Name: Get Up and Running with Python Course
Price: 12 GBP
Availability: InStock

Packt

Cloud Computing

AWS Certified Cloud Practitioner

This comprehensive course on AWS Certified Cloud Practitioner (CLF-C01) empowers you to fast-track your IT career. Gain in-depth knowledge of cloud computing, AWS services, and architectural concepts. With hands-on labs, quizzes, and real practice exams, you will confidently build cost-effective, fault-tolerant IT solutions on the AWS Cloud.

AWS Certified Cloud Practitioner (CLF-C02) - Ultimate Exam Training

General Admission

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>Individuals planning to deploy applications and create application environments on Google Cloud.<br />Developers, systems operations professionals, and solution architects getting started with Google Cloud.<br />Executives and business decision makers evaluating the potential of Google Cloud to address their business needs.</p>Overview<p>Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services.<br />Describe ways in which customers have used Google Cloud.<br />Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine.<br />Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore.<br />Make basic use of BigQuery, Google's managed data warehouse for analytics.</p><p>This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud Platform</h4><ul><li>Explain the advantages of Google Cloud Platform.</li><li>Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones.</li><li>Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS).</li></ul><h4>Getting Started with Google Cloud Platform</h4><ul><li>Identify the purpose of projects on Google Cloud Platform.</li><li>Understand the purpose of and use cases for Identity and Access Management.</li><li>List the methods of interacting with Google Cloud Platform.</li><li>Lab: Getting Started with Google Cloud Platform.</li></ul><h4>Google Compute Engine and Networking</h4><ul><li>Identify the purpose of and use cases for Google Compute Engine.</li><li>Understand the basics of networking in Google Cloud Platform.</li><li>Lab: Deploying Applications Using Google Compute Engine.</li></ul><h4>Google Cloud Platform Storage Options</h4><ul><li>Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable.</li><li>Learn how to choose between the various storage options on Google Cloud Platform.</li><li>Lab: Integrating Applications with Google Cloud Storage.</li></ul><h4>Google Container Engine</h4><ul><li>Define the concept of a container and identify uses for containers.</li><li>Identify the purpose of and use cases for Google Container Engine and Kubernetes.</li><li>Introduction to Hybrid and Multi-Cloud computing (Anthos).</li><li>Lab: Deploying Applications Using Google Container Engine.</li></ul><h4>Google App Engine and Google Cloud Datastore</h4><ul><li>Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore.</li><li>Contrast the App Engine Standard environment with the App Engine Flexible environment.</li><li>Understand the purpose of and use cases for Google Cloud Endpoints.</li><li>Lab: Deploying Applications Using App Engine and Cloud Datastore.</li></ul><h4>Deployment and Monitoring</h4><ul><li>Understand the purpose of template-based creation and management of resources.</li><li>Understand the purpose of integrated monitoring, alerting, and debugging.</li><li>Lab: Getting Started with Stackdriver and Deployment Manager.</li></ul><h4>Big Data and Machine Learning</h4><ul><li>Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.</li><li>Lab: Getting Started with BigQuery.</li></ul><h4>Summary and Review</h4><ul><li>Summary and Review.</li><li>What's Next?.</li></ul></div>

Nexus Human

Google Suite

Google Cloud

Google Workspace

Google Digital Marketing

Google Cloud Fundamentals - Core Infrastructure

<p><strong>Trade like a pro and make your money work for you!</strong></p><p>The UK is the <strong>5th-largest market</strong> for stock trading in the world. Stock trading is a popular profession in the UK due to its potential to generate significant returns. So, if you are interested in this sector, Our <strong>Stock Trading</strong> course can offer you the knowledge and abilities you need to be competitive in the stock market.</p><p>In addition to covering more complex topics like <strong>trading tactics, financial analysis, and risk management</strong>, this course offers an introduction to the fundamentals of investing. Additionally, you'll discover how to use trading systems and decipher market data.</p><p>Are you ready to embark on an exciting journey into the world of stock trading and investing? Welcome to our Stock Trading Master Bundle-an immersive learning experience designed to equip you with the knowledge, strategies, and skills needed to thrive in the financial markets.</p><p><strong>Why This Bundle Matters:</strong></p><p>In an era of financial opportunities and market volatility, acquiring a deep understanding of stock trading is more vital than ever. This comprehensive bundle is your passport to mastering the art and science of trading and investing, backed by a wealth of expert knowledge.</p><p><strong>Courses Included in this Stock Trading Bundle</strong></p><ul><li>Course 1: Stock Trading</li><li>Course 2: Stock Trading &amp; Investing</li><li>Course 3: Stock Trading Analysis with Volume Trading</li><li>Course 4: Cryptocurrency/Forex/Stock Market Trading with Elliot waves</li><li>Course 5: Day Trade Stocks with Price Action and Tape Reading Strategy</li><li>Course 6: Stock Market Chart Patterns for Day Trading</li><li>Course 7: Penny Stock Day Trading Pattern for Consistent Profits</li><li>Course 8: Trading and Financing: Candlestick Pattern</li><li>Course 9: Reverse Candlestick Trading-Forex/Stock Trading</li><li>Course 10: Certificate in Stock Management and Analytics</li><li>Course 11: Hands on Deep Learning Projects - Stock price Prognostics</li><li>Course 12: Technical Analysis Masterclass for Trading &amp; Investing</li><li>Course 13: Day Trading &amp; Scalping Strategies</li><li>Course 14: Trading Psychology: 5 Bad Habits of Losing Traders &amp; Solutions</li><li>Course 15: Complete Swing Trading Training</li><li>Course 16: Technical Analysis: Trading Strategies with MACD</li><li>Course 17: Making Automated Trading Bot Using Python</li><li>Course 18: Anti-Money Laundering (AML) Training</li></ul><p><strong>Learning Outcomes of this Stock Trading Bundle</strong></p><ul><li>Gain knowledge of the stock market and the many stock types that can be traded.</li><li>Acquire knowledge of financial statement analysis and how to spot prospective opportunities.</li><li>Recognise the many kinds of trading techniques and create a strategy.</li><li>Learn to monitor the stock market and spot prospective stock trading opportunities.</li><li>Obtain knowledge about how to recognise and control stock trading risks.</li><li>Have a fundamental understanding of chart patterns and technical analysis.</li><li>Develop the skills and abilities required to develop and sustain a successful trading strategy.</li><li>Improve your ability to comprehend and analyse market news and data.</li><li>Understand the key concepts, terminologies, and mechanics of the stock market</li><li>Analyse stock movements, identify trends, and make informed trading decisions,</li><li>Use a variety of technical indicators, patterns, and charts.</li><li>Use effective risk management techniques to safeguard your capital, reduce losses, and</li><li>manage potential market fluctuations.</li><li>Develop a thorough understanding of money laundering and its various stages.</li><li>Conduct comprehensive risk assessments and due diligence.</li></ul><p><strong>Why Choose This Bundle:</strong></p><p>This bundle is your all-inclusive solution to becoming a savvy trader and investor. It offers:</p><ul><li><p><strong>Expert Guidance:</strong> Benefit from the insights of industry experts and seasoned traders across diverse courses.</p></li><li><p><strong>Practical Application:</strong> Gain hands-on experience through real-world projects, trading simulations, and interactive exercises.</p></li><li><p><strong>Comprehensive Knowledge:</strong> Cover a wide range of trading styles, tools, and strategies, from day trading to long-term investing.</p></li><li><p><strong>Career Enhancement:</strong> Elevate your career prospects by acquiring valuable skills in financial markets.</p></li></ul><p>Embrace the challenge, seize the opportunities, and embark on a transformative journey toward financial empowerment. Enroll today in the "Stock Trading Master Bundle" and pave the way to financial success.</p><p><strong>Why People Love And Enrol in this Stock Trading Bundle From One Education</strong></p><ul><li>Eligibility for CPD-accredited certificates for each of the courses under this Bundle</li><li>Engaging video lessons</li><li>Full Tutor support on weekdays (Monday - Friday)</li><li>The privilege of studying this bundle courses at your own pace</li><li>Complete 24/7 access to the courses of Stock Trading bundle for 1 year</li><li>Efficient exam system, assessment and instant results</li><li>Exam charges</li><li>All the courses under the bundle are designed to provide useful information and teach practical skills</li></ul><p><strong>Course 1: Stock Trading</strong></p><p>This introductory course provides a foundational understanding of stock trading. Explore the basics of buying, selling, and trading stocks on various exchanges. Learn about key concepts such as market orders, limit orders, and order execution. Gain insights into different types of stocks and their valuation methods, empowering you to start your journey in the world of stock trading.</p><p><strong>Course 2: Stock Trading &amp; Investing</strong><strong><br /></strong>Embark on a comprehensive journey into the dynamic world of stock trading and investing. In this course, you'll learn how to decipher market trends, analyse financial statements, and make informed investment choices. Explore various strategies to manage risk and optimise your returns within the stock market. Whether you're a beginner or an experienced trader, this course equips you with the knowledge and tools to navigate the complexities of the stock market with confidence.</p><p><strong>Course 3. Stock Trading Analysis with Volume Trading</strong><strong><br /></strong>Delve into the fascinating realm of volume trading analysis and unlock a deeper understanding of market behaviour. This course guides you through the art of interpreting trading volume to gauge market sentiment, identify trends, and anticipate potential price movements. Through real-world examples and practical exercises, you'll master the skill of integrating volume analysis into your trading strategies for enhanced accuracy and success.</p><p><strong>Course 4. Cryptocurrency/Forex/Stock Market Trading with Elliot Waves</strong><strong><br /></strong>Unveil the secrets of the Elliott Wave Theory and harness its power across diverse financial markets. This course offers a comprehensive exploration of how to apply Elliott Waves to cryptocurrency, forex, and stock trading. Learn to recognize wave patterns, forecast market trends, and make well-informed trading decisions. By the end of the course, you'll possess a valuable skill set for navigating the complexities of different markets using Elliott Waves.</p><p><strong>Course 5. Day Trade Stocks with Price Action and Tape Reading Strategy</strong><strong><br /></strong>Immerse yourself in the exciting world of day trading with a focus on price action and tape reading strategies. This course empowers you to decode price movements, analyse real-time market orders, and execute successful day trades. Through in-depth tutorials and hands-on exercises, you'll develop the expertise to make precise trading decisions based on price charts and live market data.</p><p><strong>Course 6. Stock Market Chart Patterns for Day Trading</strong><strong><br /></strong>Elevate your day trading game by mastering the art of recognizing and capitalising on stock market chart patterns. This course provides comprehensive insights into various patterns, such as triangles, head and shoulders, and flags. You'll learn how to identify potential breakout and reversal opportunities, enabling you to make more informed and profitable day trading decisions.</p><p><strong>Course 7. Penny Stock Day Trading Pattern for Consistent Profits</strong><strong><br /></strong>Discover a specialised approach to day trading with penny stocks and unlock a pattern-driven strategy for consistent profits. Throughout this course, you'll gain a deep understanding of the unique behaviours exhibited by penny stocks. Learn how to navigate risks, identify favourable trading conditions, and apply specialised techniques to capitalise on opportunities within the realm of penny stock day trading.</p><p><strong>Course 8. Trading and Financing: Candlestick Pattern</strong><strong><br /></strong>This course delves into the significance of different candlestick formations, offering insights into market trends and potential reversals. You'll acquire the skills to incorporate candlestick analysis into your trading and financial strategies, enhancing your ability to make informed decisions.</p><p><strong>Course 9. Reverse Candlestick Trading-Forex/Stock Trading</strong><strong><br /></strong>Revolutionise your trading approach with reverse candlestick strategies, designed to identify trend reversals in both forex and stock trading. This course empowers you to decode bearish and bullish reversal patterns, enabling you to anticipate market shifts and make timely trading decisions. By the end of the course, you'll have a refined skillset for navigating the complexities of reverse candlestick trading.</p><p><strong>Course 10. Certificate in Stock Management and Analytics</strong><strong><br /></strong>This comprehensive program covers portfolio optimization, risk assessment, and data-driven decision-making. You'll learn to analyse stock performance, evaluate market trends, and construct investment portfolios that align with your financial goals.</p><p><strong>Course 11. Hands on Deep Learning Projects - Stock price Prognostics</strong><strong><br /></strong>Immerse yourself in the world of hands-on deep learning projects tailored for stock price prognostics. Through practical exercises and real-world scenarios, you'll learn to build predictive models using historical data. By the end of the course, you'll possess the skills to leverage advanced machine learning techniques for accurate stock price predictions.</p><p><strong>Course 12. Technical Analysis Masterclass for Trading &amp; Investing</strong><strong><br /></strong>Elevate your trading and investing acumen with this comprehensive technical analysis masterclass. Explore a wide array of technical indicators, chart patterns, and trend analysis methodologies. Through immersive learning, you'll gain the expertise to integrate technical insights into your trading and investment strategies.</p><p><strong>Course 13. Day Trading &amp; Scalping Strategies</strong><strong><br /></strong>Dive into the art of day trading and scalping with an emphasis on effective strategies. This course equips you with an arsenal of intraday trading techniques, including scalping tactics. Learn to navigate high-speed trading environments, execute precise trades, and manage risk to maximise your trading success.</p><p><strong>Course 14. Trading Psychology: 5 Bad Habits of Losing Traders &amp; Solutions</strong><strong><br /></strong>Explore the psychological aspects of trading and discover strategies to overcome common pitfalls. Delve into the five bad habits that often lead to trading losses and gain insights into solutions to cultivate a disciplined mindset and enhance your trading performance.</p><p><strong>Course 15. Complete Swing Trading Training</strong><strong><br /></strong>Embark on a comprehensive journey into the world of swing trading. This training covers strategy development, risk management, and market analysis tailored specifically for swing trading. By the end of the course, you'll be equipped with the skills to identify and capitalise on swing trading opportunities while effectively managing risk.</p><p><strong>Course 16. Technical Analysis: Trading Strategies with MACD</strong><strong><br /></strong>Unleash the power of the Moving Average Convergence Divergence (MACD) indicator in your trading endeavours. This course provides an in-depth exploration of MACD signals and their integration into various trading strategies. You'll learn how to leverage MACD to make informed trading decisions across diverse markets.</p><p><strong>Course 17. Making Automated Trading Bot Using Python</strong><strong><br /></strong>Learn to create your own automated trading bot using the Python programming language. Through practical tutorials, you'll gain a deep understanding of algorithmic trading principles, data analysis, and API integration. By the end of the course, you'll have a fully functional trading bot capable of executing trades autonomously.</p><p><strong>Course 18. Anti-Money Laundering (AML) Training</strong><strong><br /></strong>Acquire essential knowledge of anti-money laundering regulations and practices within the trading and investment sphere. This course equips you with the skills to detect and prevent money laundering activities, ensuring compliance with legal requirements and safeguarding the integrity of financial markets.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><strong>This Bundle is ideal for</strong></p><ul><li>Students</li><li>Recent graduates</li><li>Job Seekers</li><li>Anyone interested in Stock Trading</li><li>People already working in the relevant fields of Stock Trading and want to polish their knowledge and skill.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>People in the Stock trading sector tend to pursue their careers in the following fields</p><ul><li>Financial Analyst</li><li>Day Trader</li><li>Stock Broker</li><li>Investment Banker</li><li>Quantitative Analyst</li></ul><p>The salary range for Stock Trading in the UK is typically between £25,000 and £100,000 per annum. Salaries vary depending on qualifications, experience and location.</p>
                        </div>

One Education

Stock Trading Diploma Course

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is designed for non-technical business executives who are tasked with making business decisions about emerging technologies in their businesses.</p>Overview<p>You will learn:Blockchain Cloud BasicsIoT OverviewMobility and Ambient ComputingMachine Learning and Deep LearningChatbots, Robotics, and More</p><p>This course is designed for non-technical business executives looking to learn and understand emerging technologies.</p></div><div id="course-pills-2"><h4>Blockchain </h4><h4>Cloud Basics</h4><h4>IoT Overview</h4><h4>Mobility and Ambient Computing</h4><h4>Machine Learning and Deep Learning</h4><h4>Chatbots, Robotics, and More</h4></div>

Blockchain - Emerging Technologies

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This course is for people calculating data value. It does not assume a deep knowledge of maths or statistics. You will be guided through a series of techniques to get an understanding of where your company is losing money due to poor data.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><header style="color: rgb(74, 74, 74); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(74, 74, 74);"><h2 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Course curriculum</h2><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div></header><article fr-original-style="" style="box-sizing: border-box;"><h3 id="isPasted" fr-original-style="" style="box-sizing: border-box;">1. Welcome</h3><ul fr-original-style="" style="box-sizing: border-box;"><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><p style="color: rgb(46, 168, 54); font-size: var(--font-size); box-sizing: border-box;" fr-original-style="color: rgb(46, 168, 54);"><a href="https://courses.nicolaaskham.com/enroll/2585955?et=free_trial" style="color: rgb(46, 168, 54); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(46, 168, 54);">Welcome from Nicola</a></p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Welcome from Nicola</p></li></ul></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;"><br fr-original-style="" style="box-sizing: border-box;"></span></div><header fr-original-style="" style="box-sizing: border-box;"><h3 fr-original-style="" style="box-sizing: border-box;">2. Importance of Data Valuation</h3></header><ul fr-original-style="" style="box-sizing: border-box;"><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><a href="https://courses.nicolaaskham.com/enroll/2585955?et=free_trial" style="color: rgb(46, 168, 54); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(46, 168, 54);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">1- WELCOME AND OVERVIEW<span style="color: rgb(255, 255, 255); box-sizing: border-box;" fr-original-style=" color: rgb(255, 255, 255);">FREE PREVIEW</span></p></a></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 1</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 1</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">2- COMPANY PROCESSES</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 2</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 2</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">3 - IMPORTANCE OF DATA VALUE</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 3</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 3</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">4 - KEY DRIVERS</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 4</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 4</p></li></ul></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;"><br fr-original-style="" style="box-sizing: border-box;"></span></div><header fr-original-style="" style="box-sizing: border-box;"><h3 fr-original-style="" style="box-sizing: border-box;">3. Estimation Techniques</h3></header><ul fr-original-style="" style="box-sizing: border-box;"><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">5 - REDMON METHOD</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 5</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 5</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">6 - ESTIMATION</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 6</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 6</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">7- FERMI METHOD</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 7</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 7</p></li></ul></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;"><br fr-original-style="" style="box-sizing: border-box;"></span></div><h3 id="isPasted" fr-original-style="" style="box-sizing: border-box;">4. Statistical Techniques</h3><ul fr-original-style="" style="box-sizing: border-box;"><ul style="color: rgb(74, 74, 74); box-sizing: border-box;" fr-original-style="color: rgb(74, 74, 74);"><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">8 - STATISTICAL INTRODUCTION</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 8</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">-- Presentation - Lesson 8</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">9 - POINT ESTIMATES</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 9</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">-- Presentation - Lesson 9</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">10 - EFFECTS ESTIMATES</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 10</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">-- Presentation - Lesson 10</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">11 - GRAPHICAL TECHNIQUES AND ISSUES</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 11</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 11</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">12 - REGRESSION ANALYSIS</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 12</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">-- Presentation - Lesson 12</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">13 - MACHINE LEARNING OVERVIEW</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 13</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 13</p></li></ul></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;"><br fr-original-style="" style="box-sizing: border-box;"></span></div><h3 fr-original-style="" style="box-sizing: border-box;"><span style="color: inherit; box-sizing: border-box;" fr-original-style=" color: inherit;"><h3 fr-original-style="" style="box-sizing: border-box;">5. Wrap Up Topics</h3></span></h3><ul fr-original-style="" style="box-sizing: border-box;"><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">14 - FINANCIAL MEASURES</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 14</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">--- Presentation - Lesson 14</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">15 - THANK YOU AND FINAL THOUGHTS</p></li><li fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">---Transcript - Lesson 15</p></li></ul></ul></article>

The Data Governance Coach 

Data Management

This course is for people calculating data value. It does not assume a deep knowledge of maths or statistics. You will be guided through a series of techniques to get an understanding of where your company is losing money due to poor data.

Determining Data Value

<p>Have you ever wondered how machines are able to recognise handwritten digits with such precision? Have you ever been amazed by the power of neural networks and their ability to process complex information? If so, our Deep Learning Projects - Handwritten Digit Recognition Using Neural Network course is perfect for you.</p><p>In this course, you'll learn how to build your own neural network to recognise handwritten digits using the power of deep learning. Using Google Collab, you'll import data, preprocess it, and create your own neural network model. You'll learn how to train your model, make predictions, and even create your own convolutional neural network model for even more advanced recognition.</p><p>So if you're ready to delve into the world of deep learning and build your own handwritten digit recognition system, enroll in our Deep Learning Projects course today and unlock the power of neural networks.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a solid understanding of the principles of deep learning and neural networks.</li><li>Learn how to use Google Collab to import and preprocess data for deep learning projects.</li><li>Discover how to create your own neural network model to recognise handwritten digits.</li><li>Understand how to train your model, make predictions, and evaluate its performance.</li><li>Learn how to create your own convolutional neural network model for even more advanced recognition tasks.</li></ul><p>The Deep Learning Projects - Handwritten Digit Recognition Using Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network for handwritten digit recognition. Using Google Collab, you'll learn how to import and preprocess data for deep learning projects, create your own neural network model, and train it to recognize handwritten digits with high accuracy. You'll also discover how to evaluate the performance of your model and create a convolutional neural network model for even more advanced recognition tasks.</p><p><strong>â±â± Deep Learning Projects - Handwritten Digit Recognition Using Neural Network</strong><strong> Course Curriculum </strong></p><ul><li><strong>Lesson 01: </strong>Introduction to Project</li><li><strong>Lesson 02:</strong> Google Collab</li><li><strong>Lesson 03:</strong> Importing Packages and Data</li><li><strong>Lesson 04:</strong> Preprocessing and Model Creation</li><li><strong>Lesson 05: </strong>Training the Model and Prediction</li><li><strong>Lesson 06: </strong>Model Creation using CNN</li><li><strong>Lesson 07: </strong>CNN Model Prediction</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>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Data analysts who want to expand their skills in deep learning and neural networks.</li><li>Programmers who want to learn how to build their own neural network models for advanced recognition 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 deep learning and neural networks.</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>Software Engineer: £28,000 - £60,000</li><li>Technical Lead: £40,000 - £90,000</li><li>Chief Technology Officer: £90,000 - £250,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

Deep Learning Projects - Handwritten Digit Recognition Using Neural Network Course

<p>Are you ready to unlock the full potential of convolutional neural networks and build your own deep learning projects? Do you want to take your skills to the next level and learn how to create advanced models for image recognition? If so, our Deep Learning Projects - Convolutional Neural Network course is perfect for you.</p><p>In this course, you'll learn everything you need to know to create your own convolutional neural network models for advanced image recognition tasks. From installing the necessary packages to data preprocessing and augmentation, you'll learn how to use Python to build advanced deep learning projects. You'll also gain a deeper understanding of data generators, different models, and how to evaluate the accuracy of your models.</p><p>So if you're ready to dive into the world of deep learning and take your skills to the next level, enrol in our Deep Learning Projects course today and start building your own convolutional neural network models.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a solid understanding of convolutional neural networks and their applications in deep learning.</li><li>Learn how to install the necessary packages and set up a dataset structure for deep learning projects.</li><li>Discover how to create your own convolutional neural network model and layers using Python.</li><li>Understand how to preprocess and augment data for advanced image recognition tasks.</li><li>Learn how to evaluate the accuracy of your models and understand the different models available for deep learning projects.</li></ul><p>The Deep Learning Projects - Convolutional Neural Network course is designed to provide you with the skills and knowledge you need to build your own advanced deep learning projects. Using Python, you'll learn how to install the necessary packages, set up a dataset structure, and create your own convolutional neural network model and layers. You'll also learn how to preprocess and augment data to enhance the accuracy of your models and evaluate the performance of your models using data generators.</p><p><strong>â±â± Deep Learning Projects - Convolutional Neural Network</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction</li><li><strong>Section 02: </strong>Installations</li><li><strong>Section 03: </strong>Getting Started</li><li><strong>Section 04: </strong>Accuracy</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>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Data analysts who want to expand their skills in deep learning and convolutional neural networks.</li><li>Programmers who want to learn how to build advanced models for image recognition.</li><li>Entrepreneurs who want to develop their own deep learning-based applications for image recognition.</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 convolutional 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>Computer Vision Engineer: £30,000 - £70,000</li><li>Technical Lead: £40,000 - £90,000</li><li>Chief Technology Officer: £90,000 - £250,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

Deep Learning Projects - Convolutional Neural Network Course

<p>Some farmers and researchers are exploring the use of advanced technologies, such as artificial intelligence and machine learning, to develop weed recognition systems. These systems can automatically identify and differentiate between crops and weeds, allowing for more targeted and efficient weed management. Welcome to therealm of Weed Recognition and Management, a comprehensive course designed to equip you with invaluable insights into the intricate world of weeds and their impact on diverse ecosystems. Dive into the depths of weed identification, where you'll hone the skills needed to distinguish between various weed species, understanding their unique characteristics.</p><p>Learn about weed biology and ecology, gaining a profound understanding of the underlying mechanisms that drive their growth and proliferation. Uncover a repertoire of physical and cultural weed control methods, arming yourself with an arsenal of sustainable practices to manage and mitigate the impact of unwanted vegetation.</p><p>This course transcends conventional boundaries, offering a dynamic blend of theoretical knowledge and practical skills essential for anyone involved in agriculture, horticulture, or environmental management. Join us, where you'll emerge not only as a proficient weed manager but also as a steward of sustainable practices in a world grappling with the challenges of weed proliferation. Elevate your expertise, contribute to ecological harmony, and cultivate a brighter, weed-resilient future.</p><p><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>FREE PDF + Hardcopy certificate</li><li>Fully online, interactive course</li><li>Self-paced learning and laptop, tablet and smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum</strong></p><ul><li>Module 01: Introduction to Weeds and Their Impact</li><li>Module 02: Weed Identification</li><li>Module 03: Weed Biology and Ecology</li><li>Module 04: Physical and Cultural Weed Control Methods</li><li>Module 05: Chemical Weed Control Methods</li><li>Module 06: Integrated Weed Management</li></ul><p><strong>Learning Outcomes</strong></p><ul><li>Recognize diverse weed species through advanced identification techniques.</li><li>Understand the intricate biology and ecology of weeds for effective management.</li><li>Apply physical and cultural methods to control weeds in various environments.</li><li>Implement chemical weed control strategies with precision and environmental consciousness.</li><li>Master integrated weed management for a holistic and sustainable approach.</li><li>Develop expertise in weed recognition and management for practical application.</li></ul><p><strong>Accreditation</strong></p><p>This course is <strong>CPD Quality Standards</strong> (<strong>CPD QS)</strong> accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certificate</strong></p><p>After completing this course, you will get a FREE Digital Certificate from Training Express.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Agriculture professionals seeking advanced weed management knowledge.</li><li>Environmentalists aiming to contribute to ecosystem preservation.</li><li>Farmers looking to enhance crop yield through effective weed control.</li><li>Horticulturists interested in sustainable and integrated weed management.</li><li>Landscapers desiring in-depth knowledge of weed dynamics.</li><li>Conservationists striving to protect native flora and fauna.</li><li>Botanists keen on understanding the ecological impact of different weed species.</li><li>Individuals passionate about sustainable agriculture and environmental stewardship.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Agricultural Consultant</li><li>Environmental Scientist</li><li>Crop Protection Specialist</li><li>Ecological Restoration Technician</li><li>Horticulture Manager</li><li>Conservation Officer</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 <strong>£10</strong>.<br />Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.</p></div>

                        </div>

Training Express

Weed Recognition and Management Course

<p>Dive into the fascinating world of Python programming with our 'Python for Beginners Part 1' course. Python is a universal language with growing popularity and wide-ranging applications. This course is an excellent opportunity to unlock the power of Python, whether for personal or proficient growth. Start your journey with Python's fundamental concepts, advancing to data types and operators, and finally, mastering control flow mechanisms. This course lays the groundwork for more advanced topics, preparing you to become proficient in Python. By exploring Python for Beginners Part 1, you embark on a journey to acquiring one of the most sought-after skills in today's job market. Create your success story with Python and stay ahead in your skilled journey. Your step towards mastering Python starts here.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Learn the basics of Python programming</li><li>Understand the different data types and operators in Python</li><li>Learn how to use data structures in Python</li><li>Learn how to control the flow of your code in Python</li></ul><p>This course begins with an introduction to Python, providing an overview of the language's fundamentals, guiding learners to set up Python on their local machines, and familiarising them with its syntax. The next module delves into Python's data types and operators, providing students with the knowledge to manipulate numbers, strings, and Booleans. Finally, in module four, we expand on these foundations by teaching the use of Python's core data structures: lists, dictionaries, sets, and tuples.</p><p>Module five delves into the control flow mechanisms in Python, covering topics such as conditional statements, loops, and exception handling. These mechanisms are vital for creating programs to make decisions, repeat tasks, and handle errors. Throughout the course, the learners are encouraged to apply the learned principles and techniques to real-world problems, thus developing a practical understanding of Python.</p><p><strong>Certification</strong></p><p>Upon successful completion of the course, all learners will be eligible to receive a certificate titled Python For Beginners Part 1 to acknowledge their achievement. We are delighted to offer two formats for your certificate: PDF and Hardcopy.</p><ul><li><strong>PDF Certificate:</strong> This digital version of your certificate will be available for you to download and print for totally <strong>FREE</strong>. </li><li><strong>Hardcopy Certificate (UK Delivery):</strong> For those who wish to have a physical token of their achievement, we offer a high-quality, printed certificate. This hardcopy certificate is also provided free of charge. However, please note that delivery fees apply. If your shipping address is within the United Kingdom, the delivery fee will be only <strong>£3.99</strong>.</li><li><strong>Hardcopy Certificate (International Delivery): </strong>For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only <strong>£10</strong>. </li></ul><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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                            <p>This course is for anyone looking to start their journey into programming, explicitly using Python. It's perfect for beginners with little or no prior knowledge of Python or any other programming language. Individuals interested in data analysis, web development, artificial intelligence, or learning a new skill might find this course beneficial. Students, teachers, hobbyists, or entrepreneurs seeking to automate tasks or make sense of data can leverage Python's simplicity and versatility.</p>
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                            <ul><li>Data Analyst - £25K to £40K/year.</li><li>Software Developer - £30K to £55K/year.</li><li>Web Developer - £24K to £50K/year.</li><li>Automation Engineer - £35K to £60K/year.</li><li>Machine Learning Engineer - £40K to £70K/year.</li></ul>
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                                    <h4>Digital certificate</h4>
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            Digital certificate -
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        <h4>Hard copy certificate</h4>
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            Hard copy certificate -
            Included
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            <div><p><strong>Hardcopy Certificate (UK Delivery):</strong></p><p>For those who wish to have a physical token of their achievement, we offer a high-quality, printed certificate. This hardcopy certificate is also provided free of charge. However, please note that delivery fees apply. If your shipping address is within the United Kingdom, the delivery fee will be only <strong>£3.99</strong>.</p><p><strong>Hardcopy Certificate (International Delivery): </strong></p><p>For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only <strong>£10</strong>.</p></div>

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Get Up and Running with Python Course

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

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

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

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

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

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

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

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

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

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