, harnessing, and using data in business is no longer just a competence for a few groups of highly compensated analysts. Data analysis is increasingly becoming a requirement for many occupations. You can obtain a strong foundation in data analysis and get your foot in the growing data world with this outstanding value, six-course Data Analyst Training bundle.</p><p>You will learn the fundamentals of data analysis from the Data Analyst Training bundle, which also covers process management concepts, numerous tools &amp; techniques for data analysis, dealing with variations tactics, and chart interpretation. Additionally, you will comprehensively understand how to use Excel to analyse and visualise data. This Data Analyst Training course teaches you how to make pivot tables, maps, and forecasting data. You will also learn about Microsoft Power BI.</p><p>Our intention is to give you thorough preparation. This <strong>Data Analyst Training bundle</strong> will prepare you to work as a data analyst. Each of the data analysis topics builds upon the one before it. And this is exactly why our Training is so beneficial. We promise that everything is presented in the proper sequence and that you won't get lost along the way.</p><p><strong>So why wait? Every day is a missed opportunity.</strong></p><p><strong>Enrol Data Analysis today and enjoy a smooth learning experience.</strong></p><p><strong>Learning Outcome of Data Analyst Training bundle</strong></p><ul><li>Get introduced yourself to how to analyse data</li><li>Know more about how to structure data analysis and forecast them</li><li>Become fully aware of how to use Google data studio and Microsoft azure machine learning</li><li>Gain the ability to use JavaScript</li><li>Learn to protect your data by using a Data protection practitioner and Clinical Data Analysis</li><li>Acquire knowledge of Machine Learning and how to use them with Python</li><li>Get <strong>Data Analyst Training</strong> from the experts</li><li>Understand the basics of data science and visualisation so that you can apply them frequently</li></ul><p><strong>Key Features &amp; Benefits:</strong></p><ul><li>Quality Licence Scheme (QLS) endorsed</li><li>CPD Accredited</li><li>Lifetime Access &amp; 24/7 Support on weekdays (Monday to Friday)</li><li>Self-paced course modules</li><li>Course materials feature captivating voice-overs and visuals</li></ul><p><strong>Why Quality Licence Scheme (QLS)?</strong></p><ul><li>Achieving <strong>Quality Licence Scheme</strong> endorsed certificates can provide you with valued and credible evidence of <strong>your skills and knowledge to potential employers.</strong></li><li>Quality Licence Scheme (QLS) endorsements are awarded to training programmes and courses that <strong>meet high quality standards</strong>, ensuring that your certificate represents <strong>a valuable achievement.</strong></li><li>The stringent assessment process involved in achieving Quality Licence Scheme Endorsed Certificates ensures that you have acquired the <strong>necessary skills and knowledge to excel in your chosen field.</strong></li></ul><p>With our Data Analyst (Data Analytics) bundle course, you can gain a solid foundation in data analysis and get your foot in the thriving data world.</p><ol><li>Diploma in Data Analysis Fundamentals <strong>( QLS Level 5 Endorsed )</strong></li><li>Business Data Analysis</li><li>Data Analysis In Excel <strong>( QLS Level 5 Endorsed )</strong></li><li>Diploma in Data Analytics with Tableau <strong>( QLS Level 6 Endorsed )</strong></li><li>Data Protection</li><li>Microsoft Power BI - Master Power BI in 90 Minutes!</li></ol><p>Data Analyst (Data Analytics) bundle course will educate you on the basics of data analysis, including process management principles, various tools &amp; techniques for data analysis, the strategies to deal with variations, and the interpretation of charts. Along with that, you will also gain a clear idea of analysing and visualising data using excel. This Data Analyst course will teach you how to create pivot tables, map and forecasting data.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        150 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Analyst (Data Analytics) Training is ideal for highly motivated individuals who want to enhance their professional skills and train for the job they want!</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the Data Analyst Training, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Data Analyst Training Bundle is ideal for anyone who wants to take their career development to the next level and fast-track their career in any of the following roles:</p><ul><li>Data analytics manager</li><li>Financial analyst</li><li>Marketing analyst</li><li>Systems analyst</li><li>Data scientist</li><li>Machine learning engineer</li><li>Data analytics consultant</li></ul><p>In the UK, these professions' pay scale ranges from <strong>£45,000 to £120,000 </strong>per year.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p><strong>CPD Accredited Certification from One Education</strong></p><p>Once you have finished the bundle, you will be eligible to apply for CPD QS-accredited certificates. Certification is available in both PDF &amp; hardcopy format, which can be received by paying -</p><ul><li><strong>PDF Certificate: £9</strong></li><li><strong>Hardcopy Certificate (within the UK): £15</strong></li><li><strong>Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</strong></li></ul> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £109
        </p>
            <div> <p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After successfully completing the following courses under the bundle, learners will be able to order endorsed certificates as proof of their achievement:</p><ul><li><strong>Diploma in Data Analyst Fundamentals (within the UK: £109, International: £119)</strong></li><li><strong>Data Analyst In Excel (within the UK: £109, International: £119)</strong></li><li><strong>Diploma in Data Analytics with Tableau (within the UK: £119, International: £129</strong></li></ul> </div>

                        </div>

One Education

Data Analyst (Data Analytics) Course

<strong>All about containers, Docker, and Kubernetes for Python engineers.</strong>
        <p>Docker and Kubernetes are must-have skills for Python engineers these days. Whether your focus is on machine learning and data science or you use Python as a general programming language, you must understand Docker and Kubernetes, as they form the basis of modern cloud-native applications built using microservice architectures.
In this course, you'll learn to do the following:
&bull;	Develop and explore machine learning, data science, and Jupyter Notebooks in Docker
&bull;	Run machine learning models in production with Kubernetes and Docker Swarm
&bull;	Package your Python code into containers
&bull;	Publish your containers in image registries
&bull;	Deploy containers to production, both in Docker and Kubernetes
&bull;	Build highly modular, container-based services in a microservices way
&bull;	Monitor and maintain containerized apps
You can use the course in two ways:
&bull;	If you use Python for machine learning and data science, go top-down - start with section 7 to quickly develop practical Docker skills and use sections 2 to 6 to delve deeper into specific container topics
&bull;	If you want to use Python for building web apps and microservices, try the bottom-up approach - use the course in a linear way

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Python-in-Containers</p>
        <h2>What You Will Learn</h2><p>Build container images having Python applications and ship them to Docker Hub<br>Run Jupyter Notebooks and create virtual machines in Docker<br>Use Docker Desktop for Windows Pro and macOS and Docker Toolbox for Windows Home<br>Create custom container images from scratch and automate container image builds with Dockerfile<br>Design Flask and Django multi-container deployments and automate them with Docker Compose<br>Containerize TensorFlow models into microservices and use Kubernetes with Minikube on a development host<br>Deploy complex multi-container applications in Docker Swarm and Kubernetes</p>
        <h2>Audience</h2><p>This course is for Python programmers, data scientists, and machine learning engineers who want to use Python with Docker tools for building modern cloud-native applications.</p>
        <h2>Approach</h2><p>The course will develop your practical Docker skills and you can master Kubernetes and design your applications to run on Kubernetes</p>
        <h2>Key Features</h2><p>Become well-versed with using Docker tools to create top-class containers running your Python code * Master Docker runtime tools such as Compose and Swarm * Design your applications to run on Kubernetes and master writing Kubernetes object declarations</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-in-Containers</p>
        <h2>About the Author</h2><h3>Kris Celmer</h3><p>Kris Celmer is a Cloud Architect and has worked with many companies across Europe, Asia, and Africa, helping them to shape their private, public, and Telco clouds. He supports his customers in architecting cloud solutions, but also in the business aspects of cloud transformation. He has 25+ years' experience in the IT Industry, working at companies such as Sun Microsystems, NetApp, Citrix, and Huawei.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Docker Deep Dive</h4><p></p><br><h4>3. Build Container Images</h4><p></p><br><h4>4. Ship Containers</h4><p></p><br><h4>5. Run Containers in Docker</h4><p></p><br><h4>6. Run Containers in Kubernetes</h4><p></p><br><h4>7. Data Science & Machine Learning in Containers</h4><p></p>

Packt

Kubernetes

Docker

Python

Programming

All about containers, Docker, and Kubernetes for Python engineers.

Python in Containers

Skill Up

Healthcare Assistant

Professional Skills Course (PSC)

Health & Social Care

Trainers Course

Continuous Professional Development (CPD)

Consent

Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this

Consent in Healthcare

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Healthcare Assistant Training

<p>Embark on a captivating journey into the world of artificial intelligence with our course, '<strong>Machine Learning Basics</strong>.' This voyage begins with an immersive introduction, setting the stage for an exploration into the intricate and fascinating realm of machine learning. Envision yourself unlocking the mysteries of algorithms and data patterns, essential skills in today's technology-driven landscape. The course offers a comprehensive foray into the core principles of machine learning, starting from the very basics and gradually building to more complex concepts, making it an ideal path for beginners and enthusiasts alike.</p>
<p>As you delve deeper, each section unravels a vital component of machine learning. Grasp the essentials of regression analysis, understand the role of predictors, and navigate through the functionalities of Minitab, a key tool in data analysis. Journey through the structured world of regression trees and binary logistic regression, and master the art of classification trees. The course also emphasizes the importance of data cleaning and constructing robust data models, culminating in the achievement of learning success. This course is not just an educational experience; it's a gateway to the future of data science and AI.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Comprehend the basic principles and applications of machine learning.</li>
<li>Develop proficiency in regression analysis and predictor identification.</li>
<li>Gain practical skills in Minitab for data analysis.</li>
<li>Understand and apply regression and classification trees.</li>
<li>Acquire expertise in data cleaning and model creation.</li>
</ul>
<h3><b>Why choose this Machine Learning Basics<span> course</span>?</b></h3>
<ol>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b> and <b>CIQ </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
<li>Unlock career resources for CV improvement, interview readiness, and job success.</li>
</ol>
<h3><b>Who is this Machine Learning Basics<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Novices eager to delve into machine learning.</li>
<li>Data enthusiasts looking to enhance their analytical skills.</li>
<li>Professionals in IT and related fields expanding their expertise.</li>
<li>Academics and students in computer science and data studies.</li>
<li>Career changers interested in the field of data science and AI.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Data Analyst</strong> - £30,000 to £55,000</li>
<li><strong>Machine Learning Engineer</strong> - £40,000 to £80,000</li>
<li><strong>AI Developer</strong> - £35,000 to £75,000</li>
<li><strong>Business Intelligence Analyst</strong> - £32,000 to £60,000</li>
<li><strong>Research Scientist (Machine Learning)</strong> - £45,000 to £85,000</li>
<li><strong>Software Engineer (AI Specialization)</strong> - £38,000 to £70,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Machine Learning Basics</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Machine Learning Basics</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Section 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Supervised Machine Learning</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Regression</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluating Regression Models</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditions for Using Regression Models in ML versus in Classical Statistics</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Statistically Significant Predictors</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Additive Effects</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Models Including Categorical Predictors. Interaction Effects</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Predictors</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multicollinearity among Predictors and its Consequences</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Prediction for New Observation. Confidence Interval and Prediction Interval</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Model Building. What if the Regression Equation Contains 'Wrong' Predictors?</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Minitab</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Stepwise Regression and its Use for Finding the Optimal Model in Minitab</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression with Minitab. Example. Auto-mpg: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: Regression Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Basic idea of Regression Trees</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part1</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees with Minitab. Example. Bike Sharing: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Binary Logistics Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Binary Logistics Regression</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 07: Classification Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Classification Trees</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 1. Splitting by Misclassification Rate</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Node Splitting Methods 2. Splitting by Gini Impurity or Entropy</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicted Class for a Node</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 1. Model Misclassification Cost</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predefined Prior Probabilities and Input Misclassification Costs</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Building the Tree</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Minitab. Example. Maintenance of Machines: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classification Trees with Miitab. Example. Maintenance of Machines: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: Data Cleaning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating New Features</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 09: Data Models</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polynomial Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Interactions Regression Models for Quantitative Predictor Variables</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Qualitative and Quantitative Predictors: Interaction Models</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Final Models for Duration and TotalCharge: Without Validation</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Underfitting or Overfitting: The 'Just Right Model'</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for Duration: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 10: Learning Success</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regression Trees for Duration and TotalCharge</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: The Problem Statement</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Binary Logistic Regression Models</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predicting Learning Success: Classification Tree Models</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Learning & Development

Machine Learning (ML)

Learning To Learn

Unsupervised Machine Learning

Azure Machine Learning

Machine Learning Basics

Online Course Marketing

Programming Fundamentals

Complete Python Machine Learning & Data Science Fundamentals

<p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;">Business Intelligence Analyst Course </span></strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Did you know that businesses harnessing the power of Business Intelligence (BI) in the UK alone are 23% more likely to outperform their competition? In today&rsquo;s data-driven world, the key to success lies in making informed decisions, and that&rsquo;s where our Business Intelligence Analyst Course comes in!</span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">This course will help you master reporting, analytics, data mining, benchmarking, and complex event processing, enabling you to make data-driven decisions that can transform your business. As a business owner or aspiring business analyst, you&rsquo;ll gain the ability to implement BI strategies that drive success for large companies in this Business Intelligence Analyst Course course. Plus, you will become a pro at choosing the exemplary RTBI architecture to achieve the best results in various sectors. As you move deeper into the Business Intelligence Analyst Course course, you will gain insights into business statistics, data science, and different analytics types to give you a well-rounded skill set.&nbsp;</span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">SQL expertise is a must-have for data management, and our course ensures you&rsquo;re not just familiar with it but a true SQL wizard, versed in different data types. Furthermore, this Business Intelligence Analyst Course will teach you the essentials of database management and help you grasp data warehouse concepts to effectively manage your organization&rsquo;s data. Before the end, the course will provide you with in-depth knowledge of data visualization in business intelligence and the basics of Python programming to enhance your ability to convey critical information and perform advanced data analysis.</span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Make a smart choice for your future. Eneol in our Business Intelligence Analyst Course to excel in the business field.</span></p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span data-sheets-root="1" data-sheets-value='{"1":2,"2":"Business Intelligence Analyst Course"}' data-sheets-userformat='{"2":4226,"4":{"1":2,"2":65280},"10":2,"15":"Arial"}' style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Business Intelligence Analyst Course Curriculum&nbsp;</strong></span><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-1: An Introduction to Business Intelligence</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-2: Real-time Business Intelligence</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-3: Fundamentals of Statistics and Data Science</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-4: Fundamentals of SQL</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-5: Database Management Essentials</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-6: Data Warehouse Concepts</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-7: Data Visualisation in Business Intelligence</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Module-8: Basics of Python</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Why Choose Teachers Training</strong></span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Some of our website features are:</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">This is a dedicated website for teaching</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">24/7 tutor support</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Interactive Content</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Affordable price</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Courses accredited by the UK&#39;s top awarding bodies</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">100% online</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Flexible deadline</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Entry Requirements</strong></span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">No formal entry requirements. You need to have:</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Passion for learning</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">A good understanding of the English language</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Be motivated and hard-working</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Over the age of 16.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Certification</strong></span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">CPD Certification from The Teachers Training</span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">You can get your digital certificate (PDF) for &pound;4.99 only</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">Hard copy certificates are also available, and you can get one for only &pound;10.99</li><li style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">You can get both PDF and Hard copy certificates for just &pound;12.99!</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.</span></p>

The Teachers Training

Unlock the power of data-driven decision-making with our Business Intelligence Analyst Course. Gain essential skills in data analysis, visualization, and interpretation to extract valuable insights for business growth. Whether you're a seasoned professional or new to the field, this course provides comprehensive training in business intelligence tools and techniques. Enroll now and become proficient in transforming raw data into actionable intelligence, empowering organizations to make informed strategic decisions.

Business Intelligence Analyst Course

<div fr-original-style="" style="box-sizing: border-box;">
<section fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<h2 fr-original-style="" style="box-sizing: border-box;">Business Intelligence Analyst Course Overview</h2></div>
</div>
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Did you know that businesses harnessing the power of Business Intelligence (BI) in the UK alone are 23% more likely to outperform their competition? In today&#39;s data-driven world, the key to success lies in making informed decisions, and that&#39;s where our Business Intelligence Analyst Course comes in!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This course will help you master reporting, analytics, data mining, benchmarking, and complex event processing, enabling you to make data-driven decisions that can transform your business. As a business owner or aspiring business analyst, you&#39;ll gain the ability to implement BI strategies that drive success for large companies in this Business Intelligence Analyst Course course. Plus, you will become a pro at choosing the exemplary RTBI architecture to achieve the best results in various sectors. As you move deeper into the Business Intelligence Analyst Course course, you will gain insights into business statistics, data science, and different analytics types to give you a well-rounded skill set.&nbsp;</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">SQL expertise is a must-have for data management, and our course ensures you&#39;re not just familiar with it but a true SQL wizard, versed in different data types. Furthermore, this Business Intelligence Analyst Course will teach you the essentials of database management and help you grasp data warehouse concepts to effectively manage your organization&#39;s data. Before the end, the course will provide you with in-depth knowledge of data visualization in business intelligence and the basics of Python programming to enhance your ability to convey critical information and perform advanced data analysis.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Make a smart choice for your future. Eneol in our Business Intelligence Analyst Course to excel in the business field. </p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;">Business Intelligence Analyst Course Curriculum&nbsp;<br fr-original-style="" style="box-sizing: border-box;"></span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Module-1: An Introduction to Business Intelligence</li><li fr-original-style="" style="box-sizing: border-box;">Module-2: Real-time Business Intelligence</li><li fr-original-style="" style="box-sizing: border-box;">Module-3: Fundamentals of Statistics and Data Science</li><li fr-original-style="" style="box-sizing: border-box;">Module-4: Fundamentals of SQL</li><li fr-original-style="" style="box-sizing: border-box;">Module-5: Database Management Essentials</li><li fr-original-style="" style="box-sizing: border-box;">Module-6: Data Warehouse Concepts</li><li fr-original-style="" style="box-sizing: border-box;">Module-7: Data Visualisation in Business Intelligence</li><li fr-original-style="" style="box-sizing: border-box;">Module-8: Basics of Python</li></ul></div><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span></div><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span></div><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span></div><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span></div><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span></section><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><section fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">
</strong></span><div fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 18px; box-sizing: border-box;" fr-original-style="font-size: 18px;">
</span></strong><h3 fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Why Choose Teachers Training</strong></h3></div>
</div>
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Some of our website features are:</p></div>
</div>
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<ul fr-original-style="" style="box-sizing: border-box;">
<li fr-original-style="" style="box-sizing: border-box;">


This is a dedicated website for teaching
</li>
<li fr-original-style="" style="box-sizing: border-box;">


24/7 tutor support
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Interactive Content
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Affordable price
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Courses accredited by the UK&#39;s top awarding bodies
</li>
<li fr-original-style="" style="box-sizing: border-box;">


100% online 
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Flexible deadline
</li>
</ul>
</div>
</div>
</div>
</div>
</div>
</section>
<section fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
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<div fr-original-style="" style="box-sizing: border-box;">
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<h3 fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Entry Requirements
</strong></h3></div>
</div>
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">No formal entry requirements. You need to have:</p></div>
</div>
<div fr-original-style="" style="box-sizing: border-box;">
<div fr-original-style="" style="box-sizing: border-box;">
<ul fr-original-style="" style="box-sizing: border-box;">
<li fr-original-style="" style="box-sizing: border-box;">


Passion for learning
</li>
<li fr-original-style="" style="box-sizing: border-box;">


A good understanding of the English language
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Be motivated and hard-working
</li>
<li fr-original-style="" style="box-sizing: border-box;">


Over the age of 16.
</li>
</ul>
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<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Certification from The Teachers Training</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">You can get your digital certificate (PDF) for &pound;4.99 only</li><li fr-original-style="" style="box-sizing: border-box;">Hard copy certificates are also available, and you can get one for only &pound;10.99</li><li fr-original-style="" style="box-sizing: border-box;">You can get both PDF and Hard copy certificates for just &pound;12.99!</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.</p></div>
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Business Development

Unlock the potential of business intelligence with our specialized Business Intelligence Analyst Course. Learn to analyze data, extract insights, and drive strategic decisions to optimize business performance. Gain practical skills in data visualization, reporting, and predictive analytics using industry-leading tools and techniques. Whether you're a business professional or aspiring analyst, this course equips you with the expertise to excel in leveraging data for business intelligence.

<p>Machine learning is a vital aspect of data science, and it is the fundamental building block of artificial intelligence. This course is designed to help you master the basics of machine learning by taking you through ten comprehensive modules.</p> <p><strong>Learning outcomes:</strong></p> <ul> <li>Understand the basic principles of machine learning and its significance in today's world.</li><li>Learn how to use Minitab for data analysis and data cleaning.</li><li>Understand how regression trees and classification trees work and how to apply them.</li><li>Understand binary logistic regression and its applications.</li><li>Understand data modelling and how to use different predictors.</li><li>Learn how to evaluate and improve the performance of machine learning models.</li></ul><p>The Machine Learning Basics course is designed to help individuals develop a fundamental understanding of machine learning. In this course, you will learn about the basics of machine learning, including regression, predictors, data cleaning, and data models. Additionally, you will learn how to use Minitab for data analysis and how to apply binary logistic regression, regression trees, and classification trees. The course includes ten comprehensive modules that will help you develop the skills you need to become a machine learning expert.</p> <p>This course is for anyone who wants to learn the basics of machine learning, including students, data analysts, and business professionals. By the end of the course, you will have a deep understanding of machine learning principles, including how to apply machine learning algorithms to solve real-world problems.</p> <p><strong>Certification</strong></p> <p>Upon completion of the course, learners can obtain a certificate as proof of their achievement. You can receive a £4.99 PDF Certificate sent via email, a £9.99 Printed Hardcopy Certificate for delivery in the UK, or a £19.99 Printed Hardcopy Certificate for international delivery. Each option depends on individual preferences and locations.</p><h2>
        
        <div>CPD</div>
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                                        10 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                             <ul> <li>Students who want to learn about machine learning.</li><li>Data analysts who want to enhance their skills.</li><li>Business professionals who want to understand machine learning.</li><li>Anyone who wants to develop a fundamental understanding of machine learning.</li></ul> 
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        <div>Career path</div>
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                             <p><strong>Career paths related to this industry are:</strong></p> <ul> <li>Data analyst: £20,000 - £50,000 per year</li><li>Machine learning engineer: £30,000 - £90,000 per year</li><li>Data scientist: £35,000 - £80,000 per year</li><li>Business intelligence analyst: £25,000 - £55,000 per year</li><li>Artificial intelligence (AI) specialist: £45,000 - £100,000 per year</li><li>Software engineer: £25,000 - £70,000 per year</li></ul> 
                        </div>

NextGen Learning

Managed Learning Programme (MLP)

Explore the World of Machine Learning with Basics Course

<p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.&nbsp;</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You&#39;ll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Enrol now to gain instant access to the course materials and start your learning journey. Don&#39;t miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.</span></p><h2 dir="ltr" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Learning Outcome of Python for Data Science and Machine Learning Bootcamp &nbsp; &nbsp;</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Master fundamental concepts of machine learning and its applications.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Gain proficiency in Python programming for data analysis and manipulation.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to load, read, and manipulate CSV data files using Python libraries.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Understand dataset summary statistics and visualization techniques.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Acquire skills in data preparation and preprocessing.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Explore feature selection techniques and their importance.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Evaluate machine learning algorithms using various techniques and metrics.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Spot check classification and regression algorithms for model selection.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Improve model performance using ensemble methods and parameter tuning.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to export, save, load, and finalize machine learning models for real-time predictions.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Key Features of the Course</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A CPD certificate that is recognised worldwide.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A great online learning experience.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Interesting and unique online materials and activities.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Expert guidance and support from the field leaders.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Access to the study resources anytime you want.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Get a year-long access to the course.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Who is this course for</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); font-size: 16px; box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0); font-size: 16px;">This Python for Data Science and Machine Learning Bootcamp Course is suitable for -</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Aspiring data scientists and machine learning enthusiasts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Professionals seeking to enhance their data analysis skills</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Individuals interested in Python programming for data science</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Anyone looking to break into the field of data science and machine learning</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Students pursuing studies in computer science or related fields</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Requirements</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Basic understanding of programming concepts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Familiarity with Python programming language is beneficial but not mandatory</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">No prerequisites; suitable for individuals from any academic background.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Accessible course materials from any internet-enabled device.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">CPD Certificate from Course Gate</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for &pound;9 and the hard copy for &pound;15. Also, you can order both PDF and hardcopy certificates for &pound;22.</span></p><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Career path</span><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></span></h2><div style="color: rgb(68, 68, 68); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(68, 68, 68);"><div style="color: rgb(68, 68, 68); box-sizing: border-box;" fr-original-style=" color: rgb(68, 68, 68);"><div data-elementor-type="wp-post" data-elementor-id="357815" data-elementor-post-type="course" fr-original-style="" style="box-sizing: border-box;"><section data-id="5767f71a" data-element_type="section" fr-original-style="" style="box-sizing: border-box;"><div data-id="58784751" data-element_type="column" fr-original-style="" style="box-sizing: border-box;"><div data-id="ce105db" data-element_type="widget" data-widget_type="text-editor.default" style="color: var( --e-global-color-text ); box-sizing: border-box;" fr-original-style=" color: var( --e-global-color-text );"><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Machine Learning Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Business Intelligence Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Research Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Quantitative Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Artificial Intelligence Specialist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Consultant</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Statistician</li></ul></div></div></section></div></div></div>

Course Gate

Data Science

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills. 

Python for Data Science and Machine Learning Bootcamp

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

LearnDrive UK

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning</p>
        
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

<p>Feeling Stuck in Your Career? The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the  Python for Data Science and Machine Learning bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The  Python for Data Science and Machine Learning bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle on your job life and career growth!</p><p><strong>This Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Machine Learning Basics</li><li><strong>Course 02</strong>: Azure Machine Learning</li><li><strong>Course 03</strong>: Python Intermediate Training</li><li><strong>Course 04</strong>: Python Data Science</li><li><strong>Course 05</strong>: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li><strong>Course 06</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07</strong>: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li><strong>Course 08</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 09</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 10</strong>: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 11</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 12</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 13</strong>: Building Big Data Pipelines with PySpark MongoDB and Bokeh</li><li><strong>Course 14</strong>: Clinical Data Management with SAS Programming</li><li><strong>Course 15</strong>: Certificate in Data Entry and Management</li><li><strong>Course 16</strong>: Quick Data Science Approach from Scratch</li><li><strong>Course 17</strong>: SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 18</strong>: Programming AutoCAD with SQL Server Database Using C#</li><li><strong>Course 19</strong>: Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 20</strong>: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>Course 21</strong>: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li><strong>Course 22</strong>: Data Center Training Essentials: Power &amp; Electrical</li><li><strong>Course 23</strong>: Business Intelligence and Data Mining</li><li><strong>Course 24</strong>: Data Analysis In Excel</li><li><strong>Course 25</strong>: CV Writing and Job Searching</li><li><strong>Course 26</strong>: Learn to Level Up Your Leadership</li><li><strong>Course 27</strong>: Networking Skills for Personal Success</li><li><strong>Course 28</strong>: Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 29</strong>: Learn to Make a Fresh Start in Your Life</li><li><strong>Course 30</strong>: Motivation - Motivating Yourself &amp; Others</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this  Python for Data Science and Machine Learning  Bundle </li><li>After successfully completing the  Python for Data Science and Machine Learning bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this  Python for Data Science and Machine Learning       Bundle</li><li>The online test with immediate results </li><li>You can study and complete the  Python for Data Science and Machine Learning bundle at your own pace.</li><li>Study for the  Python for Data Science and Machine Learning bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The  Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the   Python for Data Science and Machine Learning bundle, a comprehensive, complete collection of 30 courses. Each course in the  Python for Data Science and Machine Learning bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the   Python for Data Science and Machine Learning bundle offers the flexibility and convenience to learn at your own pace. Make the   Python for Data Science and Machine Learning package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the  Python for Data Science and Machine Learning bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the  Python for Data Science and Machine Learning bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the  Python for Data Science and Machine Learning bundle.</li><li>Learn on your schedule, in the comfort of your home - the  Python for Data Science and Machine Learning bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the  Python for Data Science and Machine Learning bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses!

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<strong>In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.</strong>
        <p>The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>What You Will Learn</h2><p>Learn data cleaning, processing, wrangling, and manipulation<br>Learn plotting in R (graphs, charts, plots, histograms, and more)<br>How to create a resume and land your first job as a data scientist<br>Learn machine learning and its various practical applications<br>Learn data and file management in R<br>Use R to clean, analyze, and visualize data</p>
        <h2>Audience</h2><p>This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.</p>
        <h2>Approach</h2><p>This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.</p>
        <h2>Key Features</h2><p>Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>About the Author</h2><h3>Juan E. Galvan</h3><p>Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.</p>
        <h2>Course Outline</h2><h4>1. Data Science and Machine Leaning Course Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Science and Machine Learning Introduction Section Overview</p><p style='margin: 0'>This video explains data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Data Science?</p><p style='margin: 0'>This video explains the concept of data science.</p></td></tr><tr><td style='padding: 15px;'><p>3. Machine Learning Overview</p><p style='margin: 0'>This video gives an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Science + Machine Learning Marketplace</p><p style='margin: 0'>This video explains the various trends and job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Who is this Course For?</p><p style='margin: 0'>This video explains the audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Science and Machine Learning Job Opportunities</p><p style='margin: 0'>This video explains the various job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Science Job Roles</p><p style='margin: 0'>This video explains the various job roles for data science.</p></td></tr></tbody></table><br><h4>2. Getting Started with R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Started with R</p><p style='margin: 0'>This video explains the concept of R.</p></td></tr><tr><td style='padding: 15px;'><p>2. R Basics</p><p style='margin: 0'>This video explains the R basics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Files</p><p style='margin: 0'>This video explains working with files in R.</p></td></tr><tr><td style='padding: 15px;'><p>4. R Studio</p><p style='margin: 0'>This video explains R studio.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tidyverse Overview</p><p style='margin: 0'>This video provides an overview of tidyverse.</p></td></tr><tr><td style='padding: 15px;'><p>6. Additional Resources</p><p style='margin: 0'>This video shares some additional resources that will help you get along with the course.</p></td></tr></tbody></table><br><h4>3. Data Types and Structures in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Types and Structures in R Section Overview</p><p style='margin: 0'>This video explains data types and structures in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Types</p><p style='margin: 0'>This video explains basic types in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Vectors - Part One</p><p style='margin: 0'>This video introduces you to vectors.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vectors - Part Two</p><p style='margin: 0'>This video explains how to interact with R using vectors.</p></td></tr><tr><td style='padding: 15px;'><p>5. Vectors: Missing Values</p><p style='margin: 0'>This video explains vector missing values.</p></td></tr><tr><td style='padding: 15px;'><p>6. Vectors: Coercion</p><p style='margin: 0'>This video explains coercion.</p></td></tr><tr><td style='padding: 15px;'><p>7. Vectors: Naming</p><p style='margin: 0'>This video explains vector naming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Vectors: Miscellaneous</p><p style='margin: 0'>This video explains miscellaneous yet important concepts in vectors.</p></td></tr><tr><td style='padding: 15px;'><p>9. Working with Matrices</p><p style='margin: 0'>This video explains working with matrices.</p></td></tr><tr><td style='padding: 15px;'><p>10. Working with Lists</p><p style='margin: 0'>This video explains working with lists.</p></td></tr><tr><td style='padding: 15px;'><p>11. Introduction to Data Frames</p><p style='margin: 0'>This video explains introduction to data frames.</p></td></tr><tr><td style='padding: 15px;'><p>12. Creating Data Frames</p><p style='margin: 0'>This video explains creating data frames.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Frames: Helper Functions</p><p style='margin: 0'>This video explains data frames helper functions.</p></td></tr><tr><td style='padding: 15px;'><p>14. Data Frames: Tibbles</p><p style='margin: 0'>This video explains Tibbles in data frames.</p></td></tr></tbody></table><br><h4>4. Intermediate R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intermedia R Section Introduction</p><p style='margin: 0'>This video introduces you to R section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Relational Operators</p><p style='margin: 0'>This video explains relational operators.</p></td></tr><tr><td style='padding: 15px;'><p>3. Logical Operators</p><p style='margin: 0'>This video explains logical operators,</p></td></tr><tr><td style='padding: 15px;'><p>4. Conditional Statements</p><p style='margin: 0'>This video explains conditional statements.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Loops</p><p style='margin: 0'>This video explains working with loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Working with Functions</p><p style='margin: 0'>This video explains working with functions in R.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Packages</p><p style='margin: 0'>This video explains working with packages.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working with Factors</p><p style='margin: 0'>This video explains working with factors in R.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dates and Times</p><p style='margin: 0'>This video explains dates and times in R.</p></td></tr><tr><td style='padding: 15px;'><p>10. Functional Programming</p><p style='margin: 0'>This video explains functional programming.</p></td></tr><tr><td style='padding: 15px;'><p>11. Data Import/Export</p><p style='margin: 0'>This video explains how to import and export data in R.</p></td></tr><tr><td style='padding: 15px;'><p>12. Working with Databases</p><p style='margin: 0'>This video explains working with databases.</p></td></tr></tbody></table><br><h4>5. Data Manipulation in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Manipulation Section Introduction</p><p style='margin: 0'>This video introduces you to data manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidy Data</p><p style='margin: 0'>This video explains tidy data.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Pipe Operator</p><p style='margin: 0'>This video explains pipe operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. {dplyr}: The Filter Verb</p><p style='margin: 0'>This video explains the filter verb.</p></td></tr><tr><td style='padding: 15px;'><p>5. {dplyr}: The Select Verb</p><p style='margin: 0'>This video explains the select verb</p></td></tr><tr><td style='padding: 15px;'><p>6. {dplyr}: The Mutate Verb</p><p style='margin: 0'>This video explains the mutate verb.</p></td></tr><tr><td style='padding: 15px;'><p>7. {dplyr}: The Arrange Verb</p><p style='margin: 0'>This video explains the purpose of arrange verb.</p></td></tr><tr><td style='padding: 15px;'><p>8. {dplyr}: The Summarize Verb</p><p style='margin: 0'>This video explains the summarize verb.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Pivoting: {tidyr}</p><p style='margin: 0'>This video explains data pivoting.</p></td></tr><tr><td style='padding: 15px;'><p>10. String Manipulation: {stringr}</p><p style='margin: 0'>This video explains string manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Web Scraping: {rvest}</p><p style='margin: 0'>This video explains web scraping.</p></td></tr><tr><td style='padding: 15px;'><p>12. JSON Parsing: {jsonlite}</p><p style='margin: 0'>This video explains JSON parsing.</p></td></tr></tbody></table><br><h4>6. Data Visualization in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Visualization in R Section Introduction</p><p style='margin: 0'>This video explains data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Started with Data Visualization in R</p><p style='margin: 0'>This video explains getting started with data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Aesthetics Mappings</p><p style='margin: 0'>This vide explains aesthetics mappings.</p></td></tr><tr><td style='padding: 15px;'><p>4. Single Variable Plots</p><p style='margin: 0'>This video focuses on single variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>5. Two Variable Plots</p><p style='margin: 0'>This video explains two variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>6. Facets, Layering, and Coordinate Systems</p><p style='margin: 0'>This video explains facets, layering, and coordinate systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Styling and Saving</p><p style='margin: 0'>This video explains styling and saving.</p></td></tr></tbody></table><br><h4>7. Creating Reports with R Markdown</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Markdown</p><p style='margin: 0'>This video is an introduction to R.</p></td></tr></tbody></table><br><h4>8. Building Webapps with R Shiny</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Shiny</p><p style='margin: 0'>This video is an introduction to R Shiny</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Basic R Shiny App</p><p style='margin: 0'>This video helps you to create a basic R shiny app.</p></td></tr><tr><td style='padding: 15px;'><p>3. Other Examples with R Shiny</p><p style='margin: 0'>This video gives various examples with R shiny.</p></td></tr></tbody></table><br><h4>9. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Machine Learning Part One</p><p style='margin: 0'>This video is an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning Part Two</p><p style='margin: 0'>This video explains the different approaches in machine learning.</p></td></tr></tbody></table><br><h4>10. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Preprocessing Introduction</p><p style='margin: 0'>This video introduces you to data preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preprocessing</p><p style='margin: 0'>This video explains some of the practical ways of data preprocessing.</p></td></tr></tbody></table><br><h4>11. Linear Regression: A Simple Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression: A Simple Model Introduction</p><p style='margin: 0'>This video explains a simple linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple Model</p><p style='margin: 0'>This video demonstrates a practical application of machine learning.</p></td></tr></tbody></table><br><h4>12. Exploratory Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis Introduction</p><p style='margin: 0'>This video gives an introduction to exploratory data analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hands-on Exploratory Data Analysis</p><p style='margin: 0'>This video explains hands-on exploratory data analysis.</p></td></tr></tbody></table><br><h4>13. Linear Regression - a Real Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression - Real Model Section Introduction</p><p style='margin: 0'>This video explains linear regression - a real model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Regression in R - Real Model</p><p style='margin: 0'>This video explains linear regression in R.</p></td></tr></tbody></table><br><h4>14. Logistic Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Logistic Regression</p><p style='margin: 0'>This video introduces you to logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression in R</p><p style='margin: 0'>This video explains logistic regression in R.</p></td></tr></tbody></table><br><h4>15. Starting a Career in Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Starting a Data Science Career Section Overview</p><p style='margin: 0'>This video explains section overview of data science and career.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Data Science Resume</p><p style='margin: 0'>This video explains creating a data science resume.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with Freelancing</p><p style='margin: 0'>This vide explains how to get started with freelancing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Top Freelance Websites</p><p style='margin: 0'>This video focuses on top freelancing websites.</p></td></tr><tr><td style='padding: 15px;'><p>5. Personal Branding</p><p style='margin: 0'>This video explains personal branding.</p></td></tr><tr><td style='padding: 15px;'><p>6. Networking Do's and Don'ts</p><p style='margin: 0'>This video explains networking do's and don'ts.</p></td></tr><tr><td style='padding: 15px;'><p>7. Setting Up a Website</p><p style='margin: 0'>This video explains how to set up a website.</p></td></tr></tbody></table>

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

Janets

Data Science and Machine Learning using Python : A Bootcamp

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

R Ultimate 2023 - R for Data Science and Machine Learning

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					<div><p>Python has steadily ascended the ranks in the tech world, becoming the go-to language for Data Science and Machine Learning pursuits. Its versatility and ease of use make it an optimal choice for those aspiring to delve into the intricate realms of data analysis and algorithm development. By integrating Python into the curriculum, this course ensures that learners are not only familiar with the language but also proficient in its application to Data Science and Machine Learning tasks.</p><p>Data Science, on the other hand, stands as the backbone of today's analytical advancements. With the ever-growing quantum of data, there's a dire need for professionals who can sift through this data deluge and extract meaningful insights. This course simplifies complex topics, making it easier for learners to grasp the intricate mechanics of data manipulation, processing, and analysis. By emphasizing Python's role in Data Science, the course underscores its significance in modern-day data operations.</p><p>Lastly, Machine Learning represents the pinnacle of automated data analysis. With the power of Python and the methodologies of Data Science, Machine Learning enables systems to learn from data and make decisions. This course elucidates these concepts, teaching learners to design, implement, and test Machine Learning algorithms. The synergy of Python, Data Science, and Machine Learning in this curriculum ensures that, by the end, learners are well-equipped to tackle any data challenge that comes their way.</p></div>
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										<span>Develop real-time prediction models using trained datasets.</span>
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			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Stay ahead in the rapidly growing field of Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Enhance career opportunities in the tech and data sectors.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn to use Python, a leading programming language in Data Science.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Address real-world business challenges using data analysis.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Achieve proficiency in handling and visualizing large datasets.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Career Path</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Data Scientist</span>
									</li>
								<li>
											<span>
													</span>
										<span>Machine Learning Engineer</span>
									</li>
								<li>
											<span>
													</span>
										<span>Python Data Analyst</span>
									</li>
								<li>
											<span>
													</span>
										<span>Predictive Modeler</span>
									</li>
								<li>
											<span>
													</span>
										<span>Data Visualization Expert</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Basic understanding of programming concepts.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Eagerness to learn Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>A computer with internet access.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>

Master Data Science and Machine Learning with Python to transform data-driven decisions in businesses.

Data Science & Machine Learning with Python

<strong>In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!</strong>
        <p>The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.

We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
The code files are available here: https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>What You Will Learn</h2><p>Learn coding in Julia programming language<br>Use DataFrames (equivalent to Pandas) in Julia<br>Create ML models from scratch in a way that helps you make modifications easily<br>Learn data wrangling with Julia<br>Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis<br>Classify using decision trees and random forests</p>
        <h2>Audience</h2><p>This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.<br><br>A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.</p>
        <h2>Approach</h2><p>This course follows a code-oriented and case-study-based approach. In this course, you will learn by practice as every concept explanation is followed by practical implementation.</p>
        <h2>Key Features</h2><p>Learn the syntax of Julia and its differences from Python * Learn machine learning models, both traditional and deep * Explore data science case studies, including analysis and clustering</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Intro and Setting up</h4><p></p><br><h4>2. Core Language Basics</h4><p></p><br><h4>3. Arrays and Matrices: Native Language Support</h4><p></p><br><h4>4. Functions and Fun Stuff</h4><p></p><br><h4>5. Getting Started with Data Science</h4><p></p><br><h4>6. Case Studies in Data Science</h4><p></p><br><h4>7. Deep Learning - Flux in Julia</h4><p></p><br><h4>8. Parting Words</h4><p></p>

In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

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