Name: Spatial Data Visualization and Machine Learning in Python
Price: 25 GBP
Availability: InStock

<h1><b>Overview</b></h1>
<p><b>Uplift Your Career &amp; Skill Up to Your Dream Job - Learning Simplified From Home!</b></p>
<p><span>Kickstart your career &amp; boost your employability by helping you discover your skills, talents, and interests with our special </span><b>2021 Data Science &amp; Machine Learning with R from A-Z </b><span>Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides the professional training that employers are looking for in today's workplaces.</span></p>
<p><span>The </span><b>2021 Data Science &amp; Machine Learning with R from A-Z </b><span> Course is one of the most prestigious training offered at </span><b>Skillwise </b><span>and is highly valued by employers for good reason. This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z Course</b><span>  Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field.</span></p>
<p><span>This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z</b><span>  Course, like every one of Skillwise's courses, is meticulously developed and well-researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly.</span></p>
<p><span>At </span><b>Skillwise</b><span>, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from </span><b>Skillwise</b><span>, you get unlimited Lifetime access with 24/7 dedicated tutor support.</span></p>
<h3><b>Why buy this 2021 Data Science &amp; Machine Learning with R from A-Z Course ?</b></h3>
<ol>
<li><span>Lifetime access to the course forever</span></li>
<li><b>Digital Certificate, Transcript, and student ID </b><span>are all included in the price</span></li>
<li><span>Absolutely no hidden fees</span></li>
<li><span>Directly receive </span><b>CPD Quality Standard-accredited</b><span> qualifications after course completion</span></li>
<li><span>Receive one-to-one assistance every weekday from professionals</span></li>
<li><span>Immediately receive the PDF certificate after passing</span></li>
<li><span>Receive the original copies of your certificate and transcript on the next working day</span></li>
<li><span>Easily learn the skills and knowledge from the comfort of your home</span></li>
</ol>
<h1><b>Certification</b></h1>
<p><span>After studying the course materials of the </span><b>2021 Data Science &amp; Machine Learning with R from A-Z Course</b><span>  there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the PDF certificate for free. Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p>
<h1><b>Who is this course for?</b></h1>
<p><span>This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z </b><span> course is ideal for</span></p>
<ul>
<li><span>Students</span></li>
<li><span>Recent graduates</span></li>
<li><span>Job Seekers</span></li>
<li><span>Anyone interested in this topic</span></li>
<li><span>People already work in relevant fields and want to polish their knowledge and skills.</span></li>
</ul>
<h1><b>Prerequisites</b></h1>
<p><span>This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z Course</b><span>  does not require you to have any prior qualifications or experience. You can just enrol and start learning. This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z Course</b><span>  was made by professionals and it is compatible with all PCs, Macs, tablets, and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h1><b>Career path</b></h1>
<p><span>As this course comes with multiple courses included as a bonus, you will be able to pursue multiple occupations. This </span><b>2021 Data Science &amp; Machine Learning with R from A-Z Course</b><span>  is a great way for you to gain multiple skills from the comfort of your home.</span></p>
<h1></h1>

SkillWise

2021 Data Science & Machine Learning with R from A-Z Course

General Admission

Packt

Computer Vision

Data Science

Python

Programming

This course will help you learn the programming fundamentals with Python 3. It is designed for beginners in Python and is a complete masterclass. This course will help you understand Python GUI, data science, full-stack web development with Django, machine learning, artificial intelligence, Natural Language Processing, and Computer Vision.

Python in Practice - 15 Projects to Master Python

<h3><b>Overview</b></h3>
<p><span>In the era where information is abundant and decisions are driven by data, have you ever pondered, 'what is machine learning?' or 'what is data science?' Dive into the realm of '</span><strong><span>Data Science &amp; Machine Learning with R from A-Z</span></strong><span>,' a comprehensive guide to unravel these complexities. This course effortlessly blends the foundational aspects of data science with the intricate depths of machine learning algorithms, all through the versatile medium of R. As the digital economy booms, the demand for machine learning jobs continues to surge. Equip yourself with the proficiency to navigate this dynamic field and transition from being an inquisitive mind to a sought-after professional in the space of data science and machine learning with R.</span></p>
<p><strong><span>Learning Outcomes:</span></strong></p>
<ol>
<li><span>Understand the foundational concepts of data science and machine learning.</span></li>
<li><span>Familiarise oneself with the R environment and its functionalities.</span></li>
<li><span>Master data types, structures, and advanced techniques in R.</span></li>
<li><span>Acquire proficiency in data manipulation and visual representation using R.</span></li>
<li><span>Generate comprehensive reports using R Markdown and design web applications with R Shiny.</span></li>
<li><span>Gain a thorough understanding of machine learning methodologies and their applications.</span></li>
<li><span>Gain insights into initiating a successful career in the data science sector.</span></li>
</ol>
<h3><b>Why buy this Data Science &amp; Machine Learning with R from A-Z course?</b></h3>
<ol>
<li><span>Unlimited access to the course for forever</span></li>
<li><b>Digital Certificate, Transcript, student ID</b><span> all included in the price</span></li>
<li><span>Absolutely no hidden fees</span></li>
<li><span>Directly receive </span><b>CPD </b><span>accredited qualifications after course completion</span></li>
<li><span>Receive one to one assistance on every weekday from professionals</span></li>
<li><span>Immediately receive the PDF certificate after passing</span></li>
<li><span>Receive the original copies of your certificate and transcript on the next working day</span></li>
<li><span>Easily learn the skills and knowledge from the comfort of your home</span></li>
</ol>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials of the </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.</span></p>
<h3><b>Who is this Data Science &amp; Machine Learning with R from A-Z course for?</b></h3>
<p><span>This </span><span>course is ideal for</span></p>
<ul>
<li><span>Individuals keen on exploring the intricacies of machine learning and data science.</span></li>
<li><span>Aspiring data analysts and scientists looking to specialise in Machine Learning with R.</span></li>
<li><span>IT professionals aiming to diversify their skill set in the emerging data-driven market.</span></li>
<li><span>Researchers seeking to harness the power of R for data representation and analysis.</span></li>
<li><span>Academics and students aiming to bolster their understanding of modern data practices with R.</span></li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Career path</b></h3>
<ol>
<li><strong><span>Data Scientist</span></strong><span> - Average salary range: £35,000 - £70,000 Per Annum</span></li>
<li><strong><span>Machine Learning Engineer</span></strong><span> - Average salary range: £50,000 - £80,000 Per Annum</span></li>
<li><strong><span>Data Analyst</span></strong><span> - Average salary range: £28,000 - £55,000 Per Annum</span></li>
<li><strong><span>R Developer</span></strong><span> - Average salary range: £30,000 - £60,000 Per Annum</span></li>
<li><strong><span>R Shiny Web Developer</span></strong><span> - Average salary range: £32,000 - £65,000 Per Annum</span></li>
<li><strong><span>Machine Learning Researcher</span></strong><span> - Average salary range: £40,000 - £75,000 Per Annum</span></li>
</ol><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"> Data Science and Machine Learning Course Intro</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Data Science</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Overview</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Who is This Course for</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Marketplace</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Job Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Getting Started with R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basics</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>RStudio</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidyverse</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resources</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Unit Introduction</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic Type</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vector Part One</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors Part Two</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Coercion</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Naming</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Misc</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Matrics</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames: Helper Functions</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames Tibbles</td>
					<td> </td>
					<td><span><i></i> 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Intermediate R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Intermediate Introduction</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Relational Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditional Statements</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factors</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dates and Times</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functional Programming</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Import or Export</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Database</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Manipulation in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Manipulation in R Introduction</td>
					<td> </td>
					<td><span><i></i> 00:36:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidy Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Pipe Operator</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Filter Verb</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Select Verb</td>
					<td> </td>
					<td><span><i></i> 00:46:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Mutate Verb</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Arrange Verb</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Summarize Verb</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Pivoting</td>
					<td> </td>
					<td><span><i></i> 00:43:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>JSON Parsing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>String Manipulation</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Web Scraping</td>
					<td> </td>
					<td><span><i></i> 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Visualization in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Visualization in R Section Intro</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Aesthetics Mappings</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Single Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Two Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Facets, Layering, and Coordinate Systems</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Styling and Saving</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Reports with R Markdown</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating with R Markdown</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Building Webapps with R Shiny</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>A Basic R Shiny App</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Other Examples with R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction to Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 1</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 2</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Starting A Career in Data Science</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Starting a Data Science Career Section Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Resume</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started with Freelancing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top Freelance Websites</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Personal Branding</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importance of Website and Blo</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Networking Do's and Don'ts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Data Science &amp; Machine Learning with R</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Data Engineering

Data Skills

Data Science & Machine Learning with R from A-Z

<h2>Machine Learning and other AI: Are You Ready?</h2>
<p>Machine Learning is the latest 'hot' title in computing and Artificial Intelligence. It sounds new but is influencing your life already. Machine Learning and AI will affect more and more of your life as they mature and more enabling technologies intersect with them. Machine Learning will change many disciplines and careers, overcoming scale issues, enabling better knowledge and insights, and augmenting many professions. Are you ready?</p>
<p>This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs.</p>
<p>Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.</p>
<p>Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.</p>

IIL Europe Ltd

Machine Learning and other AI: Are You Ready?

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security </strong> bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Python &amp; Data Science </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security package, you will receive complimentary PDF certificates for all courses in <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science</strong> bundle at no extra cost. Equip yourself with the Python &amp; Data Science bundle to confidently navigate your career path or personal development journey. Enrol our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science </strong>bundletoday and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This</strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Bundle Comprises the Following CPD Accredited Courses:</strong></p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programming: Beginner To Expert<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning with Python<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Coding with Python 3<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Coding With HTML, CSS, &amp; Javascript<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python for Spatial Analysis in ArcGIS<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programming Bible | Networking, GUI, Email, XML, CGI<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Business Intelligence and Data Mining<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">SQL for Data Science, Data Analytics and Data Visualization<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Science with Numpy, Pandas and Matplotlib<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Computing / CompTIA Cloud+ (CV0-002)<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cyber Security Awareness Training<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learn Ethical Hacking From A-Z: Beginner To Expert<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Easy to Advanced Data Structures<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">R Programming for Data Science<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">GDPR UK Training</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Career Development Plan Fundamentals<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CV Writing and Job Searching<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learn to Level Up Your Leadership<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Networking Skills for Personal Success<br fr-original-style="" style="box-sizing: border-box;"></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ace Your Presentations: Public Speaking Masterclass</strong></li></ol><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcome:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">By completing the course, you will:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Gain comprehensive insights into multiple fields.</li><li fr-original-style="" style="box-sizing: border-box;">Foster critical thinking and problem-solving skills across various disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Understand industry trends and best practices through the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Python &amp; Data Science</strong> Bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Develop practical skills applicable to real-world situations.</li><li fr-original-style="" style="box-sizing: border-box;">Enhance personal and professional growth with the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Python &amp; Data Science </strong>Bundle<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">.</strong></li><li fr-original-style="" style="box-sizing: border-box;">Build a strong knowledge base in your chosen course via the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science </strong>Bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Benefit from the flexibility and convenience of online learning.</li><li fr-original-style="" style="box-sizing: border-box;">With the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science</strong> package, validate your learning with a CPD certificate.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security , a rich anthology of 15 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science</strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. This Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security bundle offers you the flexibility and convenience to learn at your own pace. Make the Python &amp; Data Science package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
                                    <div fr-original-style="" style="box-sizing: border-box;">
                                        200 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security</strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Without any formal requirements, you can delightfully enrol this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security</strong> course.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python, Data Science, Machine Learning, Data Mining &amp; Cyber Security </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python &amp; Data Science </strong>bundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will get a complimentary Hard Copy Certificate.</p></div>
        <h4 fr-original-style="" style="box-sizing: border-box;">Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>

                        </div>

NextGen Learning

Cyber Security

Professional Skills Course (PSC)

Digital Skills

Continuous Professional Development (CPD)

ICDL (International Certification of Digital Literacy)

Python, Data Science, Machine Learning, Data Mining & Cyber Security - 20 Courses Bundle

<p>The ongoing pandemic has brought the world to a standstill, but with our Hands on Machine Learning Project - Covid Mask Detector course, you can be a part of the solution. In this course, you'll learn how to develop a Covid mask detector using machine learning, a powerful tool for detecting whether people are wearing masks or not. With this technology, you can help prevent the spread of the virus and keep people safe.</p><p>Our comprehensive course modules are designed to provide you with a hands-on learning experience. You'll master OpenCV, a popular computer vision library, and learn how to detect faces and build models with TensorFlow. You'll also learn how to design and build the app, upload files, and deploy it on AWS. This course is perfect for aspiring data scientists, machine learning engineers, and developers who want to make a difference in the world by developing cutting-edge technology.</p><p>With the skills and knowledge gained from this course, you can develop your own Covid mask detector and help protect people from the virus. You'll also gain valuable experience in machine learning app development, opening up new career opportunities in the exciting field of machine learning.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Develop a Covid mask detector using machine learning.</li><li>Master OpenCV, a popular computer vision library.</li><li>Build models with TensorFlow.</li><li>Design and build the app, upload files, and deploy it on AWS.</li><li>Gain valuable experience in machine learning app development.</li></ul><p>The Hands-on Machine Learning Project - Covid Mask Detector course is designed to provide you with the skills and knowledge needed to develop a mask detector using deep learning. In this course, you'll learn how to master OpenCV, an open-source computer vision library used for image processing and face detection. You'll also learn how to build and train a deep learning model using Tensorflow, a popular machine learning framework. The course is perfect for aspiring data scientists, machine learning engineers, and developers who want to make a positive impact on society by contributing to public health and safety efforts.</p><p>By the end of this course, you'll have a deep understanding of how to develop a mask detector app that can be used to detect whether individuals are wearing masks in public spaces. You'll be able to master OpenCV and use it to preprocess and detect faces in images. You'll also learn how to build and train a deep learning model using TensorFlow and how to deploy your mask detector app on AWS. This course provides a unique opportunity for individuals to gain real-world experience in developing cutting-edge technology that can make a positive impact on society.</p><p><strong>â±â± </strong><strong>Hands on Machine Learning Project - Covid Mask Detector</strong><strong> Course Curriculum </strong></p><p><strong>Section 01: Introduction</strong></p><ul><li>Introduction to Course</li></ul><p><strong>Section 02: Mastering OpenCV</strong></p><ul><li>Getting System Ready</li><li>Read and Write Images</li><li>Resize and Crop</li><li>Working with Shapes</li><li>Working with Text</li></ul><p><strong>Section 03: Pre-Requisite for Face Detection</strong></p><ul><li>Pre-Requisite for Face Detection</li><li>Detect the Face</li></ul><p><strong>Section 04: Deep Learning with Tensorflow</strong></p><ul><li>Introduction to Deep Learning with Tensorflow</li><li>Model Building</li><li>Training the Mask Detector</li><li>Saving the Best Model</li><li>Basic Front End Design of App</li><li>File Upload Interface for App</li><li>App Prep</li><li>App Build and Testing</li><li>AWS Deployment</li><li>AWS Deployment Continued</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Aspiring data scientists.</li><li>Machine learning engineers.</li><li>Developers interested in machine learning app development.</li><li>Anyone interested in developing technology to fight the pandemic.</li><li>Professionals looking to upskill in the latest technology.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £40,000 to £80,000 per year.</li><li>Machine Learning Engineer: £55,000 to £90,000 per year.</li><li>Artificial Intelligence Developer: £40,000 to £80,000 per year.</li><li>Computer Vision Engineer: £40,000 to £80,000 per year.</li><li>Deep Learning Engineer: £55,000 to £90,000 per year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>

                        </div>

One Education

Hands on Machine Learning Project - Covid Mask Detector Course

<div>
						<div>
							<div>
							<section>
						<div>
							<div>
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				<div>
			<h2>Overview
</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning &amp; Data Science Bootcamp 2023 course can help you out.</span></p><p><span>This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing. </span></p><p><span>Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today!</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets.</span></p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.</span></p><p> </p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as</span></p><ul><li><span>Data Analyst </span></li><li><span>Data Scientist </span></li><li><span>Data Manager </span></li><li><span>Business Analyst</span></li></ul>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>18 sections • 98 lectures • 23:48:00 total length</p></div><div><div><div><div><div><div><div><p>•Welcome &amp; Course Overview6: 00:07:00</p></div><div><div><p>•Set-up the Environment for the Course (lecture 1): 00:09:00</p></div><div><div><p>•Set-up the Environment for the Course (lecture 2): 00:25:00</p></div><div><div><p>•Two other options to setup environment: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python data types Part 1: 00:21:00</p></div><div><div><p>•Python Data Types Part 2: 00:15:00</p></div><div><div><p>•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00</p></div><div><div><p>•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00</p></div><div><div><p>•Python Essentials Exercises Overview: 00:02:00</p></div><div><div><p>•Python Essentials Exercises Solutions: 00:22:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is Numpy? A brief introduction and installation instructions.: 00:03:00</p></div><div><div><p>•NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00</p></div><div><div><p>•NumPy Essentials - Indexing, slicing, broadcasting &amp; boolean masking: 00:26:00</p></div><div><div><p>•NumPy Essentials - Arithmetic Operations &amp; Universal Functions: 00:07:00</p></div><div><div><p>•NumPy Essentials Exercises Overview: 00:02:00</p></div><div><div><p>•NumPy Essentials Exercises Solutions: 00:25:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•What is pandas? A brief introduction and installation instructions.: 00:02:00</p></div><div><div><p>•Pandas Introduction: 00:02:00</p></div><div><div><p>•Pandas Essentials - Pandas Data Structures - Series: 00:20:00</p></div><div><div><p>•Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00</p></div><div><div><p>•Pandas Essentials - Handling Missing Data: 00:12:00</p></div><div><div><p>•Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00</p></div><div><div><p>•Pandas Essentials - Groupby: 00:10:00</p></div><div><div><p>•Pandas Essentials - Useful Methods and Operations: 00:26:00</p></div><div><div><p>•Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00</p></div><div><div><p>•Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00</p></div><div><div><p>•Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00</p></div><div><div><p>•Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Matplotlib Essentials (Part 1) - Basic Plotting &amp; Object Oriented Approach: 00:13:00</p></div><div><div><p>•Matplotlib Essentials (Part 2) - Basic Plotting &amp; Object Oriented Approach: 00:22:00</p></div><div><div><p>•Matplotlib Essentials (Part 3) - Basic Plotting &amp; Object Oriented Approach: 00:22:00</p></div><div><div><p>•Matplotlib Essentials - Exercises Overview: 00:06:00</p></div><div><div><p>•Matplotlib Essentials - Exercises Solutions: 00:21:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Seaborn - Introduction &amp; Installation: 00:04:00</p></div><div><div><p>•Seaborn - Distribution Plots: 00:25:00</p></div><div><div><p>•Seaborn - Categorical Plots (Part 1): 00:21:00</p></div><div><div><p>•Seaborn - Categorical Plots (Part 2): 00:16:00</p></div><div><div><p>•Seborn-Axis Grids: 00:25:00</p></div><div><div><p>•Seaborn - Matrix Plots: 00:13:00</p></div><div><div><p>•Seaborn - Regression Plots: 00:11:00</p></div><div><div><p>•Seaborn - Controlling Figure Aesthetics: 00:10:00</p></div><div><div><p>•Seaborn - Exercises Overview: 00:04:00</p></div><div><div><p>•Seaborn - Exercise Solutions: 00:19:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pandas Built-in Data Visualization: 00:34:00</p></div><div><div><p>•Pandas Data Visualization Exercises Overview: 00:03:00</p></div><div><div><p>•Panda Data Visualization Exercises Solutions: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting (Part 1): 00:19:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting (Part 2): 00:14:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting Exercises (Overview): 00:11:00</p></div><div><div><p>•Plotly &amp; Cufflinks - Interactive &amp; Geographical Plotting Exercises (Solutions): 00:17:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00</p></div><div><div><p>•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00</p></div><div><div><p>•Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to ML - What, Why and Types..: 00:15:00</p></div><div><div><p>•Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00</p></div><div><div><p>•scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00</p></div><div><div><p>•scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00</p></div><div><div><p>•Good to know! How to save and load your trained Machine Learning Model!: 00:01:00</p></div><div><div><p>•scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00</p></div><div><div><p>•scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00</p></div><div><div><p>•scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00</p></div><div><div><p>•scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00</p></div><div><div><p>•scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00</p></div><div><div><p>•scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: D-Tree &amp; Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests - Hands-on  (Part 1): 00:19:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00</p></div><div><div><p>•scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00</p></div><div><div><p>•scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00</p></div><div><div><p>•scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: K Means Clustering, Elbow method.: 00:11:00</p></div><div><div><p>•scikit-learn - K Means Clustering - Hands-on: 00:23:00</p></div><div><div><p>•scikit-learn - K Means Clustering (Project Overview): 00:07:00</p></div><div><div><p>•scikit-learn - K Means Clustering (Project Solutions): 00:22:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Principal Component Analysis (PCA): 00:09:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00</p></div><div><div><p>•scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Theory: Recommender Systems their Types and Importance: 00:06:00</p></div><div><div><p>•Python for Recommender Systems - Hands-on (Part 1): 00:18:00</p></div><div><div><p>•Python for Recommender Systems - - Hands-on (Part 2): 00:19:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Natural Language Processing (NLP) - (Theory Lecture): 00:13:00</p></div><div><div><p>•NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00</p></div><div><div><p>•NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00</p></div><div><div><p>•NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00</p></div><div><div><p>•NLTK - BoW, TF-IDF, Machine Learning, Training &amp; Evaluation, Naive Bayes : 00:13:00</p></div><div><div><p>•NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Online Course Marketing

Online Course Creation

Managed Learning Programme (MLP)

Trainers Course

Complete Machine Learning & Data Science Bootcamp 2023

Scala

Apache Spark

Big Data

Data Processing

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

Apache Spark with Scala - Hands-On with Big Data!

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				<p><span>Register on the Spatial Data Visualization and Machine Learning in Python today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.</span></p><p><span>The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.</span></p><p><span>Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion.</span></p><p><span>The Spatial Data Visualization and Machine Learning in Python is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.</span></p><p><span>The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!</span></p><h5><b>What You Get With The Spatial Data Visualization and Machine Learning in Python </b></h5><ul><li><span>Receive a e-certificate upon successful completion of the course</span></li><li><span>Get taught by experienced, professional instructors</span></li><li><span>Study at a time and pace that suits your learning style</span></li><li><span>Get instant feedback on assessments </span></li><li><span>24/7 help and advice via email or live chat</span></li><li><span>Get full tutor support on weekdays (Monday to Friday)</span></li></ul>					</div>
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			<h2>Course Design</h2>		</div>
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				<div id="course-home"><div><p><span>The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.</span></p><p><span>You are taught through a combination of</span></p><ul><li><span>Video lessons</span></li><li><span>Online study materials</span></li></ul></div></div>					</div>
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			<h2>Certification</h2>		</div>
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				<p><span>Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99.</span></p>					</div>
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			<h2>Who Is This Course For:</h2>		</div>
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				<p><span>The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.</span></p>					</div>
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			<h2>Requirements:</h2>		</div>
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				<p><span>The online training is open to all students and has no formal entry requirements. To study the Spatial Data Visualization and Machine Learning in Python, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.</span></p>					</div>
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				<h3>Course Content</h3>

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<table width="100%">				<tbody><tr>
					<td colspan="4"> Section 01: Introduction</td>
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					<td><i></i></td>
					<td>Introduction</td>
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					<td><span><i></i> 00:14:00</span></td>
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					<td colspan="4">Section 02: Setup and Installations</td>
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					<td>Python Installation</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>Installing Bokeh</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td colspan="4">Section 03: Data Preparation</td>
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					<td>Data Preparation</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>Creating a Bar Chart</td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td>Creating a Line Chart</td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td>Creating a Doughnut Chart</td>
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					<td><span><i></i> 00:22:00</span></td>
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					<td>Creating a Magnitude Plot</td>
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					<td><span><i></i> 00:31:00</span></td>
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					<td>Creating a Geo Map Plot</td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td colspan="4">Section 05: Machine Learning</td>
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					<td>Data Pre-processing</td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td>Building a Predictive Model</td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td>Building a Prediction Dataset</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td colspan="4">Section 06: Building the Dashboard</td>
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					<td>Adding predicted data to our plots - Part 1</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>Adding the Grid Plot</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>Installing Visual Studio Code</td>
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					<td><span><i></i> 00:01:00</span></td>
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					<td>Creating the Project and Virtual Environment</td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td>Building and Running the Server</td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td colspan="4">Section 08: Project Source Code</td>
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					<td>Project Source Code</td>
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					<td><span><i></i> 00:00:00</span></td>
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			<h2>Frequently Asked Questions</h2>
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                                    Are there any prerequisites for taking the course?
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                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
                                    </p>
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                                    Can I access the course at any time, or is there a set
                                    schedule?
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                                        You have the flexibility to access the course at any time
                                        that suits your schedule. Our courses are self-paced,
                                        allowing you to study at your own pace and convenience.
                                    </p>
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                                    How long will I have access to the course?
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                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
                                    </p>
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                                <span>
                                    Is there a certificate of completion provided after
                                    completing the course?
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                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
                                    </p>
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                                    Can I switch courses or get a refund if I'm not satisfied
                                    with the course?
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Janets

Spatial Data Visualization and Machine Learning in Python

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p>The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.</p><p></p><p></p><p>This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. </p><p></p>Prerequisites<p>Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.   Specifically:  </p><ul> <li>Experience using computers and the internet.</li> <li>Interest in use cases for AI applications and machine learning models. </li> <li>A willingness to learn through hands-on exp...</li><p></p></ul></div><div id="course-pills-2"><h3>1 - Fundamental AI Concepts</h3> <ul> <li>Understand machine learning</li> <li>Understand computer vision</li> <li>Understand natural language processing</li> <li>Understand document intelligence and knowledge mining</li> <li>Understand generative AI</li> <li>Challenges and risks with AI</li> <li>Understand Responsible AI</li> </ul> <h3>2 - Fundamentals of machine learning</h3> <ul> <li>What is machine learning?</li> <li>Types of machine learning</li> <li>Regression</li> <li>Binary classification</li> <li>Multiclass classification</li> <li>Clustering</li> <li>Deep learning</li> <li>Azure Machine Learning</li> </ul> <h3>3 - Fundamentals of Azure AI services</h3> <ul> <li>AI services on the Azure platform</li> <li>Create Azure AI service resources</li> <li>Use Azure AI services</li> <li>Understand authentication for Azure AI services</li> </ul> <h3>4 - Fundamentals of Computer Vision</h3> <ul> <li>Images and image processing</li> <li>Machine learning for computer vision</li> <li>Azure AI Vision</li> </ul> <h3>5 - Fundamentals of Facial Recognition</h3> <ul> <li>Understand Face analysis</li> <li>Get started with Face analysis on Azure</li> </ul> <h3>6 - Fundamentals of optical character recognition</h3> <ul> <li>Get started with Vision Studio on Azure</li> </ul> <h3>7 - Fundamentals of Text Analysis with the Language Service</h3> <ul> <li>Understand Text Analytics</li> <li>Get started with text analysis</li> </ul> <h3>8 - Fundamentals of question answering with the Language Service</h3> <ul> <li>Understand question answering</li> <li>Get started with the Language service and Azure Bot Service</li> </ul> <h3>9 - Fundamentals of conversational language understanding</h3> <ul> <li>Describe conversational language understanding</li> <li>Get started with conversational language understanding in Azure</li> </ul> <h3>10 - Fundamentals of Azure AI Speech</h3> <ul> <li>Understand speech recognition and synthesis</li> <li>Get started with speech on Azure</li> </ul> <h3>11 - Fundamentals of Azure AI Document Intelligence</h3> <ul> <li>Explore capabilities of document intelligence</li> <li>Get started with receipt analysis on Azure</li> </ul> <h3>12 - Fundamentals of Knowledge Mining with Azure Cognitive Search</h3> <ul> <li>What is Azure Cognitive Search?</li> <li>Identify elements of a search solution</li> <li>Use a skillset to define an enrichment pipeline</li> <li>Understand indexes</li> <li>Use an indexer to build an index</li> <li>Persist enriched data in a knowledge store</li> <li>Create an index in the Azure portal</li> <li>Query data in an Azure Cognitive Search index</li> </ul> <h3>13 - Fundamentals of Generative AI</h3> <ul> <li>What is generative AI?</li> <li>Large language models</li> <li>What is Azure OpenAI?</li> <li>What are copilots?</li> <li>Improve generative AI responses with prompt engineering</li> </ul> <h3>14 - Fundamentals of Azure OpenAI Service</h3> <ul> <li>What is generative AI</li> <li>Describe Azure OpenAI</li> <li>How to use Azure OpenAI</li> <li>Understand OpenAI's natural language capabilities</li> <li>Understand OpenAI code generation capabilities</li> <li>Understand OpenAI's image generation capabilities</li> <li>Describe Azure OpenAI's access and responsible AI policies</li> </ul> <h3>15 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

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

Machine Learning (ML)

Machine Learning for Apps Level 4

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.</p><p></p><p></p><p>AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language.</p><p></p>Prerequisites<p>Before attending this course, students must have:  </p><li>Knowledge of Microsoft Azure and ability to navigate the Azure portal  </li><li>Knowledge of either C# or Python  </li><li>Familiarity with JSON and REST programming semantics  <br />  Recommended course prerequisites  </li><li>AI-900T00: Microsoft Azure AI Fundamentals course<p></p></li></div><div id="course-pills-2"><h3>1 - Prepare to develop AI solutions on Azure</h3> <ul> <li>Define artificial intelligence</li> <li>Understand AI-related terms</li> <li>Understand considerations for AI Engineers</li> <li>Understand considerations for responsible AI</li> <li>Understand capabilities of Azure Machine Learning</li> <li>Understand capabilities of Azure AI Services</li> <li>Understand capabilities of the Azure Bot Service</li> <li>Understand capabilities of Azure Cognitive Search</li> </ul> <h3>2 - Create and consume Azure AI services</h3> <ul> <li>Provision an Azure AI services resource</li> <li>Identify endpoints and keys</li> <li>Use a REST API</li> <li>Use an SDK</li> </ul> <h3>3 - Secure Azure AI services</h3> <ul> <li>Consider authentication</li> <li>Implement network security</li> </ul> <h3>4 - Monitor Azure AI services</h3> <ul> <li>Monitor cost</li> <li>Create alerts</li> <li>View metrics</li> <li>Manage diagnostic logging</li> </ul> <h3>5 - Deploy Azure AI services in containers</h3> <ul> <li>Understand containers</li> <li>Use Azure AI services containers</li> </ul> <h3>6 - Analyze images</h3> <ul> <li>Provision an Azure AI Vision resource</li> <li>Analyze an image</li> <li>Generate a smart-cropped thumbnail</li> </ul> <h3>7 - Classify images</h3> <ul> <li>Provision Azure resources for Azure AI Custom Vision</li> <li>Understand image classification</li> <li>Train an image classifier</li> </ul> <h3>8 - Detect, analyze, and recognize faces</h3> <ul> <li>Identify options for face detection analysis and identification</li> <li>Understand considerations for face analysis</li> <li>Detect faces with the Azure AI Vision service</li> <li>Understand capabilities of the face service</li> <li>Compare and match detected faces</li> <li>Implement facial recognition</li> </ul> <h3>9 - Read Text in images and documents with the Azure AI Vision Service</h3> <ul> <li>Explore Azure AI Vision options for reading text</li> <li>Use the Read API</li> </ul> <h3>10 - Analyze video</h3> <ul> <li>Understand Azure Video Indexer capabilities</li> <li>Extract custom insights</li> <li>Use Video Analyzer widgets and APIs</li> </ul> <h3>11 - Analyze text with Azure AI Language</h3> <ul> <li>Provision an Azure AI Language resource</li> <li>Detect language</li> <li>Extract key phrases</li> <li>Analyze sentiment</li> <li>Extract entities</li> <li>Extract linked entities</li> </ul> <h3>12 - Build a question answering solution</h3> <ul> <li>Understand question answering</li> <li>Compare question answering to Azure AI Language understanding</li> <li>Create a knowledge base</li> <li>Implement multi-turn conversation</li> <li>Test and publish a knowledge base</li> <li>Use a knowledge base</li> <li>Improve question answering performance</li> </ul> <h3>13 - Build a conversational language understanding model</h3> <ul> <li>Understand prebuilt capabilities of the Azure AI Language service</li> <li>Understand resources for building a conversational language understanding model</li> <li>Define intents, utterances, and entities</li> <li>Use patterns to differentiate similar utterances</li> <li>Use pre-built entity components</li> <li>Train, test, publish, and review a conversational language understanding model</li> </ul> <h3>14 - Create a custom text classification solution</h3> <ul> <li>Understand types of classification projects</li> <li>Understand how to build text classification projects</li> </ul> <h3>15 - Create a custom named entity extraction solution</h3> <ul> <li>Understand custom named entity recognition</li> <li>Label your data</li> <li>Train and evaluate your model</li> </ul> <h3>16 - Translate text with Azure AI Translator service</h3> <ul> <li>Provision an Azure AI Translator resource</li> <li>Specify translation options</li> <li>Define custom translations</li> </ul> <h3>17 - Create speech-enabled apps with Azure AI services</h3> <ul> <li>Provision an Azure resource for speech</li> <li>Use the Azure AI Speech to Text API</li> <li>Use the text to speech API</li> <li>Configure audio format and voices</li> <li>Use Speech Synthesis Markup Language</li> </ul> <h3>18 - Translate speech with the Azure AI Speech service</h3> <ul> <li>Provision an Azure resource for speech translation</li> <li>Translate speech to text</li> <li>Synthesize translations</li> </ul> <h3>19 - Create an Azure AI Search solution</h3> <ul> <li>Manage capacity</li> <li>Understand search components</li> <li>Understand the indexing process</li> <li>Search an index</li> <li>Apply filtering and sorting</li> <li>Enhance the index</li> </ul> <h3>20 - Create a custom skill for Azure AI Search</h3> <ul> <li>Create a custom skill</li> <li>Add a custom skill to a skillset</li> </ul> <h3>21 - Create a knowledge store with Azure AI Search</h3> <ul> <li>Define projections</li> <li>Define a knowledge store</li> </ul> <h3>22 - Plan an Azure AI Document Intelligence solution</h3> <ul> <li>Understand AI Document Intelligence</li> <li>Plan Azure AI Document Intelligence resources</li> <li>Choose a model type</li> </ul> <h3>23 - Use prebuilt Azure AI Document Intelligence models</h3> <ul> <li>Understand prebuilt models</li> <li>Use the General Document, Read, and Layout models</li> <li>Use financial, ID, and tax models</li> </ul> <h3>24 - Extract data from forms with Azure Document Intelligence</h3> <ul> <li>What is Azure Document Intelligence?</li> <li>Get started with Azure Document Intelligence</li> <li>Train custom models</li> <li>Use Azure Document Intelligence models</li> <li>Use the Azure Document Intelligence Studio</li> </ul> <h3>25 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>26 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>27 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>28 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>29 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>30 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul> <h3>31 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div>

Microsoft AZ-104

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

<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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