Name: Machine Learning for Predictive Maps in Python and Leaflet Course
Price: 25 GBP
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Studyhub UK

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Programming Fundamentals

Trainers Course

Complete Python Machine Learning & Data Science Fundamentals

General Admission

<p>According to recent studies, the demand for data analysis and data science professionals is on the rise, with the industry expected to grow by almost 30% in the next decade. In today's digital world, having the skills to analyse and interpret data has never been more crucial, and it's never too late to start learning. In the UK alone, there are over 100,000 job openings in data analysis and data science, with salaries ranging from £40,000 to £100,000 per year.</p><p>This is why we are proud to introduce the Data Analysis, Data Analytics with Tableau &amp; Data Science &amp; Machine Learning with R bundle, the ultimate learning experience for anyone looking to boost their skills in this field. This bundle is a testament to the high quality of education that can be achieved through QLS-endorsed courses. With three such courses included in this bundle, learners are sure to receive a top-notch education that is both informative and engaging.</p><p>Investing in your education is always a wise choice, and with 3 QLS Endorsed certificates to your name, you can be sure that you're double boosting your resume. Apart from the QLS courses, the learners will get access to 10 CPD courses that are in line with the subject matter. These will enhance your overall understanding as well as broaden your knowledge.</p><p>Whether you're a beginner or have some experience in the field, this bundle has something for everyone. So why wait? Start your journey to a successful career in data analysis and data science today!</p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Data Analysis Fundamentals at QLS Level 5</li><li><strong>Course 02:</strong> Advanced Diploma in Data Analytics with Tableau at QLS Level 6</li><li><strong>Course 03:</strong> Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04: </strong>Microsoft Excel - Beginner, Intermediate &amp; Advanced</li><li><strong>Course 05:</strong> Conditional Formatting in Excel</li><li><strong>Course 06:</strong> Excel Lesson for Beginners: Create Pivot Table</li><li><strong>Course 07:</strong> Data Center Training Essentials: General Introduction</li><li><strong>Course 08:</strong> Google Data Studio: Data Analytics</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>Understanding of basic data analysis techniques and tools</li><li>Knowledge of data visualisation and reporting using Tableau</li><li>Ability to perform data analysis using R programming language</li><li>Understanding of data science and machine learning algorithms</li><li>Ability to create predictive models and interpret results</li></ul><p><strong>Basic Data Analysis</strong>: This course provides an introduction to data analysis techniques and tools. You'll learn how to collect, clean, and analyse data, as well as how to interpret results and create meaningful visualisations.</p><p><strong>Introduction to Data Analytics with Tableau:</strong> In this course, you'll learn how to use Tableau to analyse and visualise data. You'll learn how to connect to data sources, create reports, and publish interactive dashboards.</p><p><strong>2021 Data Science &amp; Machine Learning with R from A-Z: </strong>This comprehensive course covers data science and machine learning using R programming language. You'll learn how to perform data analysis, create predictive models, and interpret results.</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">
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                                        30 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Business professionals and managers who want to improve their data analysis skills</li><li>Marketing and sales professionals who want to gain a deeper understanding of customer behaviour and preferences</li><li>IT professionals who want to specialise in data analysis and machine learning</li><li>Data analysts and data scientists who want to expand their skill set</li><li>Anyone who is interested in learning about data analysis, data science, and machine learning.</li></ul>
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        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £40,000 to £70,000</li><li>Data Scientist: £50,000 to £100,000</li><li>Business Intelligence Analyst: £45,000 to £80,000</li><li>Data Engineer: £55,000 to £100,000</li><li>Data and Analytics Manager: £70,000 to £120,000</li></ul>
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        <div>Certificates</div>
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                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
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                        </div>

One Education

Data Analysis,  Data Analytics with Tableau & Data Science & Machine Learning with R Course

NextGen Learning

Statistics & Probability for Data Science & Machine Learning Course

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					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Data Science &amp; Machine Learning with R Complete Training has been specially designed to help learners gain a good command of Data Science &amp; Machine Learning with R Complete Training, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Data Science &amp; Machine Learning with R Complete Training, you will gain both practical and theoretical understanding of Data Science &amp; Machine Learning with R Complete Training that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>You will receive a completion certificate upon completing the course.</span>
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                                <h2>Course Curriculum</h2>
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					<td colspan="4"><strong>Data Science and Machine Learning Course Intro</strong></td>
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					<td>Data Science and Machine Learning Introduction</td><td><span>🕐 00:03:00</span></td>
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					<td>What is Data Science</td><td><span>🕐 00:10:00</span></td>
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					<td>Machine Learning Overview</td><td><span>🕐 00:05:00</span></td>
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					<td>Who is This Course for</td><td><span>🕐 00:03:00</span></td>
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					<td>Data Science and Machine Learning Marketplace</td><td><span>🕐 00:05:00</span></td>
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					<td>Data Science and Machine Learning Job Opportunities</td><td><span>🕐 00:03:00</span></td>
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					<td colspan="4"><strong>Getting Started with R</strong></td>
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					<td>Getting Started</td><td><span>🕐 00:11:00</span></td>
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					<td>Basics</td><td><span>🕐 00:06:00</span></td>
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					<td>Files</td><td><span>🕐 00:11:00</span></td>
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					<td>RStudio</td><td><span>🕐 00:07:00</span></td>
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					<td>Tidyverse</td><td><span>🕐 00:05:00</span></td>
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					<td>Resources</td><td><span>🕐 00:04:00</span></td>
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					<td colspan="4"><strong>Data Types and Structures in R</strong></td>
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					<td>Unit Introduction</td><td><span>🕐 00:30:00</span></td>
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					<td>Basic Type</td><td><span>🕐 00:09:00</span></td>
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					<td>Vector Part One</td><td><span>🕐 00:20:00</span></td>
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					<td>Vectors Part Two</td><td><span>🕐 00:25:00</span></td>
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					<td>Vectors - Missing Values</td><td><span>🕐 00:16:00</span></td>
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					<td>Vectors - Coercion</td><td><span>🕐 00:14:00</span></td>
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					<td>Vectors - Naming</td><td><span>🕐 00:10:00</span></td>
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					<td>Vectors - Misc</td><td><span>🕐 00:06:00</span></td>
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					<td>Creating Matrics</td><td><span>🕐 00:31:00</span></td>
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					<td>List</td><td><span>🕐 00:32:00</span></td>
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					<td>Introduction to Data Frames</td><td><span>🕐 00:19:00</span></td>
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					<td>Creating Data Frames</td><td><span>🕐 00:20:00</span></td>
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					<td>Data Frames: Helper Functions</td><td><span>🕐 00:31:00</span></td>
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					<td>Data Frames Tibbles</td><td><span>🕐 00:39:00</span></td>
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					<td>Intermediate Introduction</td><td><span>🕐 00:47:00</span></td>
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					<td>Relational Operations</td><td><span>🕐 00:11:00</span></td>
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					<td>Conditional Statements</td><td><span>🕐 00:11:00</span></td>
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					<td>Loops</td><td><span>🕐 00:08:00</span></td>
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					<td>Functions</td><td><span>🕐 00:14:00</span></td>
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					<td>Packages</td><td><span>🕐 00:11:00</span></td>
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					<td>Factors</td><td><span>🕐 00:28:00</span></td>
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					<td>Functional Programming</td><td><span>🕐 00:37:00</span></td>
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					<td>Data Manipulation in R Introduction</td><td><span>🕐 00:36:00</span></td>
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					<td>The Pipe Operator</td><td><span>🕐 00:15:00</span></td>
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					<td>The Filter Verb</td><td><span>🕐 00:22:00</span></td>
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					<td>The Select Verb</td><td><span>🕐 00:46:00</span></td>
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					<td>The Mutate Verb</td><td><span>🕐 00:32:00</span></td>
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					<td>The Arrange Verb</td><td><span>🕐 00:10:00</span></td>
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					<td>The Summarize Verb</td><td><span>🕐 00:23:00</span></td>
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					<td>Data Pivoting</td><td><span>🕐 00:43:00</span></td>
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					<td>JSON Parsing</td><td><span>🕐 00:11:00</span></td>
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					<td>String Manipulation</td><td><span>🕐 00:33:00</span></td>
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					<td>Web Scraping</td><td><span>🕐 00:59:00</span></td>
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					<td colspan="4"><strong>Data Visualization in R</strong></td>
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					<td>Data Visualization in R Section Intro</td><td><span>🕐 00:17:00</span></td>
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					<td>Getting Started</td><td><span>🕐 00:16:00</span></td>
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					<td>Aesthetics Mappings</td><td><span>🕐 00:25:00</span></td>
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					<td>Single Variable Plots</td><td><span>🕐 00:37:00</span></td>
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					<td>Two Variable Plots</td><td><span>🕐 00:21:00</span></td>
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					<td>Facets, Layering, and Coordinate Systems</td><td><span>🕐 00:18:00</span></td>
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					<td>Styling and Saving</td><td><span>🕐 00:12:00</span></td>
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					<td>Creating with R Markdown</td><td><span>🕐 00:29:00</span></td>
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					<td colspan="4"><strong>Building Webapps with R Shiny</strong></td>
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					<td>Introduction to R Shiny</td><td><span>🕐 00:26:00</span></td>
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					<td>A Basic R Shiny App</td><td><span>🕐 00:31:00</span></td>
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					<td>Other Examples with R Shiny</td><td><span>🕐 00:34:00</span></td>
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					<td colspan="4"><strong>Introduction to Machine Learning</strong></td>
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					<td>Machine Learning Part 1</td><td><span>🕐 00:22:00</span></td>
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					<td>Machine Learning Part 2</td><td><span>🕐 00:47:00</span></td>
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					<td colspan="4"><strong>Starting A Career in Data Science</strong></td>
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					<td>Starting a Data Science Career Section Overview</td><td><span>🕐 00:03:00</span></td>
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					<td>Data Science Resume</td><td><span>🕐 00:04:00</span></td>
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					<td>Getting Started with Freelancing</td><td><span>🕐 00:05:00</span></td>
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					<td>Top Freelance Websites</td><td><span>🕐 00:05:00</span></td>
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					<td>Personal Branding</td><td><span>🕐 00:05:00</span></td>
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					<td>Importance of Website and Blo</td><td><span>🕐 00:04:00</span></td>
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					<td>▶</td>
					<td>Networking Do's and Don'ts</td><td><span>🕐 00:04:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
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Skill Up

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

Data Science & Machine Learning with R Complete Training

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			<h4>Overview</h4>		</div>
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							<p><span>By enroling in Spatial Data Visualization and Machine Learning in Python, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Spatial Data Visualization and Machine Learning in Python course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Spatial Data Visualization and Machine Learning in Python course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Spatial Data Visualization and Machine Learning in Python Program.</span></p><p><span>So enrol in our Spatial Data Visualization and Machine Learning in Python course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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			<h4>Description</h4>		</div>
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							<p><span>Enroling in this Spatial Data Visualization and Machine Learning in Python course can improve your Spatial Data Visualization and Machine Learning in Python perspective, regardless of your skill levels in the Spatial Data Visualization and Machine Learning in Python topics you want to master. If you're already a Spatial Data Visualization and Machine Learning in Python expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Spatial Data Visualization and Machine Learning in Python insights that will help you make the most of your time. This Spatial Data Visualization and Machine Learning in Python course will act as a guide for you if you've ever wished to excel at Spatial Data Visualization and Machine Learning in Python.</span></p>						</div>
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			<h4>Why Choose Us?</h4>		</div>
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										<span>This course is accredited by the CPD Quality Standards.</span>
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										<span>Lifetime access to the whole collection of the learning materials.</span>
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										<span>Enroling in the course has no additional cost.</span>
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										<span>You can study and complete the course at your own pace.</span>
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										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
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			<h4>Will I Receive A Certificate Of Completion?</h4>		</div>
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							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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			<h4>Who Is This Course For?</h4>		</div>
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							<p><span>This Spatial Data Visualization and Machine Learning in Python course is a great place to start if you're looking to start a new career in Spatial Data Visualization and Machine Learning in Python field. This training is for anyone interested in gaining in-demand Spatial Data Visualization and Machine Learning in Python proficiency to help launch a career or their business aptitude. </span></p>						</div>
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			<h4>Requirements</h4>		</div>
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							<p><span>The Spatial Data Visualization and Machine Learning in Python course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Spatial Data Visualization and Machine Learning in Python, without any fuss.</span></p>						</div>
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			<h4>Career Path</h4>		</div>
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							<p><span>This Spatial Data Visualization and Machine Learning in Python training will assist you develop your Spatial Data Visualization and Machine Learning in Python ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Spatial Data Visualization and Machine Learning in Python professional story and personalise your path to a new career. Furthermore, developing this Spatial Data Visualization and Machine Learning in Python skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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	<span>Course Curriculum</span>
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					<td colspan="4">Section 01: Introduction</td>
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					<td><span><i></i> 00:14:00</span></td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td><span><i></i> 00:24:00</span></td>
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					<td colspan="4">Section 04: Data Visualization</td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td><span><i></i> 00:13:00</span></td>
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					<td>Adding predicted data to our plots - Part 2</td>
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					<td><span><i></i> 00:14:00</span></td>
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					<td>Adding predicted data to our plots - Part 3</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td colspan="4">Section 07: Creating the Dashboard Server</td>
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					<td><span><i></i> 00:01:00</span></td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td><span><i></i> 00:00:00</span></td>
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					<td colspan="4">Order Your Certificate</td>
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					<td>Order Your Certificate</td>
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					<td><span><i></i> 00:00:00</span></td>
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			</div>

Spatial Data Visualization and Machine Learning in Python Course

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Make the most of the plotting and AI capabilities of the world's benchmark programming language by taking this course to create </span><b>Spatial Data Visualisation and Machine Learning in Python Level 4. </b><span>Using the intuitive syntax available to you, you will be amazed at the results you can achieve with the power of its libraries and mapping potential for all manner of complex projects.  </span></p><p><span>This comprehensive </span>Python <span>tutorial is an excellent way to learn the important and potentially ground-breaking aspects of machine learning. With the benefit of expert guidance and step-by-step training, IT technology, you will be taken from quick installations to complex coding. You will learn how to become proficient with coding capabilities that will put you at the forefront of advanced programming techniques and the aptitude to envisage AI projects that will impress and be used for practical and useful purposes. </span></p></div>
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					<div><p>This best selling <strong>Spatial Data Visualization and Machine Learning in Python Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Spatial Data Visualization and Machine Learning in Python Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p>
<p>The <strong>Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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> Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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>Spatial Data Visualization and Machine Learning in Python 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

Spatial Data Visualization and Machine Learning in Python Level 4

<p><strong>Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2</strong></p><p>The Machine Learning Model Using AWS SageMaker Canvas is all you need to advance your career in the relevant fields.</p><p>Enrol now to discover everything you need to know about the Machine Learning Model Using AWS SageMaker Canvas and the skills to improve your talents in this field and be confident in your knowledge bucket with One Education as your right hand!</p><p><strong>Expert Support</strong></p><p>Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course.</p><p><strong>Key Benefits</strong></p><ul><li>Learning materials of the Design course contain engaging voiceover and visual elements for your comfort.</li><li>Get 24/7 access to all content for a full year.</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li></ul><p><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>Quality License Endorsed</li><li>Certificate Available Upon Course Completion</li></ul><p><strong>Course Curriculum:</strong></p><p><strong><strong>Introduction to Machine Learning</strong></strong></p><ul><li>What is Machine Learning?</li></ul><p><strong><strong>Introduction to AWS</strong></strong></p><ul><li>What is Amazon Web Services (AWS)?</li><li>Signing into AWS Console</li></ul><p><strong><strong>Introduction to SageMaker</strong></strong></p><ul><li><strong>What is SageMaker, and how it is used for Machine Learning?</strong></li><li>What is SageMaker Canvas?</li></ul><p><strong><strong>Setup</strong></strong></p><ul><li>SageMaker Domain and User Setup</li><li>Setup Data in S3 Buckets for use in SageMaker</li></ul><p><strong><strong>SageMaker Canvas Interface Walkthrough</strong></strong></p><ul><li>Navigating in SageMaker Canvas</li></ul><p><strong><strong>Project 1: Banknote Authentication</strong></strong></p><ul><li>Adding Training Data</li><li>Building and Using Model for Prediction</li><li>Predict Single &amp; Batch Dataset</li><li>Validating Accuracy of Batch Predictions</li></ul><p><strong><strong>Project 2: Spam SMS Detection</strong></strong></p><ul><li>Adding Train &amp; Test Data</li><li>Building and Using Model for Prediction</li><li>Predicting Data and Validating Accuracy</li></ul><p><strong><strong>Project 3: Customer Churn Prediction</strong></strong></p><ul><li>Adding Data</li><li>Building Model</li><li>Performing &amp; Validating Predictions</li></ul><p><strong><strong>Project 4: Wine Quality Prediction</strong></strong></p><ul><li>Adding &amp; Joining Datasets</li><li>Building Model</li><li>Predicting Test Data</li></ul><p><strong><strong>Assignmen</strong>t</strong></p><ul><li>White Wine Quality Prediction</li></ul><p><strong><strong>Other Important Features in SageMaker Canvas</strong></strong></p><ul><li>Versioning</li></ul><p><strong><strong>Congratulations &amp; Next Steps</strong></strong></p><ul><li>Getting Datasets for Practice</li><li>Getting Help on SageMaker Canvas</li><li>Congratulations &amp; Thankyou</li></ul><p><strong>Exam and Assessment</strong></p><ul><li>MCQ based test</li><li>60% Marks to pass</li><li>Instant Assessment and Feedback</li></ul><p><strong>Certification</strong></p><ul><li>CPD Accredited PDF and Hardcopy Certificate</li><li>Level 2 QLS Endorsed Hardcopy Certificate for <strong>Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        120 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 Machine Learning Model Using AWS SageMaker Canvas course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Machine Learning Model Using AWS SageMaker Canvas course is open to all, with no formal entry requirements.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon successful completion of the Machine Learning Model Using AWS SageMaker Canvas Course, learners will be equipped with many indispensable skills and have the opportunity to grab.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £69
        </p>
            <div> <p><strong>QLS Endorsed Certificate</strong></p><p>Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying -</p><ul><li>Within the UK: £69</li><li>International: £69 + £10 (postal charge) = £79 </li></ul><p><strong>CPD Accredited Certification from One Education</strong></p><ul><li>Hardcopy Certificate (within the UK): £15</li><li>Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</li></ul> </div>

                        </div>

Machine Learning Model Using AWS SageMaker Canvas Course

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification</p>Overview<p>In this course, you will develop AI solutions for business problems.<br />You will:<br />Solve a given business problem using AI and ML.<br />Prepare data for use in machine learning.<br />Train, evaluate, and tune a machine learning model.<br />Build linear regression models.<br />Build forecasting models.<br />Build classification models using logistic regression and k -nearest neighbor.<br />Build clustering models.<br />Build classification and regression models using decision trees and random forests.<br />Build classification and regression models using support-vector machines (SVMs).<br />Build artificial neural networks for deep learning.<br />Put machine learning models into operation using automated processes.<br />Maintain machine learning pipelines and models while they are in production</p><p>Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.</p></div><div id="course-pills-2"><h4>Solving Business Problems Using AI and ML</h4><ul><li>Topic A: Identify AI and ML Solutions for Business Problems</li><li>Topic B: Formulate a Machine Learning Problem</li><li>Topic C: Select Approaches to Machine Learning</li></ul><h4>Preparing Data</h4><ul><li>Topic A: Collect Data</li><li>Topic B: Transform Data</li><li>Topic C: Engineer Features</li><li>Topic D: Work with Unstructured Data</li></ul><h4>Training, Evaluating, and Tuning a Machine Learning Model</h4><ul><li>Topic A: Train a Machine Learning Model</li><li>Topic B: Evaluate and Tune a Machine Learning Model</li></ul><h4>Building Linear Regression Models</h4><ul><li>Topic A: Build Regression Models Using Linear Algebra</li><li>Topic B: Build Regularized Linear Regression Models</li><li>Topic C: Build Iterative Linear Regression Models</li></ul><h4> Building Forecasting Models</h4><ul><li>Topic A: Build Univariate Time Series Models</li><li>Topic B: Build Multivariate Time Series Models</li></ul><h4>Building Classification Models Using Logistic Regression and k-Nearest Neighbor</h4><ul><li>Topic A: Train Binary Classification Models Using Logistic Regression</li><li>Topic B: Train Binary Classification Models Using k-Nearest Neighbor</li><li>Topic C: Train Multi-Class Classification Models</li><li>Topic D: Evaluate Classification Models</li><li>Topic E: Tune Classification Models</li></ul><h4>Building Clustering Models</h4><ul><li>Topic A: Build k-Means Clustering Models</li><li>Topic B: Build Hierarchical Clustering Models</li></ul><h4>Building Decision Trees and Random Forests</h4><ul><li>Topic A: Build Decision Tree Models</li><li>Topic B: Build Random Forest Models</li></ul><h4>Building Support-Vector Machines</h4><ul><li>Topic A: Build SVM Models for Classification</li><li>Topic B: Build SVM Models for Regression</li></ul><h4>Building Artificial Neural Networks</h4><ul><li>Topic A: Build Multi-Layer Perceptrons (MLP)</li><li>Topic B: Build Convolutional Neural Networks (CNN)</li><li>Topic C: Build Recurrent Neural Networks (RNN)</li></ul><h4>Operationalizing Machine Learning Models</h4><ul><li>Topic A: Deploy Machine Learning Models</li><li>Topic B: Automate the Machine Learning Process with MLOps</li><li>Topic C: Integrate Models into Machine Learning Systems</li></ul><h4>Maintaining Machine Learning Operations</h4><ul><li>Topic A: Secure Machine Learning Pipelines</li><li>Topic B: Maintain Models in Production</li></ul></div>

Nexus Human

Machine Learning (ML)

NLP Practitioner

Azure Machine Learning

Microsoft AI-900

AWS Certified Solutions Architect

AI (Artificial Intelligence)

CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)

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			<h2>Overview</h2>		</div>
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							<p><span>By enroling in Machine Learning for Predictive Maps in Python and Leaflet, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Machine Learning for Predictive Maps in Python and Leaflet course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Machine Learning for Predictive Maps in Python and Leaflet course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Machine Learning for Predictive Maps in Python and Leaflet Program.</span></p><p><span>So enrol in our Machine Learning for Predictive Maps in Python and Leaflet course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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			<h2><b>Description</b>
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							<p><span>Enroling in this Machine Learning for Predictive Maps in Python and Leaflet course can improve your Machine Learning for Predictive Maps in Python and Leaflet perspective, regardless of your skill levels in the Machine Learning for Predictive Maps in Python and Leaflet topics you want to master. If you're already a Machine Learning for Predictive Maps in Python and Leaflet expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Machine Learning for Predictive Maps in Python and Leaflet insights that will help you make the most of your time. This Machine Learning for Predictive Maps in Python and Leaflet course will act as a guide for you if you've ever wished to excel at Machine Learning for Predictive Maps in Python and Leaflet.</span></p>						</div>
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			<h2>Why Choose Us?</h2>		</div>
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							<ul><li><span>After successfully completing the Machine Learning for Predictive Maps in Python and Leaflet Course, you will receive a </span><b>FREE PDF Certificate </b><span>as evidence of your newly acquired abilities.</span></li><li><span>Lifetime access to the whole collection of Machine Learning for Predictive Maps in Python and Leaflet learning materials.</span></li><li><span>Online test with immediate results</span></li><li><span>Enroling in the course has no additional cost.</span></li><li><span>You can study and complete the Machine Learning for Predictive Maps in Python and Leaflet course at your own pace.</span></li><li><span>Study for the Machine Learning for Predictive Maps in Python and Leaflet course using any internet-connected device, such as a computer, tablet, or mobile device.</span></li></ul>						</div>
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			<h2>Who Is This Course For?</h2>		</div>
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							<p><span>This Machine Learning for Predictive Maps in Python and Leaflet course is a great place to start if you're looking to start a new career in Machine Learning for Predictive Maps in Python and Leaflet field. This training is for anyone interested in gaining in-demand Machine Learning for Predictive Maps in Python and Leaflet proficiency to help launch a career or their business aptitude. </span></p>						</div>
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			<h2>Requirements</h2>		</div>
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							<p><span>The Machine Learning for Predictive Maps in Python and Leaflet course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Machine Learning for Predictive Maps in Python and Leaflet, without any fuss.</span></p>						</div>
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			<h2>Career Path</h2>		</div>
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							<p><span>This Machine Learning for Predictive Maps in Python and Leaflet training will assist you develop your Machine Learning for Predictive Maps in Python and Leaflet ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Machine Learning for Predictive Maps in Python and Leaflet professional story and personalise your path to a new career. Furthermore, developing this Machine Learning for Predictive Maps in Python and Leaflet skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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				<h3>
	<span>Course Curriculum</span>
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<table>				<tbody><tr>
					<td colspan="4">Section 01: Introduction</td>
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					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td colspan="4">Section 02: Setup and Installations</td>
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					<td><i></i></td>
					<td>Python Installation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td><i></i></td>
					<td>Creating a Python Virtual Environment</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td><i></i></td>
					<td>Installing Django</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td><i></i></td>
					<td>Installing Visual Studio Code IDE</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>Installing PostgreSQL Database Server Part 1</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td><i></i></td>
					<td>Installing PostgreSQL Database Server Part 2</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td colspan="4">Section 03: Writing the Django Server-Side Code</td>
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					<td><i></i></td>
					<td>Adding the settings.py Code</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Creating a Django Model</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding the admin.py Code</td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td colspan="4">Section 04: Writing the Application Front-end Code</td>
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					<td>Creating Template Files</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating Django Views</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating URL Patterns for the REST API</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Adding the index.html code</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Adding the layout.html code</td>
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					<td><span><i></i> 00:19:00</span></td>
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					<td>Creating our First Map</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding Markers</td>
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					<td><span><i></i> 00:16:00</span></td>
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					<td colspan="4">Section 05: Machine Learning</td>
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					<td>Installing Jupyter Notebook</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Data Pre-processing</td>
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					<td><span><i></i> 00:31:00</span></td>
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					<td>Model Selection</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
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					<td>Model Evaluation and Building a Prediction Dataset</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
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					<td colspan="4">Section 06: Automating the Machine Learning Pipeline</td>
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					<td><i></i></td>
					<td>Creating a Django Model</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Embedding the Machine Learning Pipeline in the Application</td>
					<td> </td>
					<td><span><i></i> 00:42:00</span></td>
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					<td><i></i></td>
					<td>Creating a URL Endpoint for our Prediction Dataset</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">Section 07: Leaflet Programming</td>
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					<td><i></i></td>
					<td>Creating Multiple Basemaps</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td><i></i></td>
					<td>Creating the Marker Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Creating the Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><i></i></td>
					<td>Creating the Predicted Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td>Creating the Predicted High Risk Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td>Creating the Legend</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td>Creating the Prediction Score Legend</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
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					<td colspan="4">Section 08: Project Source Code</td>
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					<td>Resource</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Machine Learning for Predictive Maps in Python and Leaflet 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>

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

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

Packt

Data Science

Python

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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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!

The Complete Machine Learning Course with Python

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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

Learning To Learn

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.

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 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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