Name: Data Analyst Training Course
Price: 69 GBP
Availability: InStock

<p>Do you want to prepare for your dream job but strive hard to find the right courses? Then, stop worrying, for our strategically modified<strong> Data Analysis with R</strong> bundle will keep you up to date with the relevant knowledge and most recent matters of this emerging field. So, invest your money and effort in our <strong>33</strong> course mega bundle that will exceed your expectations within your budget.</p> <p>The<strong> Data Analysis with R</strong> related fields are thriving across the UK, and recruiters are hiring the most knowledgeable and proficient candidates. It's a demanding field with magnitudes of lucrative choices. If you need more guidance to specialise in this area and need help knowing where to start, then <strong>StudyHub </strong> proposes a preparatory bundle. This comprehensive <strong>Data Analysis with R</strong> bundle will help you build a solid foundation to become a proficient worker in the sector.</p> <p><strong>This Data Analysis with R Bundle consists of the following</strong><strong> 30 </strong><strong>CPD Accredited Premium courses - </strong></p> <ul> <li>Course 01 :Business Intelligence and Data Mining</li><li>Course 02 :Diploma in Data Analysis Fundamentals</li><li>Course 03 :Google Data Studio: Data Analytics</li><li>Course 04 :Statistics</li><li>Course 05 :Statistical Analysis</li><li>Course 06 :Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 07 :Quick Data Science Approach from Scratch</li><li>Course 08 :SAS Programming Basic to Advanced</li><li>Course 09 :R Programming for Data Science</li><li>Course 10 :Python Data Science</li><li>Course 11 :Data Science &amp; Machine Learning with Python</li><li>Course 12 :Microsoft Power BI - Master Power BI in 90 Minutes!</li><li>Course 13 :PowerBI Formulas</li><li>Course 14 :Excel Data Analysis</li><li>Course 15 :Excel Data Tools and Data Management</li><li>Course 16 :Master JavaScript with Data Visualization</li><li>Course 17 :Research Methods in Business</li><li>Course 18 :Fundamentals of Business Analysis</li><li>Course 19 :Financial Analysis</li><li>Course 20 :Financial Modeling Using Excel</li><li>Course 21 :Investment Analyst</li><li>Course 22 :Technical Analysis Masterclass for Trading &amp; Investing</li><li>Course 23 :Understanding Financial Statements and Analysis</li><li>Course 24 :Strategic Planning and Analysis for Marketing</li><li>Course 25: Stock Trading Analysis with Volume Trading</li><li>Course 26: Time Management Training - Online Course</li><li>Course 27: Complete Communication Skills Master Class for Life</li><li>Course 28: Public Speaking</li><li>Course 29: Minute Taking Executive Training</li><li>Course 30: Receptionist Skills</li></ul> <p><strong>3 </strong><strong>Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques-</strong></p> <ul> <li>Course 01 :Career Development Plan Fundamentals</li><li>Course 02 :CV Writing and Job Searching</li><li>Course 03 :Interview Skills: Ace the Interview</li></ul> <p><strong>Learning Outcome</strong></p> <p><strong>This tailor-made </strong><strong>Data Analysis with R</strong><strong> bundle will allow you to-</strong></p> <ul> <li>Uncover your skills and aptitudes to break new ground in the related fields</li><li>Deep dive into the fundamental knowledge </li><li>Acquire some hard and soft skills in this area </li><li>Gain some transferable skills to elevate your performance</li><li>Maintain good report with your clients and staff</li><li>Gain necessary office skills and be tech savvy utilising relevant software </li><li>Keep records of your work and make a report </li><li>Know the regulations around this area</li><li>Reinforce your career with specific knowledge of this field </li><li>Know your legal and ethical responsibility as a professional in the related field</li></ul> <p>This Data Analysis with R Bundle resources were created with the help of industry experts, and all subject-related information is kept updated on a regular basis to avoid learners from falling behind on the latest developments.</p> <p><strong>Certification</strong></p> <p>After studying the complete training you will be able to take the assessment. After successfully passing the assessment you will be able to claim all courses pdf certificates and <strong>1 hardcopy certificate for the Title Course completely free</strong>. Other Hard Copy certificates need to be ordered at an additional cost of •8.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        330 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>Ambitious learners who want to strengthen their CV for their desired job should take advantage of the Data Analysis with R bundle! This bundle is also ideal for professionals looking for career advancement.</p> 
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p>To participate in this course, all you need is -</p> <ul> <li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Data Analysis with R</li></ul> 
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p>Upon completing this essential Bundle, you will discover a new world of endless possibilities. These courses will help you to get a cut above the rest and allow you to be more efficient in the relevant fields.</p> 
                        </div>

Studyhub UK

Business Analytics & Intelligence

Data Science

Professional Skills Course (PSC)

Data Skills

Trainers Course

Data Analyst

Data Analysis with R

General Admission

<p>The world of technology is constantly evolving, and to stay ahead of the curve, you need to be constantly learning. That's why we're proud to present the Python, C++ Development &amp; MySql Masterclass bundle. This comprehensive collection of courses will help you develop the skills you need to excel in the tech industry.</p><p>With the rise of the gig economy, there's never been a better time to invest in yourself and your future. Whether you're looking to start a new career or simply want to upskill, this bundle is the perfect place to start. It's packed with cutting-edge knowledge and skills that will help you succeed in today's fast-paced world.</p><p>According to a recent study, the technology sector in the UK is expected to grow by 6.5% in the next five years, creating hundreds of thousands of new job opportunities. With the Python, C++ Development &amp; MySql Masterclass bundle, you'll be positioning yourself to take advantage of this growth. You'll gain valuable skills that will help you succeed in a competitive job market and stand out from the crowd.</p><p>The bundle includes 3 QLS-endorsed courses, which are Python Programming for Non-Programmers Level 5, MySql Masterclass, and C++ Development: The Complete Coding Guide. Upon completion, you'll receive 3 hardcopy certificates, recognising your achievements and demonstrating your commitment to learning.</p><p>In addition to the 3 QLS courses, there are also 5 CPD QS accredited relevant courses and 5 more CPD QS accredited career development courses to choose from. Whether you're looking to develop your coding skills or build a career in data science, this bundle has everything you need to get started. So why wait? Enrol today and start learning!</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Python Programming at QLS Level 5</li><li><strong>Course 02:</strong> Advanced Diploma in MySql Masterclass at QLS Level 7</li><li><strong>Course 03:</strong> Diploma in C++ Development: The Complete Coding Guide at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Level 2 Python Course</li><li><strong>Course 05:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 06:</strong> Learn MySQL from Scratch for Data Science and Analytics</li><li><strong>Course 07:</strong> Learn to Code HTML, CSS, Javascript: Guide for Beginners</li><li><strong>Course 08:</strong> C++ Programming Certificate Course</li></ul><p>Take your career to the next level with our bundle that includes not only technical courses but also <strong>five guided courses</strong> focused on personal development and <strong> career growth.</strong></p><ul><li><strong>Course 09: </strong>Leadership and Management Course</li><li><strong>Course 10:</strong> Career Development and Passion</li><li><strong>Course 11: </strong>Professional CV Writing</li><li><strong>Course 12: </strong>Boost Your Confidence and Self-Esteem</li><li><strong>Course 13: </strong>Public Speaking: Confident Delivery Skills</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a deep understanding of Python programming</li><li>Master the use of MySql for data science and analytics</li><li>Become proficient in C++ development and coding</li><li>Learn to code HTML, CSS, and JavaScript</li><li>Understand the fundamentals of Python machine learning and data science</li><li>Gain valuable skills in career development</li><li>Receive three hardcopy certificates upon completion</li></ul><p><strong>The Python Programming for Non-Programmers Level 5</strong> course will introduce you to the basics of Python programming, allowing you to understand the logic behind coding and write your own programs.</p><p><strong>The MySql Masterclass</strong> will teach you how to use MySql for data science and analytics, allowing you to work with large amounts of data and make informed decisions.</p><p><strong>The C++ Development: </strong>The Complete Coding Guide will provide you with a comprehensive understanding of C++ development, including object-oriented programming and coding best practices.</p><p><strong>Certification</strong></p><p>Learners will qualify for the <strong>highly sought-after QLS Endorsed Certificate</strong> after successfully passing the assessment at the conclusion of each endorsed course.</p><p><strong>The Quality Licence Scheme</strong> is a credible and renowned endorsement that marks <strong>high quality</strong><strong>and excellence</strong> in the UK. It is well accepted and sought after by industry experts and recruiters.</p><p>Learners who purchase this bundle will receive <strong>Three Hardcopy QLS Certificates at their door!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 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">
                            <ul><li>Aspiring programmers and coders</li><li>Individuals looking to upskill in the latest programming languages</li><li>Students looking to enter the tech industry</li><li>Non-programmers looking to learn Python programming</li><li>Data scientists and analysts looking to expand their skills</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Software Developer: £49,000-£80,000 per year</li><li>Data Scientist: £45,000-£80,000 per year</li><li>Web Developer: £30,000-£60,000 per year</li><li>Database Administrator: £40,000-£70,000 per year</li><li>IT Manager: £50,000-£100,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

One Education

Python, C++ Development & MySql Masterclass Course

<p><strong>Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now</strong></p><p>With a single payment you will gain access to <strong>Data Science Course Bundle 2022</strong> including <strong>10</strong> Career development courses, <strong>original hardcopy certificate, transcript</strong> and a <strong>student ID card</strong> which will allow you to get discounts on things like music, food, travel and clothes etc.</p><p>The world is one big data bank, and data science is one of the most demanding professional sectors of the present era.</p><p>The analytical and programming-oriented field of data science has limited resources for candidates to learn and develop skills, which is why you need our highly advanced [course_title] course.With step-by-step interactive video content, our training will equip you with extensive knowledge and expertise in data science, including machine learning.</p><p>This bundle course offers an opportunity to foster your career opportunities with an expert-level understanding of data science and become skilful in this industry.</p><p>Take this course anywhere and at any time. Don't let your lifestyle limit your learning or your potential.</p><p><strong>Data Science Course Bundle 2022</strong> will provide you with the <strong> CPD certificate </strong>that you'll need to succeed.</p><p>Gain experience online and interact with experts. This can prove to be the perfect way to get noticed by a prospective employer and stand out from the crowd.</p><p><strong>Data Science Course Bundle 2022 </strong>has been rated and reviewed highly by our learners and professionals alike. We have a passion for teaching, and it shows.</p><p>All of our courses have interactive online modules that allow studying to take place where and when you want it to.</p><p>The only thing you need to take <strong>Data Science Course Bundle 2022 </strong> is Wi-Fi and a screen. You'll never be late for class again.</p><p>Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes.</p><p><strong>Benefits you'll get choosing Apex Learning for this Course:</strong></p><ul><li>One payment, but lifetime access to 11 CPD courses</li><li>Certificates, student ID for the title course included in a one-time fee</li><li>Full tutor support available from Monday to Friday</li><li>Free up your time - don't waste time and money travelling for classes</li><li>Accessible, informative modules taught by expert instructors</li><li>Learn at your ease - anytime, from anywhere</li><li>Study the course from your computer, tablet or mobile device</li><li>CPD accredited course - improve the chance of gaining professional skills</li><li>Gain valuable knowledge without leaving your home</li></ul><p><strong>What other courses are included with this Course?</strong></p><ol><li>Level 2 Microsoft Office Essentials</li><li>Microsoft Teams</li><li>Leadership &amp; Management Diploma</li><li>Working from Home Essentials</li><li>Mental Health and Working from Home</li><li>Online Meeting Management</li><li>Effective Communication Skills</li><li>Time Management</li><li>Report Writing</li><li>Emotional Intelligence and Human Behaviour</li></ol><p><strong>Curriculum</strong></p><p><strong>***Data Science Course Bundle 2022***</strong></p><p><strong>Welcome, Course Introduction &amp; overview, and Environment set-up</strong></p><ul><li>Welcome &amp; Course Overview</li><li>Set-up the Environment for the Course (lecture 1)</li><li>Set-up the Environment for the Course (lecture 2)</li><li>Two other options to setup environment</li></ul><p><strong>Python Essentials</strong></p><ul><li>Python data types Part 1</li><li>Python Data Types Part 2</li><li>Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)</li><li>Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)</li><li>Python Essentials Exercises Overview</li><li>Python Essentials Exercises Solutions</li></ul><p><strong>Python for Data Analysis using NumPy</strong></p><ul><li>What is Numpy? A brief introduction and installation instructions.</li><li>NumPy arrays, built-in methods, array methods and attributes.</li><li>Indexing, slicing, broadcasting &amp; boolean masking</li><li>Arithmetic Operations &amp; Universal Functions</li><li>Exercises Overview</li><li>Exercises Solutions</li></ul><p><strong>Python for Data Analysis using Pandas</strong></p><ul><li>What is pandas? A brief introduction and installation instructions.</li><li>Pandas Introduction</li><li>Pandas Data Structures - Series</li><li>Pandas Data Structures - DataFrame</li><li>Handling Missing Data</li><li>Data Wrangling - Combining, merging, joining</li><li>Groupby</li><li>Useful Methods and Operations</li><li>Project 1 (Overview) Customer Purchases Data</li><li>Project 1 (Solutions) Customer Purchases Data</li><li>Project 2 (Overview) Chicago Payroll Data</li><li>Project 2 (Solutions Part 1) Chicago Payroll Data</li></ul><p><strong>Python for Data Visualization using matplotlib</strong></p><ul><li>Matplotlib Essentials (Part 1) - Basic Plotting &amp; Object Oriented Approach</li><li>Matplotlib Essentials (Part 2) - Basic Plotting &amp; Object Oriented Approach</li><li>Matplotlib Essentials (Part 3) - Basic Plotting &amp; Object Oriented Approach</li><li>Matplotlib Essentials - Exercises Overview</li><li>Matplotlib Essentials - Exercises Solutions</li></ul><p><strong>Python for Data Visualization using Seaborn</strong></p><ul><li>Introduction &amp; Installation</li><li>Distribution Plots</li><li>Categorical Plots (Part 1)</li><li>Categorical Plots (Part 2)</li><li>Axis Grids</li><li>Matrix Plots</li><li>Regression Plots</li><li>Controlling Figure Aesthetics</li><li>Exercises Overview</li><li>Exercise Solutions</li></ul><p><strong>Python for Data Visualization using pandas</strong></p><ul><li>Pandas Built-in Data Visualization</li><li>Pandas Data Visualization Exercises Overview</li><li>Panda Data Visualization Exercises Solutions</li></ul><p><strong>Python for interactive &amp; geographical plotting using Plotly and Cufflinks</strong></p><ul><li>Interactive &amp; Geographical Plotting (Part 1)</li><li>Interactive &amp; Geographical Plotting (Part 2)</li><li>Interactive &amp; Geographical Plotting Exercises (Overview)</li><li>Interactive &amp; Geographical Plotting Exercises (Solutions)</li></ul><p><strong>Capstone Project - Python for Data Analysis &amp; Visualization</strong></p><ul><li>Project 1 - Oil vs Banks Stock Price during recession (Overview)</li><li>Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)</li><li>Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)</li><li>Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)</li><li>Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)</li></ul><p><strong>Python for Machine Learning (ML) - scikit-learn - Linear Regression Model</strong></p><ul><li>Introduction to ML - What, Why and Typesâ¦..</li><li>Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff</li><li>Linear Regression Model - Hands-on (Part 1)</li><li>Linear Regression Model Hands-on (Part 2)</li><li>Good to know! How to save and load your trained Machine Learning Model!</li><li>Linear Regression Model (Insurance Data Project Overview)</li><li>Linear Regression Model (Insurance Data Project Solutions)</li></ul><p><strong>Python for Machine Learning - scikit-learn - Logistic Regression Model</strong></p><ul><li>Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityâ¦etc.</li><li>Logistic Regression Model - Hands-on (Part 1)</li><li>Logistic Regression Model - Hands-on (Part 2)</li><li>Logistic Regression Model - Hands-on (Part 3)</li><li>Logistic Regression Model - Hands-on (Project Overview)</li><li>Logistic Regression Model - Hands-on (Project Solutions)</li></ul><p><strong>Python for Machine Learning - scikit-learn - K Nearest Neighbors</strong></p><ul><li>Theory: K Nearest Neighbors, Curse of dimensionality â¦.</li><li>K Nearest Neighbors - Hands-on</li><li>K Nearest Neighbors (Project Overview)</li><li>K Nearest Neighbors (Project Solutions)</li></ul><p><strong>Python for Machine Learning - scikit-learn - Decision Tree and Random Forests</strong></p><ul><li>Theory: D-Tree &amp; Random Forests, splitting, Entropy, IG, Bootstrap, Baggingâ¦.</li><li>Decision Tree and Random Forests - Hands-on (Part 1)</li><li>Decision Tree and Random Forests (Project Overview)</li><li>Decision Tree and Random Forests (Project Solutions)</li></ul><p><strong>Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)</strong></p><ul><li>Support Vector Machines (SVMs) - (Theory Lecture)</li><li>Support Vector Machines - Hands-on (SVMs)</li><li>Support Vector Machines (Project 1 Overview)</li><li>Support Vector Machines (Project 1 Solutions)</li><li>Support Vector Machines (Optional Project 2 - Overview)</li></ul><p><strong>Python for Machine Learning - scikit-learn - K Means Clustering</strong></p><ul><li>Theory: K Means Clustering, Elbow method â¦..</li><li>K Means Clustering - Hands-on</li><li>K Means Clustering (Project Overview)</li><li>K Means Clustering (Project Solutions)</li></ul><p><strong>Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)</strong></p><ul><li>Theory: Principal Component Analysis (PCA)</li><li>Principal Component Analysis (PCA) - Hands-on</li><li>Principal Component Analysis (PCA) - (Project Overview)</li><li>Principal Component Analysis (PCA) - (Project Solutions)</li></ul><p><strong>Recommender Systems with Python - (Additional Topic)</strong></p><ul><li>Theory: Recommender Systems their Types and Importance</li><li>Python for Recommender Systems - Hands-on (Part 1)</li><li>Python for Recommender Systems - - Hands-on (Part 2)</li></ul><p><strong>Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)</strong></p><ul><li>Natural Language Processing (NLP) - (Theory Lecture)</li><li>NLP-Challenges, Data Sources, Data Processing â¦..</li><li>Feature Engineering and Text Preprocessing in Natural Language Processing</li><li>NLP - Tokenization, Text Normalization, Vectorization, BoWâ¦.</li><li>BoW, TF-IDF, Machine Learning, Training &amp; Evaluation, Naive Bayes â¦</li><li>Pipeline feature to assemble several steps for cross-validationâ¦</li></ul><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free (Previously it was £10 * 11 = £110)</strong></li></ul><ul><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><p>If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a<strong> Flat 50% discount </strong>on hard copy certificates, and you can get each for just <strong>£10</strong>!</p><p><strong>P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. </strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        20 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>There is no experience or previous qualifications required for enrolment on this <strong>Data Science Course Bundle 2022</strong>. It is available to all students, of all academic backgrounds.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Our <strong>Data Science Course Bundle 2022</strong> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G.</p><p>There is no time limit for completing this course, it can be studied in your own time at your own pace.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Apex Learning

Data Science 2022 - CPD Accredited

<p>Do you want to prepare for your dream job but strive hard to find the right Data Scientist and Cybersecurity Engineer courses? Then, stop worrying, for our strategically modified<strong> Data Scientist and Cybersecurity Engineer</strong> bundle will keep you up to date with the relevant knowledge and most recent matters of this emerging field. So, invest your money and effort in our <strong> 33</strong> course mega Data Scientist and Cybersecurity Engineer bundle that will exceed your expectations within your budget.</p><p>The<strong> Data Scientist and Cybersecurity Engineer</strong> related fields are thriving across the UK, and recruiters are hiring the most knowledgeable and proficient candidates. It's a demanding field with magnitudes of lucrative choices. If you need more guidance to specialise in this area and need help knowing where to start, then <strong>StudyHub </strong>proposes a preparatory bundle. This comprehensive Data Scientist and Cybersecurity Engineer bundle will help you build a solid foundation to become a proficient worker in the sector.</p><p><strong>This Data Scientist and Cybersecurity Engineer Bundle consists of the following</strong><strong> 30 </strong><strong>CPD Accredited Premium courses - </strong></p><ul><li>Course 01:Basic Data Analysis</li><li>Course 02:Excel Data Analysis</li><li>Course 03:2021 Python Programming From A-Z: Beginner To Expert</li><li>Course 04:Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 05:2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 06:Mastering SQL Programming</li><li>Course 07:Research Methods in Business</li><li>Course 08:Cyber Security Incident Handling and Incident Response</li><li>Course 09:Microsoft Azure 2017</li><li>Course 10:AZ-104: Microsoft Azure Administrator</li><li>Course 11:Microsoft Azure Cloud Concepts</li><li>Course 12:Advanced Excel Analytics</li><li>Course 13:Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 14:Quick Data Science Approach from Scratch</li><li>Course 15:R Programming for Data Science</li><li>Course 16:Learn Python, JavaScript, and Microsoft SQL for Data science</li><li>Course 17:Google Data Studio: Data Analytics</li><li>Course 18:Introduction to Excel Data Tools and Data Management</li><li>Course 19:Microsoft Access Tables and Queries</li><li>Course 20:Microsoft Access Databases Forms and Reports</li><li>Course 21:Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</li><li>Course 22:VLOOKUP: Master Excel Formula VLOOKUP in 60 minutes!</li><li>Course 23:Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li><li>Course 24:GDPR</li><li>Course 25:Data Center Training Essentials: General Introduction</li><li>Course 26:Web Scraping and Mapping Dam Levels in Python and Leaflet</li><li>Course 27:Microsoft Power BI - Master Power BI in 90 Minutes!</li><li>Course 28:PowerBI Formulas</li><li>Course 29:Business Intelligence and Data Mining</li><li>Course 30:Financial Ratio Analysis for Business Decisions</li></ul><p><strong>3 </strong><strong>Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques-</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li></ul><p><strong>Learning Outcomes of Data Scientist and Cybersecurity Engineer</strong></p><p><strong>This tailor-made Data Scientist and Cybersecurity Engineer</strong><strong> bundle will allow you to-</strong></p><ul><li>Uncover your skills and aptitudes to break new ground in the related fields</li><li>Deep dive into the fundamental knowledge</li><li>Acquire some hard and soft skills in this area</li><li>Gain some transferable skills to elevate your performance</li><li>Maintain good report with your clients and staff</li><li>Gain necessary office skills and be tech savvy utilising relevant software</li><li>Keep records of your work and make a report</li><li>Know the regulations around this area</li><li>Reinforce your career with specific knowledge of this field</li><li>Know your legal and ethical responsibility as a professional in the related field</li></ul> <p>This Data Scientist and Cybersecurity Engineer Bundle resources were created with the help of industry experts, and all subject-related information is kept updated on a regular basis to avoid learners from falling behind on the latest developments.</p><p><strong>Certification</strong></p><p>After studying the complete Data Scientist and Cybersecurity Engineer training you will be able to take the assessment. After successfully passing the assessment you will be able to claim all courses pdf certificates and <strong>1 hardcopy certificate for the Title Course completely free</strong>. Other Hard Copy certificates need to be ordered at an additional cost of •8.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        330 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>Ambitious learners who want to strengthen their CV for their desired job should take advantage of the Data Scientist and Cybersecurity Engineer bundle! This bundle is also ideal for professionals looking for career advancement.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To participate in this Data Scientist and Cybersecurity Engineer course, all you need is -</p><ul><li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in Data Analysis and Cyber Security</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completing this essential Data Scientist and Cybersecurity Engineer Bundle, you will discover a new world of endless possibilities. These Data Scientist and Cybersecurity Engineer courses will help you to get a cut above the rest and allow you to be more efficient in the relevant fields.</p>
                        </div>

Certified in Cybersecurity (CC)

Data Engineering

Cyber Security

Cyber Security Foundation

Certified in Data Protection (CDP)

Data Scientist and Cybersecurity Engineer

Packt

Python

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

Natural Language Processing with Real-World Projects

<section style="color: rgb(33, 37, 41); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(33, 37, 41);"><h2 style="color: rgb(62, 57, 52); box-sizing: border-box;" fr-original-style=" color: rgb(62, 57, 52);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">About this Training Course</strong></h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The goal of data analytics is to gain understanding and intelligence from data which can be used to make improvements or changes in business. Analytics often involves studying past historical data for hidden valuable trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The aim of this 2 full-day course is to enable participants to understand the process behind Data Analytics or Data Science, equip them to find opportunities from their data to make better business decisions as well as apply the insights obtained from the data to identify potential gaps and develop improvements needed in the business. The applications are to help participants understand better where the possible use of data analytics can be applied in their respective field of work or discipline.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This course can also be offered through Virtual Instructor Led Training (VILT) format.</p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Download your brochure</a></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Please Send me the Brochure for Introduction to Data Analytics & Visualization" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Email me the brochure</a></p></section><section style="color: rgb(33, 37, 41); box-sizing: border-box;" fr-original-style=" color: rgb(33, 37, 41);"><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapseOne" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">TRAINING OBJECTIVES</strong></a><div data-parent="#accordion" 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;">By the end of this course, participants will be able to:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Describe the definition of Data Analytics, Data Science, Machine Learning &amp; Artificial intelligence and the main difference between them</li><li fr-original-style="" style="box-sizing: border-box;">Describe the use and purpose of data analytics and data science</li><li fr-original-style="" style="box-sizing: border-box;">Describe the strengths and weakness of data analytics and visualization in a business setting</li><li fr-original-style="" style="box-sizing: border-box;">Explain the importance of visualization, the first step to gain value from Data Analytics and what benefits visualization provides</li><li fr-original-style="" style="box-sizing: border-box;">Propose how data visualization and its tools can be used to identify potential gaps and develop improvements in the business</li><li fr-original-style="" style="box-sizing: border-box;">Describe how data analytics and visualisation can enhance decision making</li><li fr-original-style="" style="box-sizing: border-box;">Walkthrough the steps on how to start building data analytics capabilities, namely data governance and visualization.</li><li fr-original-style="" style="box-sizing: border-box;">Explain the technology buzzwords around Data Science, Artificial Intelligence and Big Data technology for better communication/discussion with technology vendors</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapseTwo" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">TARGET AUDIENCE</strong></a><div data-parent="#accordion" 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;">This course will benefit:</p><ul fr-original-style="" style="box-sizing: border-box;"><li 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;">Managers</p></li><li 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;">Analysts</p></li><li 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;">Technologists</p></li><li 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;">Technology leaders</p></li><li 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;">Oil &amp; Gas professionals&nbsp;who would like to find out more on what data analytics has to offer and how they can gain further insights from their existing data.</p></li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapsecl" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">COURSE LEVEL</strong></a><div data-parent="#accordion" 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;">Basic</li><li fr-original-style="" style="box-sizing: border-box;">Foundation</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapseThree" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">TRAINING METHODS</strong></a><div data-parent="#accordion" 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;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">System requirements for this course:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The system requirements are as follows:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Excel 2013</li><li fr-original-style="" style="box-sizing: border-box;">Windows 7 Enterprise Above</li><li fr-original-style="" style="box-sizing: border-box;">8GB Memory RAM, preferably 16 RAM</li></ul></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapseFive" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">TRAINER</strong></a><div data-parent="#accordion" 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;">Your expert course leader is an experienced data scientist, consultant and seasoned trainer with over 1,600 man-hours of training completed to date. He currently conducts training at the SAS Institute, and is also the adjunct senior faculty with SUSS and SCALE, NUS. He was a guest lecturer at NUS-ISS before and previously held an adjunct position in School of Information Systems, SMU. He is currently the mentor to the trainees accepted into the IMDA-SAS BIA Programme.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Prior to this, he was the Analytics Practicum Manager of the Master of IT in Business (Analytics) Singapore Management University, School of Information Systems. He managed the industrial relationships through projects and attachments. He often advised companies on the type of Analytics projects they can do with their data and is a co-supervisor for many MITB students on their Analytics Capstone Projects.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Through his career, he has gathered much experience on statistical modeling, from working in the industry, supervising MITB (Analytics) students and doing education research. His Analytics experience range from a wide variety of business functions and industries, gathered from consulting with companies from a wide variety of industries.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">His strong passion in Analytics/Data Science can be seen through his involvement in Analytics interest groups, being a Co-founder of DataScience.Sg, former Working Committee Chairman of SAS User Group Singapore and Data Ambassador for one of DataKind SG project. He also reads widely on the different topics related to Data Science and Artificial Intelligence, keeping himself up-to-date with their developments.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">His research interest lies in how Data Science can help organisations and businesses be more efficient and effective, and how organisations can build in-house data science capabilities successfully. He holds an MBA from the University of Adelaide. He obtained his Bachelor Degree in Economics from the National University of Singapore, with a minor in Computational Finance.</p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Download your brochure</a></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Please Send me the Brochure for Introduction to Data Analytics & Visualization" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;">Email me the brochure</a></p></div><a data-toggle="collapse" href="https://petroedgeasia.net/training/introduction-to-data-analytics-visualization-classroom-training/#collapseCS" style="color: rgb(51, 51, 51); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 51, 51);">POST TRAINING COACHING SUPPORT (OPTIONAL)</a><div data-parent="#accordion" 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;">To further optimise your learning experience from our courses, we also offer individualized &ldquo;One to One&rdquo; coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br fr-original-style="" style="box-sizing: border-box;"><a href="mailto:info@asiaedge.net?subject=Request%20for%20further%20information%20regarding%20post%20training%20coaching%20support" target="_blank" style="color: rgb(0, 123, 255); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(0, 123, 255);">Request for further information post training support</a>&nbsp;and fees applicable</p></div></section>

EnergyEdge - Training for a Sustainable Energy Future

Machine Learning (ML)

Develop your expertise in data analytics and visualization with EnergyEdge's Introduction to Data Analytics Visualization course. Join our classroom training to acquire essential skills in this field.

Introduction to Data Analytics & Visualization

Team of 10, pp

Normal Fee, pp

<p>Whether you're a beginner looking to start your coding journey or an experienced developer aiming to dive into data science and machine learning, this comprehensive course has you covered.</p><p>In this Python bundle, you'll embark on a transformative learning journey through 11 carefully curated courses. First, you'll lay the foundation with "Python Programming: Python Coding Essential Training," where you'll grasp the fundamentals of Python coding. Then, you'll advance into the world of data with the "Data Structures Complete Course," equipping you with the skills to manipulate and manage data efficiently.</p><p>But that's just the beginning. With courses spanning data science, machine learning, cloud computing, SQL programming, business intelligence, and critical thinking, this bundle is your passport to a versatile skill set that's in high demand. By enrolling in this course, you're investing in a brighter future, gaining the knowledge and expertise that will open doors to exciting career opportunities and enable you to tackle real-world challenges. Don't miss out on this chance to become a Python powerhouse and shape your professional destiny.</p><p><strong>This Python Powerhouse: Python Bundle Course for All Levels Bundle Consists of the following Premium courses:</strong></p><ul><li>Course 01: Python Programming: Python Coding Essential Training</li><li>Course 02: Data Structures Complete Course</li><li>Course 03: Data Science with Python</li><li>Course 04: Data Science &amp; Machine Learning with Python</li><li>Course 05: Python for Data Analysis</li><li>Course 06: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li>Course 07: SQL Programming Complete Bundle</li><li>Course 08: Higher Order Functions in Python - Level 03</li><li>Course 09: Basic Google Data Studio</li><li>Course 10: Business Intelligence and Data Mining Masterclass</li><li>Course 11: Decision Making and Critical Thinking</li></ul><p><strong>Learning Outcomes</strong></p><p>Upon completion of this bundle, you should be able to:</p><ul><li>Develop a solid foundation in Python programming, enabling them to apply coding skills efficiently.</li><li>Acquire a deep understanding of data structures and their applications in healthcare data management.</li><li>Master the use of Python for data science, allowing them to analyse healthcare data and extract valuable insights.</li><li>Explain SQL programming, which is crucial for handling healthcare databases and managing patient information securely.</li><li>Explore higher-order functions in Python, enhancing their problem-solving skills and programming capabilities.</li><li>Develop basic proficiency in Google Data Studio, a valuable tool for visualising healthcare data.</li><li>Master business intelligence and data mining techniques are specific to the healthcare industry.</li></ul><p>"Python Powerhouse: Python Bundle Course for All Levels" is your gateway to mastering Python and unleashing its potential across various domains. This comprehensive course comprises 11 meticulously designed modules, each geared towards equipping learners with essential skills and knowledge. Starting with "Python Programming: Python Coding Essential Training," you'll build a strong foundation in Python, a programming language known for its versatility and widespread use. As you progress through the "Data Structures Complete Course," you'll delve into the world of efficient data manipulation, a fundamental skill applicable in numerous professional settings.</p><p>One of the highlights of this bundle is "Data Science with Python," which empowers learners to harness Python's capabilities for data analysis. In "Data Science &amp; Machine Learning with Python," you'll take the next step, exploring advanced techniques that are vital in today's data-driven world. Furthermore, "Python for Data Analysis" enhances your proficiency in making data-driven decisions. Beyond data, you'll gain expertise in cloud computing, SQL programming, higher-order functions in Python, Google Data Studio, business intelligence, and critical thinking - all essential skills that can open doors to a multitude of career opportunities. This course is your passport to a fulfilling career by providing a comprehensive toolkit to excel in various domains and industries. Don't miss the chance to become a Python powerhouse and shape your professional destiny.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        110 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 course is tailored for:</p><ul><li>Data Enthusiasts</li><li>Intermediate Python Developers</li><li>Data Visualisation Enthusiasts</li><li>Aspiring Business Analysts</li><li>Anyone interested in Python</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No requirements to enrol this Python Powerhouse: Python Bundle Course for All Levels course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completion, various career opportunities include:</p><ul><li><strong>Software Developer</strong> (Average salary range of £35,000 to £70,000)</li><li><strong>Data Analyst</strong> (Average salary range of £25,000 and £60,000.)</li><li><strong>Machine Learning Engineer</strong> (Average salary range of £45,000 to £80,000)</li><li><strong>Web Developer </strong>(Average salary range of £30,000 to £65,000)</li><li><strong>Research Scientist </strong>(Average salary range of £40,000 to £75,000)</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPDQS Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPDQS Accredited Hard-Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>You will get the Hard Copy certificate for the title course (<strong>Python Programming: Python Coding Essential Training</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each.</p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p></div>

                        </div>

Python Powerhouse: Python Bundle Course for All Levels

<p>Forget stock markets and weather trends - the hottest data point right now is you. In a world brimming with information, data analysts are the alchemists, transforming raw numbers into actionable insights that fuel businesses.</p><p>The UK's digital gold rush is in full swing, with demand for skilled analysts surging 32% in the past year alone. This isn't just a trend; it's a tidal wave, and you can ride it to the top. Our Data Analyst Training equips you with the tools and knowledge to navigate this data-driven landscape with confidence.</p><p>From wrangling spreadsheets to crafting compelling visualisations, you'll master the skills that turn data into decisions. Don't just be part of the information age - become its architect. Enroll now and unlock your inner data analyst.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Analyse business data effectively for informed decision-making using diverse tools.</li><li>Develop expertise in utilising Excel for data analysis and forecasting.</li><li>Master the intricacies of Google Analytics and Google Data Studio.</li><li>Acquire proficiency in MySQL Database Development and SQL NoSQL Big Data.</li><li>Apply Python and R Programming for Data Science and Machine Learning applications.</li></ul><p>This comprehensive Data Analyst Training covers the fundamentals of data analysis, business insights, forecasting, database development, and programming, providing a holistic approach to harnessing the power of data. Embrace a versatile skill set that includes Excel, Google Analytics, MySQL, SQL, NoSQL, Big Data, Python, and R Programming. This course caters to aspiring data analysts, professionals seeking to enhance their analytical skills, and individuals venturing into the realm of data science.</p><p>This CPDQS Accredited Data Analyst Training consists of the following 11 courses:</p><ul><li>Diploma in Data Analysis Fundamentals</li><li>Business Data Analysis</li><li>Data Analysis and Forecasting in Excel</li><li>Excel Data Analysis</li><li>Learn Google Analytics-From a Beginner to becoming a Pro</li><li>Google Data Studio</li><li>MySQL Database Development</li><li>SQL NoSQL Big Data and Hadoop</li><li>Data Science &amp; Machine Learning with Python</li><li>R Programming for Data Science</li><li>Quick Data Science Approach from Scratch</li></ul><p><strong>Certification</strong><br />Once you have finished the course, you will be eligible to apply for a CPD QS accredited certificate as proof of your new skills and your continued professional development. Certificates are available as PDF for £9, or you can request a hard copy to be sent in the post for £15.<br /></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        35 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>Embark on this data-driven journey if you are:</p><ul><li>Aspiring Data Analysts</li><li>Professionals aiming to enhance analytical skills</li><li>Individuals entering the field of data science</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This course is open to all, requiring only a passion for data and curiosity to explore its depths. No formal prerequisites are necessary, ensuring accessibility for everyone interested in becoming a data analyst.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst - £30,000 to £50,000</li><li>Business Intelligence Analyst - £35,000 to £55,000</li><li>Database Developer - £40,000 to £60,000</li><li>Data Scientist - £45,000 to £70,000</li><li>Machine Learning Engineer - £50,000 to £80,000</li></ul>
                        </div>

Data Analyst Training Course

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</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. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

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Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

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        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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