Name: Learn AI with Python Course
Price: 12 GBP
Availability: InStock

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Dive deep into the world of ChatGPT with our ChatGPT Masterclass. From basic functionalities to advanced applications across various domains, this course equips you with the knowledge to leverage ChatGPT effectively. Enhance your professional skills, academic pursuits, and personal projects by mastering ChatGPT today.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;">Understand the mechanics behind ChatGPT&rsquo;s responses.</li><li fr-original-style="" style="box-sizing: border-box;">Create precise and effective prompts for ChatGPT.</li><li fr-original-style="" style="box-sizing: border-box;">Utilize ChatGPT for creative and technical writing.</li><li fr-original-style="" style="box-sizing: border-box;">Leverage ChatGPT for educational support and learning.</li><li fr-original-style="" style="box-sizing: border-box;">Integrate ChatGPT with Microsoft Excel for data management.</li><li fr-original-style="" style="box-sizing: border-box;">Employ ChatGPT to achieve professional excellence.</li><li fr-original-style="" style="box-sizing: border-box;">Explore practical and innovative ChatGPT prompts.</li><li fr-original-style="" style="box-sizing: border-box;">Implement ChatGPT strategies in social media marketing.</li><li fr-original-style="" style="box-sizing: border-box;">Understand the capabilities of ChatGPT Plus and New Bing.</li></ul><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;">Course Curriculum</strong></p><h4 id="isPasted" fr-original-style="" style="box-sizing: border-box;">Module 01: Getting Started with ChatGPT</h4><ul 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;">Introduction to ChatGPT:</strong> Basics of how to interact with and utilize ChatGPT effectively.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 02: Understanding ChatGPT</h4><ul 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;">How ChatGPT Works:</strong> Insights into the AI and machine learning principles powering ChatGPT.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 03: Crafting Effective ChatGPT Prompts: A Guide</h4><ul 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;">Prompt Engineering:</strong> Techniques for developing precise prompts that generate desired outcomes.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 04: Writing with ChatGPT</h4><ul 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;">Creative and Technical Writing:</strong> Using ChatGPT to assist with various writing tasks and projects.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 05: ChatGPT for Students</h4><ul 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;">Educational Applications:</strong> How students can use ChatGPT for studying, research, and homework assistance.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 06: ChatGPT for MS Excel</h4><ul 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;">Excel Integration:</strong> Harnessing ChatGPT for automating tasks and analyzing data in Microsoft Excel.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 07: ChatGPT for Professional Excellence</h4><ul 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;">Career Development:</strong> Applying ChatGPT in professional settings for communication, problem-solving, and innovation.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 08: Useful ChatGPT Prompts</h4><ul 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;">Practical Prompts:</strong> A collection of effective ChatGPT prompts for various uses.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 09: Social Media Marketing with ChatGPT</h4><ul 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;">Marketing Strategies:</strong> Leveraging ChatGPT for content creation, customer engagement, and campaign management.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 10: ChatGPT Plus and New Bing</h4><ul 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;">Advanced Features:</strong> Exploring the enhanced capabilities and applications of ChatGPT Plus and New Bing.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 11: ChatGPT in Personal Life</h4><ul 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;">Everyday Uses:</strong> Practical ways to incorporate ChatGPT into daily personal tasks and activities.</li></ul><h4 fr-original-style="" style="box-sizing: border-box;">Module 12: The Future with ChatGPT</h4><ul 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;">Looking Ahead:</strong> Discussing potential future developments in ChatGPT technology and its implications for various sectors.</li></ul>

RapidEDX

AI (Artificial Intelligence)

ChatGPT

ChatGPT Masterclass

General Admission

<p>Jumpstart your Data Analytics Career with Tableau by solving Realistic Analytics Projects. Learn Data Visualisation!</p><p>Tableau is a powerful tool for analysing data and uncovering insights. It provides an easy-to-use interface that allows users to quickly create visualisations and dashboards to explore data. Tableau is a great tool for data exploration and discovery, as it allows users to quickly analyse large datasets and uncover patterns and trends.</p><p>In this course, You will construct six distinct dashboards in this course that each represent a<strong> real-world </strong>issue. You'll discover how to connect to a range of data sources, including <strong>cloud servers and Excel files</strong>. Additionally, you'll discover how to create interactive dashboards and upload them to Tableau Online to demonstrate your abilities to potential companies and link these in your resume.</p><p><strong>What you'll learn</strong></p><ul><li>You'll comprehend the inner workings of the data analytics pipeline by the end of this course, from merging, filtering, and extracting data to creating dashboards.</li><li>Create five interactive dashboards and display them online to build a strong work history for your resume.</li><li>Connect to several data sources, including servers, Google Sheets, and Excel!</li><li>Be able to use maps, line charts, donut charts, and bar charts to create in-depth analyses.</li><li>Gain a thorough knowledge of how Tableau's calculations operate.</li><li>Learning how to join data sources in Tableau and the many sorts of joins</li><li>Engage in set-based work and level-of-detail calculations</li><li>Recognise how Tableau's Groups and Hierarchies Operate.</li></ul><p>When you finish this course, you'll be an expert user of <strong>Tableau </strong> who can apply all you've learned to advance your career in data and analytics.</p><p><strong>Key Benefits</strong></p><ul><li>Learning materials of the Design course contain engaging voiceover and visual elements for your comfort.</li><li>Get 24/7 access to all content for a full year.</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li></ul><p>This course is designed with the following modules:</p><p><strong>***Introduction to Data Analytics with Tableau***</strong></p><ul><li>Module 01:Introduction to the Course</li><li>Module 02:Project 1: Discount Mart (Sales and Profit Analytics)</li><li>Module 03:Project 2: Green Destinations (HR Analytics)</li><li>Module 04:Project 3: Superstore (Sales Agent Tracker)</li><li>Module 05:Northwind Trade (Shipping Analytics)</li><li>Module 06:Project 5: Tesla (Stock Price Analytics)</li><li>Module 07:Bonus: Introduction to Database Concepts</li><li>Module 08:Tableau Stories</li></ul><p><strong>Course Assessment </strong></p><p>To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%.</p><p>When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme.</p><p><strong>Certificate of Achievement</strong></p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After successfully completing the Diploma in Data Analytics with Tableau course, learners will be able to order an endorsed certificate as proof of their achievement. Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying</p><ul><li>Within the UK: £119</li><li>International: £119 + £10 (postal charge) = £129</li><li>CPD Accredited Certification from One Education</li></ul><p>After successfully completing this Diploma in Data Analytics with Tableau course, you will qualify for the CPD accredited certificate from One Education. Certification is available in both PDF &amp; hardcopy format, which can be received by paying -</p><ul><li>PDF Certificate: £9</li><li>Hardcopy Certificate (within the UK): £15</li><li>Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>This course is for anyone who wants to kickstart their career in Data Analytics.</li><li>This course is for anyone who wants to learn more about Tableau</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
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                        <div id="requirements">
                            <p>This Diploma in Data Analytics with Tableau course is open to all, with no formal entry requirements.</p>
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        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After successfully completing this course, you can develop your confidence and pursue possibilities such as-</p><ul><li>Data analytics manager</li><li>Financial analyst</li><li>Scientist</li><li>Machine learning engineer</li></ul><p>The salary range for data analysts in the UK is £23,000 to £62,000 per year.</p>
                        </div>

One Education

Diploma in Data Analytics with Tableau Course

<p>In a world where artificial intelligence is becoming increasingly important, learning the skills needed to build your own neural networks is more crucial than ever. Imagine having the ability to create your own neural network and train it to perform complex tasks. With our Project on Deep Learning - Artificial Neural Network course, you can do just that.</p><p>In this course, you'll learn how to build your own artificial neural network from scratch, using Python. From installing the necessary packages to data preprocessing and encoding, you'll discover how to prepare your data for neural network training. You'll also gain a deeper understanding of the steps involved in building your own artificial neural network and learn how to make predictions using your model.</p><p>So if you're ready to unlock the power of deep learning and take your skills to the next level, enrol in our Project on Deep Learning - Artificial Neural Network course today and start building your own neural networks.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a solid understanding of artificial neural networks and their applications in deep learning.</li><li>Learn how to install the necessary packages and preprocess data for neural network training.</li><li>Discover how to encode data and build your own artificial neural network using Python.</li><li>Understand the steps involved in making predictions using your neural network model.</li><li>Learn how to deal with imbalanced data in your neural network training.</li></ul><p>The Project on Deep Learning - Artificial Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network and perform complex tasks using deep learning. You'll learn how to install the necessary packages, preprocess data, and encode data for neural network training. You'll also gain a deeper understanding of artificial neural networks and learn how to build your own model using Python. By the end of the course, you'll be able to make predictions using your neural network model and understand how to deal with imbalanced data in your training.</p><p><strong>â±â± Project on Deep Learning - Artificial Neural Network</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02:</strong> ANN Installation<strong><br /></strong></li><li><strong>Section 03: </strong>Data Preprocessing<strong><br /></strong></li><li><strong>Section 04: </strong>Data Encoding<strong><br /></strong></li><li><strong>Section 05:</strong> Steps to Build ANN<strong><br /></strong></li><li><strong>Section 06:</strong> Predictions and Imbalance-Learn</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.<br /><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>
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                        <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>Data analysts who want to expand their skills in deep learning and artificial neural networks.</li><li>Programmers who want to learn how to build their own neural network models for advanced tasks.</li><li>Entrepreneurs who want to develop their own deep learning-based applications.</li><li>Students who want to enhance their skills in deep learning and prepare for a career in the field.</li><li>Anyone who wants to explore the world of artificial neural networks and deep learning projects.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £24,000 - £45,000</li><li>Machine Learning Engineer: £28,000 - £65,000</li><li>Deep Learning Engineer: £30,000 - £75,000</li><li>Technical Lead: £40,000 - £90,000</li><li>Chief Technology Officer: £90,000 - £250,000</li></ul>
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        <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>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Build Structures in Portuguese Course

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to<br />Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras<br />Apply modern solutions to a wide range of applications such as object detection and video analysis<br />Run your models on mobile devices and web pages and improve their performance.<br />Create your own neural networks from scratch<br />Classify images with modern architectures including Inception and ResNet<br />Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net<br />Tackle problems faced when developing self-driving cars and facial emotion recognition systems<br />Boost your application's performance with transfer learning, GANs, and domain adaptation<br />Use recurrent neural networks (RNNs) for video analysis<br />Optimize and deploy your networks on mobile devices and in the browser</p><p>Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.<br />This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.</p></div><div id="course-pills-2"><h4>Computer Vision and Neural Networks </h4><ul><li>Computer Vision and Neural Networks </li><li>Technical requirements </li><li>Computer vision in the wild </li><li>A brief history of computer vision </li><li>Getting started with neural networks </li></ul><h4>TensorFlow Basics and Training a Model </h4><ul><li>TensorFlow Basics and Training a Model </li><li>Technical requirements </li><li>Getting started with TensorFlow 2 and Keras </li><li>TensorFlow 2 and Keras in detail </li><li>The TensorFlow ecosystem </li></ul><h4>Modern Neural Networks </h4><ul><li>Modern Neural Networks </li><li>Technical requirements </li><li>Discovering convolutional neural networks </li><li>Refining the training process </li></ul><h4>Influential Classification Tools </h4><ul><li>Influential Classification Tools </li><li>Technical requirements </li><li>Understanding advanced CNN architectures </li><li>Leveraging transfer learning </li></ul><h4>Object Detection Models </h4><ul><li>Object Detection Models </li><li>Technical requirements </li><li>Introducing object detection </li><li>A fast object detection algorithm ? YOLO </li><li>Faster R-CNN ? a powerful object detection model </li></ul><h4>Enhancing and Segmenting Images </h4><ul><li>Enhancing and Segmenting Images </li><li>Technical requirements </li><li>Transforming images with encoders-decoders </li><li>Understanding semantic segmentation </li></ul><h4>Training on Complex and Scarce Datasets </h4><ul><li>Training on Complex and Scarce Datasets </li><li>Technical requirements </li><li>Efficient data serving </li><li>How to deal with data scarcity </li></ul><h4>Video and Recurrent Neural Networks </h4><ul><li>Video and Recurrent Neural Networks </li><li>Technical requirements </li><li>Introducing RNNs </li><li>Classifying videos </li></ul><h4>Optimizing Models and Deploying on Mobile Devices </h4><ul><li>Optimizing Models and Deploying on Mobile Devices </li><li>Technical requirements </li><li>Optimizing computational and disk footprints </li><li>On-device machine learning </li><li>Example app ? recognizing facial expressions </li></ul></div>

Nexus Human

Computing Skills

Computer Vision

TensorFlow

Trainers Course

Deep Learning

Hands-On Computervision with TensorFlow 2 (TTML6900)

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			<h2>Overview
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					<p><span>This comprehensive course on <strong>Microsoft Azure Cloud Concepts </strong></span><span>will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <strong>Microsoft Azure Cloud Concepts</strong></span><b> </b><span>comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.   </span></p><p><span>So enrol in this course today to fast-track your career ladder.</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>There is no experience or previous qualifications required for enrolment on this <strong>Microsoft Azure Cloud Concepts</strong></span><span>. It is available to all students, of all academic backgrounds.</span></p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our <strong>Microsoft Azure Cloud Concepts</strong></span><span> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</span></p><ul><li><span>Open doors of opportunities</span></li><li><span>Increase your adaptability</span></li><li><span>Keep you relevant</span></li><li><span>Boost confidence</span></li></ul><p><span>And much more!</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>1 sections • 29 lectures • 03:33:00 total length</p></div><div><div><div><div><div><div><div><p>•Unit 01: What will you learn and Cloud Concepts: 00:18:00</p></div><div><div><p>•Unit 02: Core Azure architectural components: 00:18:00</p></div><div><div><p>•Unit 03: LAB Create a Windows and Linux VM Computer: 00:10:00</p></div><div><div><p>•Unit 04: LAB Container creation: 00:04:00</p></div><div><div><p>•Unit 05: Storage with Azure: 00:04:00</p></div><div><div><p>•Unit 06: LAB Create a storage account: 00:07:00</p></div><div><div><p>•Unit 07: Network concepts: 00:03:00</p></div><div><div><p>•Unit 08: Lab Network Peering: 00:16:00</p></div><div><div><p>•Unit 09: Lab scale set: 00:11:00</p></div><div><div><p>•Unit 10: Marketspace and Serverless: 00:07:00</p></div><div><div><p>•Unit 11: Event HUB and Logic APPS: 00:07:00</p></div><div><div><p>•Unit 12: DevOps Overview: 00:04:00</p></div><div><div><p>•Unit 13: Azure Databases Overview: 00:04:00</p></div><div><div><p>•Unit 14: Lab SQL: 00:08:00</p></div><div><div><p>•Unit 15: What are AI and machine learning: 00:10:00</p></div><div><div><p>•Unit 16: Powershell and CLI: 00:09:00</p></div><div><div><p>•Unit 17: Azure Advisor: 00:05:00</p></div><div><div><p>•Unit 18: Review Core Azure: 00:04:00</p></div><div><div><p>•Unit 19: Azure security compliance and trust: 00:03:00</p></div><div><div><p>•Unit 20: Lab DDOS and NSGs: 00:07:00</p></div><div><div><p>•Unit 21: Authentication and Authorisation: 00:07:00</p></div><div><div><p>•Unit 22: Azure security centre: 00:06:00</p></div><div><div><p>•Unit 23: LAB Azure key vault and AIP overview: 00:06:00</p></div><div><div><p>•Unit 24: Azure Advanced Threat Protection (Azure ATP): 00:06:00</p></div><div><div><p>•Unit 25: Azure monitoring: 00:05:00</p></div><div><div><p>•Unit 26: Manage Azure Governance: 00:07:00</p></div><div><div><p>•Unit 27: Azure privacy and compliance: 00:04:00</p></div><div><div><p>•Unit 28: Summary: 00:03:00</p></div><div><div><p>•Unit 29: Azure Pricing and support: 00:10:00</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>

Apex Learning

Microsoft AZ-900

AWS Certified Cloud Practitioner

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Microsoft Azure Cloud Concepts

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><b data-stringify-type="bold" fr-original-style=" color: rgb(209, 210, 211);" style="color: rgb(209, 210, 211); box-sizing: border-box;" id="isPasted"><span fr-original-style="color: rgb(0, 0, 0);" style="color: rgb(0, 0, 0); box-sizing: border-box;">Welcome to IT Security for IT Professionals. In this course, you&#39;ll learn:</span></b></p><ol style="color: rgb(209, 213, 219); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(209, 213, 219);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Introduction to IT Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Overview of IT security concepts and principles</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding the threat landscape and the importance of cybersecurity</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Historical perspectives on major security incidents and their impact on IT</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Security Policies and Procedures:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Developing and implementing effective security policies</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding compliance requirements and industry standards</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Incident response planning and execution</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Security awareness training for organizations</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Network Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Securing network infrastructure and devices</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Intrusion detection and prevention systems</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Virtual Private Networks (VPNs) and secure communication</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Firewalls and network segmentation</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Operating System Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Hardening operating systems for security</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">User access controls and permissions</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Patch management and software updates</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Securing system logs and monitoring</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Cryptography and Encryption:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Basics of cryptography and encryption algorithms</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Implementing secure communication using encryption</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Public and private key infrastructure</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cryptographic protocols and best practices</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Web Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Securing web applications and websites</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Common web vulnerabilities and exploits</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Web application firewalls</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Secure coding practices</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Wireless Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Securing wireless networks</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Authentication and encryption for Wi-Fi</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Wireless intrusion detection and prevention</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Mobile device security</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Emerging Trends in IT Security:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cloud security and virtualization</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Internet of Things (IoT) security</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Artificial intelligence and machine learning in cybersecurity</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Blockchain and its implications for security</li></ul></li></ol><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;"></p>

John Academy

Cryptography

Explore the world of cybersecurity with our "IT Security for IT Professionals" course. Gain expertise in fundamental areas such as network security, cryptography, web security, and emerging trends. Equip yourself with the skills needed to protect against evolving cyber threats. Enroll now for a comprehensive journey into IT security and fortify your role as a vigilant IT professional.

IT Security for IT Professionals

<strong>Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources</strong>
        <p>Cloud computing is one of the biggest technology revolutions in the IT industry, spreading at the speed of light all over the world. A growing number of business companies and organizations are looking for ways to migrate their applications into the cloud or build new cloud-native applications from scratch on top of cloud infrastructure. The demand for more skilled people in the area of cloud computing is increasing every day across multiple industries. Companies are looking for cloud experts!

Becoming a cloud expert - If you are looking to become a cloud expert, then this training program is designed to help you build your knowledge of, and experience with, cloud computing while using the Azure cloud platform. The training program is divided into levels.

Level 2  - All about monitoring.
In level 1, we learned to build an end-to-end cloud solution on the Microsoft Azure platform. Now is the time to learn how to monitor our IaaS solution effectively. In level 2, we will learn how to monitor and analyze the performance and health of our Azure resources, applications, and the Azure platform and services. One thing for sure, it is going to be an exciting journey!

All the codes and supporting files for this course are available at - https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-2</p>
        <h2>What You Will Learn</h2><p>Understand operational and business requirements for monitoring the performance and health of a cloud solution<br>Understand the different types of telemetry data (metrics and logs) that can be collected<br>Build your own demo system in Azure (using Linux, Apache, MySQL, and PHP) &nbsp;&nbsp;<br>Set up alert rules and action groups to create an automation layer<br>Simulate traffic load on the demo system<br>Monitor the status of the Azure platform and services<br>Monitor your Azure resources (such as VMs, storage accounts, VNet, interfaces, NSGs, and web apps)<br>Analyze performance metrics in multiple layers (Azure hosting infrastructures, guest OSes, applications)<br>Analyze logs using the kusto query language in log analytics<br>Manage the life cycle of Azure alerts</p>
        <h2>Audience</h2><p>The course is designed for Cloud administrators, IT and cloud experts and DevOps engineers.</p>
        <h2>Approach</h2><p>This complete course, packed with step-by-step instructions, working examples, and helpful advice, is clearly divided into small chunks to help you understand each part individually and learn at your own pace.</p>
        <h2>Key Features</h2><p>A comprehensive overview of the Microsoft Azure platform * A unique balance between theoretical topics and practical demonstrations * The second level of Becoming a Cloud Expert training program</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/becoming-a-cloud-expert---microsoft-azure-iaas---level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome!</p><p style='margin: 0'>Getting Started: Welcome!</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Getting Started: Course Objectives and Structure</p></td></tr><tr><td style='padding: 15px;'><p>3. Why?</p><p style='margin: 0'>Getting Started: Why?</p></td></tr><tr><td style='padding: 15px;'><p>4. What?</p><p style='margin: 0'>Getting Started: What?</p></td></tr><tr><td style='padding: 15px;'><p>5. How?</p><p style='margin: 0'>Getting Started: How?</p></td></tr></tbody></table><br><h4>2. Telemetry Data Collection - Types and Sources</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Sources - Collection from Multiple Layers</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Sources - Collection from Multiple Layers</p></td></tr><tr><td style='padding: 15px;'><p>3. Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Metrics and Logs</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Type #1 - Azure Subscription-Level Activity Log</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #1 - Azure Subscription-Level Activity Log</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Type #2 - Azure Resources Diagnostics Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #2 - Azure Resources Diagnostics Logs</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Type #3 - Azure Resources Metrics</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #3 - Azure Resources Metrics</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Type #4 - Guest OS Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #4 - Guest OS Metrics and Logs</p></td></tr><tr><td style='padding: 15px;'><p>8. Data Type #5 - Applications Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #5 - Applications Metrics and Logs</p></td></tr></tbody></table><br><h4>3. Preparing our Demo System "Playground"</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Preparing our Demo System "Playground": Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Step #1 - Back-End MySQL DB Server</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #1 - Back-End MySQL DB Server</p></td></tr><tr><td style='padding: 15px;'><p>3. Step #2 - Front-End Apache Web Server</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #2 - Front-End Apache Web Server</p></td></tr><tr><td style='padding: 15px;'><p>4. Step #3 - Log Analytics Workspace</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #3 - Log Analytics Workspace</p></td></tr><tr><td style='padding: 15px;'><p>5. Step #4 - Diagnostics Settings</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #4 - Diagnostics Settings</p></td></tr><tr><td style='padding: 15px;'><p>6. Step #5 - Azure Monitor for VMs</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #5 - Azure Monitor for VMs</p></td></tr><tr><td style='padding: 15px;'><p>7. Step #6 - Application Insights</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #6 - Application Insights</p></td></tr></tbody></table><br><h4>4. Effective Monitoring is all about Automation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Effective Monitoring is all about Automation: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. The Concept of Alerts and Actions</p><p style='margin: 0'>Effective Monitoring is all about Automation: The Concept of Alerts and Actions</p></td></tr><tr><td style='padding: 15px;'><p>3. How to Create an Alert Rule</p><p style='margin: 0'>Effective Monitoring is all about Automation: How to Create an Alert Rule</p></td></tr><tr><td style='padding: 15px;'><p>4. Configure Our Demo Rules and Actions</p><p style='margin: 0'>Effective Monitoring is all about Automation: Configure Our Demo Rules and Actions</p></td></tr><tr><td style='padding: 15px;'><p>5. Simulating a Load on the Web Server</p><p style='margin: 0'>Effective Monitoring is all about Automation: Simulating a Load on the Web Server</p></td></tr><tr><td style='padding: 15px;'><p>6. Managing Alerts</p><p style='margin: 0'>Effective Monitoring is all about Automation: Managing Alerts</p></td></tr></tbody></table><br><h4>5. Monitoring the Azure Platform</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Monitoring the Azure Platform: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Azure Global Status</p><p style='margin: 0'>Monitoring the Azure Platform: Azure Global Status</p></td></tr><tr><td style='padding: 15px;'><p>3. Personalised Service Health</p><p style='margin: 0'>Monitoring the Azure Platform: Personalised Service Health</p></td></tr></tbody></table><br><h4>6. Monitoring our Azure Solution</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Monitoring our Azure Solution: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Review the Status, Health and Activity Logs</p><p style='margin: 0'>Monitoring our Azure Solution: Review the Status, Health and Activity Logs</p></td></tr><tr><td style='padding: 15px;'><p>3. Metrics Explorer</p><p style='margin: 0'>Monitoring our Azure Solution: Metrics Explorer</p></td></tr><tr><td style='padding: 15px;'><p>4. Log Analytics</p><p style='margin: 0'>Monitoring our Azure Solution: Log Analytics</p></td></tr><tr><td style='padding: 15px;'><p>5. Azure Monitor for VMs</p><p style='margin: 0'>Monitoring our Azure Solution: Azure Monitor for VMs</p></td></tr><tr><td style='padding: 15px;'><p>6. Azure Advisor</p><p style='margin: 0'>Monitoring our Azure Solution: Azure Advisor</p></td></tr><tr><td style='padding: 15px;'><p>7. Application Insights</p><p style='margin: 0'>Monitoring our Azure Solution: Application Insights</p></td></tr></tbody></table><br><h4>7. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. A Quick Recap</p><p style='margin: 0'>Course Summary: A Quick Recap</p></td></tr></tbody></table>

Packt

Cloud Computing

Linux

Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 2

<p>In today's technology-driven world, a <strong>Diploma in Python Programming</strong> has become indispensable. Python, renowned for its simplicity and versatility, has emerged as a cornerstone in various industries, including Environmental Management. This course, spanning from beginner to expert levels, offers a comprehensive understanding of Python's fundamentals and advanced concepts. </p><p>With modules covering basic data types, control flow, functions, scripting, and libraries like NumPy and Pandas, participants gain proficiency in developing efficient solutions for data analysis and management tasks. <strong>The demand for Python skills is soaring</strong>, <strong>particularly in the UK</strong>, where job opportunities abound, <strong>with salaries averaging £45,000 annually</strong>. </p><p>Enrolling in this <strong>Diploma in Python Programming</strong> course not only opens doors to lucrative career prospects but also equips individuals with the tools to excel in Environmental Management through data-driven insights and automation. Enrich your skill set and stay ahead of the curve by enrolling in this <strong>Diploma in Python Programming</strong><strong>essential</strong> course today.</p><p><strong>Key Features:</strong></p><ul><li>CPD Certified</li><li>Instant e-certificate and hard copy dispatch by next working day</li><li>Fully online, interactive course with audio voiceover</li><li>Developed by qualified professionals in the field</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><strong>Course Curriculum<strong>:</strong></strong><p><strong>Python Programming: Beginner To Expert</strong></p><ul><li><strong>Module 01:</strong> Introduction to Python Programming from A-Z</li><li><strong>Module 02:</strong> Getting Familiar with Python</li><li><strong>Module 03:</strong> Basic Data Types</li><li><strong>Module 04:</strong> Python Operators</li><li><strong>Module 05:</strong> Advanced Data Types</li><li><strong>Module 06:</strong> Control Flow Part 1</li><li><strong>Module 07:</strong> Control Flow Part 2</li><li><strong>Module 08:</strong> Python Functions</li><li><strong>Module 09:</strong> User Input and Error Handling</li><li><strong>Module 10:</strong> Python Advanced Functions</li><li><strong>Module 11:</strong> Python Scripting and Libraries</li><li><strong>Module 12:</strong> NumPy</li><li><strong>Module 13:</strong> Pandas</li><li><strong>Module 14:</strong> Introduction to OOP</li><li><strong>Module 15:</strong> Advanced OOP</li><li><strong>Module 16:</strong> Starting a Career in Python</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Master Python syntax and fundamentals proficiently.</li><li>Apply advanced data types and operators effectively.</li><li>Develop robust control flow structures for efficient programming.</li><li>Utilize Python functions and error handling techniques adeptly.</li><li>Harness the power of object-oriented programming for scalable applications.</li><li>Explore career opportunities and kickstart your journey into the Python industry.</li></ul><p><strong>Accreditation</strong></p><p>All of our courses are fully accredited, including this Diploma in Python Programming Course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in Python Programming.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your Diploma in Python Programming Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our Diploma in Python Programming Course certification has no expiry dates, although we do recommend that you renew them every 12 months.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Aspiring programmers seeking a solid foundation in Python.</li><li>Professionals aiming to upgrade their coding skills.</li><li>Students pursuing careers in software development.</li><li>Individuals interested in data analysis and manipulation using Python.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <ul><li>Python Developer - £30K to £60K/year.</li><li>Data Analyst - £25K to £45K/year.</li><li>Software Engineer - £35K to £70K/year.</li><li>Machine Learning Engineer - £40K to £80K/year.</li><li>Data Scientist - £40K to £80K/year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Training Express

Diploma in Python Programming Course

<p>The world of Artificial Intelligence (AI) is rapidly expanding, with new breakthroughs and applications emerging every day. If you're looking to stay ahead of the curve, then look no further than our comprehensive course, "Learn AI with Python." Through a combination of engaging modules and real-world case studies, our course covers everything from the basics of AI to advanced techniques for detecting patterns and natural language processing.</p><p>With a particular focus on Python programming language, learners will gain insights into Class Imbalance and Grid Search, Adaboost Regressor, Affinity Propagation Model, Gaussian Mixture Model, Logic Programming, Heuristic Search and more. Whether you're a beginner or an experienced programmer, our course will take you on a journey through the fascinating and ever-changing world of AI.</p><p>By the end of the course, learners will be equipped with the knowledge and skills to create AI models and programs that can solve real-world problems. With our expert guidance and in-depth analysis, you'll gain the knowledge and confidence to make informed decisions and navigate the complex and dynamic world of AI. Don't miss out on this opportunity to enhance your skills and advance your career - enroll in "Learn AI with Python" today and join the ranks of successful AI programmers around the world.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Develop an understanding of the principles and practices of Artificial Intelligence (AI)</li><li>Learn effective strategies for detecting patterns and natural language processing</li><li>Develop analytical skills for creating AI models and programs</li><li>Understand Python programming language and its applications in AI</li><li>Be able to make informed decisions and navigate the complex and dynamic world of AI</li></ul><p>The "Learn AI with Python" course is designed to provide a comprehensive understanding of the principles and practices that underpin successful AI programming. Through engaging modules and real-world case studies, learners will gain insights into the basics of AI, advanced techniques for detecting patterns and natural language processing, and effective strategies for creating AI models and programs using Python programming language.</p><p>By the end of the course, learners will be equipped with the knowledge and skills to make informed decisions and navigate the complex and dynamic world of AI. Whether you're a beginner or an experienced programmer, this course is a must-have for anyone interested in the world of AI.</p><p><strong>â±â± Learn AI with Python</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02: </strong>Class Imbalance and Grid Search<strong><br /></strong></li><li><strong>Section 03: </strong>Adaboost Regressor<strong><br /></strong></li><li><strong>Section 04: </strong>Detecting patterns with Unsupervised Learning<strong><br /></strong></li><li><strong>Section 05: </strong>Affinity Propagation Model<strong><br /></strong></li><li><strong>Section 06: </strong>Clustering Quality<strong><br /></strong></li><li><strong>Section 07: </strong>Gaussian Mixture Model<strong><br /></strong></li><li><strong>Section 08: </strong>Classifiers<strong><br /></strong></li><li><strong>Section 09: </strong>Logic Programming<strong><br /></strong></li><li><strong>Section 10: </strong>Heuristic Search<strong><br /></strong></li><li><strong>Section 11: </strong>Natural Language Processing</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>
        
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                                        10 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                        <div id="who_is_this_course_for">
                            <ul><li>Programmers looking to enhance their AI skills</li><li>Business professionals interested in AI applications</li><li>Computer science students interested in AI</li><li>Entrepreneurs looking to incorporate AI into their products or services</li><li>Anyone interested in gaining a comprehensive understanding of AI and its applications</li></ul>
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        <div>Requirements</div>
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                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
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        <div>Career path</div>
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                            <ul><li>AI Programmer: £30,000 - £70,000 per year</li><li>Data Scientist: £30,000 - £80,000 per year</li><li>Machine Learning Engineer: £35,000 - £90,000 per year</li><li>AI Researcher: £40,000 - £100,000 per year</li><li>Software Developer: £25,000 - £70,000 per year</li></ul>
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        <div>Certificates</div>
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                                    <h4>Certificate of completion</h4>
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            Digital certificate -
            £9
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            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
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            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Learn AI with Python Course

<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>

Azure Machine Learning

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

Data Science

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

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>

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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