Name: Artificial Intelligence & Project Management
Price: 21.21 GBP
Availability: InStock

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			<h2>Overview
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					<p><span>This comprehensive course on </span><b><span>Complete Microsoft Power BI 2021</span></b> will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This </span><b>Complete Microsoft Power BI 2021</b> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this </span><b><span>Complete Microsoft Power BI 2021</span></b>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our </span><b><span>Complete Microsoft Power BI 2021</span></b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>15 sections • 140 lectures • 14:25:00 total length</p></div><div><div><div><div><div><div><div><p>•Welcome!: 00:01:00</p></div><div><div><p>•What is Power BI?: 00:03:00</p></div><div><div><p>•Download &amp; Installing Power BI Desktop: 00:04:00</p></div><div><div><p>•Getting to know the interface: 00:03:00</p></div><div><div><p>•Mini Project: Transform Data: 00:07:00</p></div><div><div><p>•Mini Project: Visualize Data: 00:05:00</p></div><div><div><p>•Mini Project: Creating a Data Model: 00:07:00</p></div><div><div><p>•Course Outline: What will you learn in this course?: 00:05:00</p></div><div><div><p>•How to learn best with this course?: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating our initial project file: 00:04:00</p></div><div><div><p>•Working with the attached project files: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Exploring the Query Editor: 00:06:00</p></div><div><div><p>•Connecting to our data source: 00:07:00</p></div><div><div><p>•Editing rows: 00:08:00</p></div><div><div><p>•Changing data types: 00:08:00</p></div><div><div><p>•Replacing values: 00:03:00</p></div><div><div><p>•Close &amp; Apply: 00:03:00</p></div><div><div><p>•Connecting to a csv file: 00:03:00</p></div><div><div><p>•Connecting to a web page: 00:05:00</p></div><div><div><p>•Extracting characters: 00:06:00</p></div><div><div><p>•Splitting &amp; merging columns: 00:09:00</p></div><div><div><p>•Creating conditional columns: 00:06:00</p></div><div><div><p>•Creating columns from examples: 00:09:00</p></div><div><div><p>•Merging Queries: 00:17:00</p></div><div><div><p>•Pivoting &amp; Unpivoting: 00:06:00</p></div><div><div><p>•Appending Queries: 00:08:00</p></div><div><div><p>•Practice &amp; Solution: Population table: 00:15:00</p></div><div><div><p>•The Fact-Dimension-Model: 00:09:00</p></div><div><div><p>•Practice: Load the dimension table: 00:04:00</p></div><div><div><p>•Organizing our queries in groups: 00:03:00</p></div><div><div><p>•Entering data manually: 00:05:00</p></div><div><div><p>•Creating an index column: 00:03:00</p></div><div><div><p>•Workflow &amp; more transformations: 00:05:00</p></div><div><div><p>•Module summary: 00:05:00</p></div><div><div><p>•Exercise  1 - Instruction: 00:02:00</p></div><div><div><p>•Exercise  1 - Exercise Solution: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Advanced Editor - Best practices: 00:09:00</p></div><div><div><p>•Performance: References vs. Duplicating: 00:10:00</p></div><div><div><p>•Performance: Enable / Disable Load &amp; Report Refresh: 00:05:00</p></div><div><div><p>•Group by: 00:05:00</p></div><div><div><p>•Mathematical Operations: 00:05:00</p></div><div><div><p>•Run R Script: 00:15:00</p></div><div><div><p>•Using Parameters to dynamically transform data: 00:06:00</p></div><div><div><p>•M formula language: Basics: 00:07:00</p></div><div><div><p>•M formula language: Values, Lists &amp; Tables: 00:14:00</p></div><div><div><p>•M formula language: Functions: 00:13:00</p></div><div><div><p>•M formula language: More functions &amp; steps: 00:05:00</p></div><div><div><p>•Exercise 2 - Instructions: 00:01:00</p></div><div><div><p>•Exercise 2 - solution: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Understanding the relationship: 00:05:00</p></div><div><div><p>•Create &amp; edit relationships: 00:06:00</p></div><div><div><p>•One-to-many &amp; one-to-one relationship: 00:06:00</p></div><div><div><p>•Many-to-many (m:n) relationship: 00:08:00</p></div><div><div><p>•Cross filter direction: 00:06:00</p></div><div><div><p>•Activate &amp; deactivate relationships: 00:06:00</p></div><div><div><p>•Model summary: 00:03:00</p></div><div><div><p>•Exercise 3 Create Model: 00:02:00</p></div><div><div><p>•Exercise 3 Solution: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Our first visual: 00:08:00</p></div><div><div><p>•The format tab: 00:12:00</p></div><div><div><p>•Understanding tables: 00:10:00</p></div><div><div><p>•Conditional formatting: 00:09:00</p></div><div><div><p>•The Pie Chart: 00:06:00</p></div><div><div><p>•All about the filter visual: 00:13:00</p></div><div><div><p>•The filter pane for developers: 00:09:00</p></div><div><div><p>•Cross filtering &amp; edit interactions: 00:04:00</p></div><div><div><p>•Syncing slicers across pages: 00:07:00</p></div><div><div><p>•Creating drill downs: 00:08:00</p></div><div><div><p>•Creating drill throughs: 00:07:00</p></div><div><div><p>•The tree map visual: 00:07:00</p></div><div><div><p>•The decomposition tree: 00:05:00</p></div><div><div><p>•Understanding the matrix visual: 00:05:00</p></div><div><div><p>•Editing pages: 00:07:00</p></div><div><div><p>•Buttons &amp; Actions: 00:09:00</p></div><div><div><p>•Bookmarks to customize your report: 00:10:00</p></div><div><div><p>•Analytics and Forecasts with line charts: 00:10:00</p></div><div><div><p>•Working with custom visuals: 00:07:00</p></div><div><div><p>•Get data using R Script &amp; R Script visual: 00:08:00</p></div><div><div><p>•Asking questions - Q&amp;A visual: 00:04:00</p></div><div><div><p>•Wrap up - data visualization: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Python in Power BI - Plan of attack: 00:03:00</p></div><div><div><p>•Setting up Python for Power BI: 00:03:00</p></div><div><div><p>•Transforming data using Python: 00:11:00</p></div><div><div><p>•Creating visualizations using Python: 00:08:00</p></div><div><div><p>•Violin plots, pair plots &amp; ridge plots using Python: 00:15:00</p></div><div><div><p>•Machine learning (BayesTextAnalyzer) using Python: 00:00:00</p></div><div><div><p>•Performance &amp; Troubleshooting: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction: 00:01:00</p></div><div><div><p>•Show Empathy &amp; Identify the Requirement: 00:03:00</p></div><div><div><p>•Finding the Most Suitable KPI's: 00:02:00</p></div><div><div><p>•Choose an Effective Visualization: 00:04:00</p></div><div><div><p>•Make Use of Natural Reading Pattern: 00:03:00</p></div><div><div><p>•Tell a Story Using Visual Cues: 00:05:00</p></div><div><div><p>•Avoid Chaos &amp; Group Information: 00:02:00</p></div><div><div><p>•Warp Up - Storytelling with Data: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction: 00:03:00</p></div><div><div><p>•The project data: 00:04:00</p></div><div><div><p>•Measures vs. Calculated Columns: 00:15:00</p></div><div><div><p>•Automatically creating a date table in DAX: 00:08:00</p></div><div><div><p>•CALENDAR: 00:05:00</p></div><div><div><p>•Creating a complete date table with features: 00:04:00</p></div><div><div><p>•Creating key measure table: 00:03:00</p></div><div><div><p>•Aggregation functions: 00:06:00</p></div><div><div><p>•The different versions of COUNT: 00:14:00</p></div><div><div><p>•SUMX - Row based calculations: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•CALCULATE - The basics: 00:11:00</p></div><div><div><p>•Changing the context with FILTER: 00:07:00</p></div><div><div><p>•ALL: 00:08:00</p></div><div><div><p>•ALL SELECTED: 00:03:00</p></div><div><div><p>•ALL EXCEPT: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•How to go on now?: 00:03:00</p></div><div><div><p>•Power BI Pro vs Premium &amp; Signing up: 00:04:00</p></div><div><div><p>•Exploring the interface: 00:04:00</p></div><div><div><p>•Discovering your workspace: 00:03:00</p></div><div><div><p>•Connecting Power BI Desktop &amp; Cloud: 00:04:00</p></div><div><div><p>•Understanding datasets &amp; reports: 00:03:00</p></div><div><div><p>•Working on reports: 00:04:00</p></div><div><div><p>•Updating reports from Power BI Desktop: 00:04:00</p></div><div><div><p>•Creating and working with workspaces: 00:07:00</p></div><div><div><p>•Installing &amp; using a data gateway: 00:13:00</p></div><div><div><p>•Get Quick Insights: 00:03:00</p></div><div><div><p>•Creating dashboards: 00:04:00</p></div><div><div><p>•Sharing our results through Apps: 00:10:00</p></div><div><div><p>•Power BI Mobile App: 00:05:00</p></div><div><div><p>•Creating the layout for the Mobile App: 00:04:00</p></div><div><div><p>•Wrap up - Power BI Cloud: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction: 00:03:00</p></div><div><div><p>•Creating a Row-Level Security: 00:05:00</p></div><div><div><p>•Row-Level Security in the Cloud: 00:04:00</p></div><div><div><p>•Row-Level Security &amp; Data Model: 00:05:00</p></div><div><div><p>•Dynamic Row-Level Security: 00:07:00</p></div><div><div><p>•Dynamic Many-to-Many RLS: 00:04:00</p></div><div><div><p>•Hierarchical Row-Level Security: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•JSON &amp; REST API: 00:10:00</p></div><div><div><p>•Setting up a local MySQL database: 00:14:00</p></div><div><div><p>•Connecting to a MySQL database in Power BI: 00:05:00</p></div><div><div><p>•Connecting to a SQL database (PostgreSQL): 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Congratulations &amp; next steps: 00:06:00</p></div><div><div><p>•The End: 00:01:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources - Complete Microsoft Power BI 2021: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Powerbi

Online Course Creation

Microsoft Power Apps

Microsoft Power BI

Microsoft Power Platform

Microsoft BI

Complete Microsoft Power BI 2021

General Admission

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>To gain the most from attending this course you should possess the following incoming skills:<br />Basic knowledge of programming concepts and syntax in Python.<br />Familiarity with common data formats such as CSV, JSON, and XML.<br />Experience using command-line interfaces and basic text editing tools.<br />Understanding of basic machine learning concepts and algorithms.</p>Overview<p>Working in an interactive learning environment, led by our engaging expert, you will:<br />Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions.<br />Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4.<br />Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements.<br />Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases.<br />Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions.<br />Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex.<br />Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development.</p><p>Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts.  Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment.</p></div><div id="course-pills-2"><h4>Introduction to Prompt Engineering</h4><ul><li>Overview of prompt engineering and its importance in AI applications</li><li>Major applications of prompt engineering in business</li><li>Common challenges faced in prompt engineering</li><li>Overview of GPT-4 and its role in prompt engineering</li><li>Key terminology and concepts in prompt engineering</li></ul><h4>Getting Things Ready: Text Preprocessing and Data Cleansing</h4><ul><li>Importance of data preprocessing in prompt engineering</li><li>Techniques for text cleaning and normalization</li><li>Tokenization and n-grams</li><li>Stop word removal and stemming</li><li>Regular expressions and pattern matching</li></ul><h4>GPT-4 Tokenization and Input Formatting</h4><ul><li>GPT-4 tokenization and its role in prompt engineering</li><li>Understanding and formatting GPT-4 inputs</li><li>Context windows and token limits</li><li>Controlling response length and quality</li><li>Techniques for handling out-of-vocabulary tokens</li></ul><h4>Prompt Design and Optimization</h4><ul><li>Master the skills to design, optimize, and test prompts for various business tasks.</li><li>Designing effective prompts for different tasks</li><li>Techniques for prompt optimization</li><li>GPT-4 system and user parameters for controlling behavior</li><li>Importance of prompt testing and iteration</li><li>Best practices for prompt engineering in business applications</li></ul><h4>Advanced Techniques and Tools in Prompt Engineering</h4><ul><li>Learn advanced techniques and tools for prompt engineering and their integration in business applications.</li><li>Conditional text generation with GPT-4</li><li>Techniques for handling multi-turn conversations</li><li>Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground</li><li>Integration of GPT-4 with other software platforms and tools</li><li>Monitoring and maintaining prompt performance</li></ul><h4>Code Generation and Testing with Prompt Engineering</h4><ul><li>Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects.</li><li>Introduction to code generation with AI models like GPT-4</li><li>Designing prompts for code generation across programming languages</li><li>Techniques for specifying requirements and constraints in prompts</li><li>Generating and interpreting code snippets using AI-driven solutions</li><li>Integrating generated code into existing projects and codebases</li><li>Best practices for testing and validating AI-generated code</li></ul><h4>Ethics and Responsible AI</h4><ul><li>Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business.</li><li>Ethical considerations in prompt engineering</li><li>Bias in AI systems and its impact on prompt engineering</li><li>Techniques to minimize bias and ensure fairness</li><li>Best practices for responsible AI deployment in business applications</li><li>Monitoring and addressing ethical concerns in prompt engineering</li></ul></div>

Nexus Human

Generative AI

NLP Practitioner

Quick Start to Mastering Prompt Engineering for Software Developers  (TTAI2300)

<p><span>Introducing the '</span><b>Python Programming Bible | Networking, GUI, Email, XML, CGI</b><span>' - your comprehensive, all-in-one resource for </span><b>mastering Python</b><span>!</span></p>
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<p><span>Are you an aspiring developer looking to dive into the ocean of Python programming or a seasoned coder seeking to level up your Python game? Look no further! Our course is expertly designed to take you from the basics to the complexities of </span><b>Python, including Networking, GUI, Email, XML, and CGI</b><span>.</span></p>
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<p><span>If you've ever dreamt of not just learning Python but truly mastering it, this is the course for you. This program is designed to provide a solid foundation and sharpen your skills in one of the most in-demand programming languages, while also introducing you to its many applications.</span></p>
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<p><span>This course starts with the</span><b> basics of Python</b><span>, providing a gentle yet thorough introduction and setup that caters to beginners as well as those looking to refresh their</span><b> Python knowledge</b><span>. As we study deeper into the heart of Python, we dive into objects, classes, and the power of regular expressions. But it doesn't stop there! You'll also become comfortable with concepts like </span><b>CGI programming</b><span>, which is an important building block for creating dynamic web pages.</span></p>
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<p><span>Navigating from core programming, we transition into the intricacies of managing databases and executing multithreading in Python. You'll gain the confidence to handle complex data management tasks, understand how Python interacts with databases, and efficiently manages multiple tasks simultaneously. The XML section allows you to get hands-on with parsing, data extraction, and manipulation, while the GUI section unveils the art of creating beautiful, user-friendly interfaces using Python.</span></p>
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<p><span>The course is enriched with a diverse set of resources, including real-world projects, quizzes, and interactive coding exercises. This is more than just a course, it's </span><b>your passport to a new realm of opportunities</b><span>, unlocking a world where </span><b>Python programming is your strength, not just a skill</b><span>. </span></p>
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<p><span>So whether you're a student aiming to get a head start on your peers, a professional looking to diversify your skills, or an enthusiast wanting to dive deeper into the Python universe, the </span><b>Python Programming Bible</b><span> is the starting point for your journey to becoming a </span><b>Python expert</b><span>. Enrol today and step into a future of endless opportunities with Python!</span></p>
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<p><b>Learning Outcomes:</b></p>
<p><span>Upon completion of the Python Programming Bible course, you should be able to:</span></p>
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<ul>
<li><span>Understand and implement Python basics and advanced concepts.</span></li>
<li><span>Build object-oriented programs with Python.</span></li>
<li><span>Utilise regular expressions for pattern-matching tasks.</span></li>
<li><span>Develop dynamic web pages using CGI programming.</span></li>
<li><span>Interact with databases efficiently using Python.</span></li>
<li><span>Apply multithreading for better utilisation of resources.</span></li>
<li><span>Process and manipulate data using XML in Python.</span></li>
<li><span>Design and create user-friendly GUIs with Python.</span></li>
</ul>
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<p><b>Who is this course for?</b></p>
<p><span>This Python Programming Bible course is ideal for the following:</span></p>
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<ul>
<li><span>Beginners aiming to learn Python from scratch.</span></li>
<li><span>Professionals looking to broaden their programming skills.</span></li>
<li><span>Students pursuing a degree in Computer Science.</span></li>
<li><span>Web developers looking to integrate Python into their toolkit.</span></li>
<li><span>Data enthusiasts aiming to handle data with Python.</span></li>
</ul>
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<p><b>Career Path:</b></p>
<p><span>This Python Programming Bible course will help you to develop your knowledge and skills to pursue different careers, such as:</span></p>
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<ul>
<li><span>Python Developer: (£35,000 - £70,000).</span></li>
<li><span>Data Analyst: (£27,000 - £55,000).</span></li>
<li><span>Web Developer: (£24,000 - £60,000).</span></li>
<li><span>Data Scientist: (£45,000 - £90,000).</span></li>
<li><span>Machine Learning Engineer: (£50,000 - £90,000).</span></li>
<li><span>Software Developer: (£30,000 - £70,000).</span></li>
</ul>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials of the </span><b>Python Programming Bible | Networking, GUI, Email, XML, CGI</b><span> there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for free. Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Python Programming Bible | Networking, GUI, Email, XML, CGI</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Python Programming Bible | Networking, GUI, Email, XML, CGI</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p><div>
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	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Section 01: Introduction &amp; Setup</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup on Mac OS X</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup On Linux/Ubuntu</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup On Windows</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Run Code Online</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: Basics</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Comments</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Variables &amp; Variable Types</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lists</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tuples</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dictionary</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Type Conversion</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Arithmetic Operators</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Comparison Operators</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment Operators</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Bitwise Operators</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Logical Operators</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Membership Operators</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Identity Operators</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Operator Precedence</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Making</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loop Control Statements</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Numbers</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Strings</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lists In Depth</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tuples In Depth</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dictionary In Depth</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Date &amp; Time</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Modules</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>File Inputs &amp; Outputs</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Handling Exceptions</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Classes/Objects</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Simple Example</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Instance Objects</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Accessing Attributes</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Constructor New &amp; Init Method</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Destroying Objects</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Class Inheritance</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Overriding Methods</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Overloading Methods</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Overloading Operators</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Hiding</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Regular Expressions</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Match Function</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Search Function</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Advanced Expressions</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Search &amp; Replace</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: CGI Programming</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic CGI Programming</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Get Method</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Post Method</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Cookies</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Database</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup Database</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Connect To Database</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create Table</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>INSERT Operation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>READ Operation</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>UPDATE Operation</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>DELETE Operation</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Simple Network Example</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Simple Client</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 07: Multithreading</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Initiate a New Thread</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create Thread</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Synchronise Threads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multithreaded Priority Queue</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: XML</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Parse an XML File</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 09: GUI</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Button Preview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Canvas</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Checkbutton</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Entry</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Frame</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Label</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List Box</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Menu button</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Menu</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Message</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Radio button</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Scale</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Scrollbar</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Text</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top-level</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Spinbox</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Paned Window</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Message Box</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Label Frame</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 10: Resource</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resource</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Python Programming Bible | Networking, GUI, Email, XML, CGI</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

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													</span>
										<span>Access to any internet-enabled smart device.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<h2>Why should I take this course?</h2>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>12+ hours of on-demand video lectures and downloadable resources.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will receive a completion certificate upon completing the course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will learn the researched and proven approach adopted by successful salespeople to transform their careers.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will be able to incorporate various practical sales techniques successfully and understand your customers better.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Who is This Course For?</span>
									</li>
						</ul>
				</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Individuals interested in Python Data Science </span>
									</li>
								<li>
											<span>
													</span>
										<span>Students</span>
									</li>
								<li>
											<span>
													</span>
										<span>Job Seekers </span>
									</li>
								<li>
											<span>
													</span>
										<span>Existing worker wanting to polish their skills</span>
									</li>
								<li>
											<span>
													</span>
										<span>Workers wanting pay rise or promotion </span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>

Skill Up

Maternity Support Worker

Midwifery

Nursing & Midwifery

Midwife

Trainers Course

Maternity Care Assistant

With the rise of machine learning applications in recent years, demand for python programmers continues to grow correspondingly.

Midwifery Training

<section>
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			<div>
					<div>
				<div>

				</div>
				<div>
				<div>
					<div><p><span>If you're looking forward to building a career in a sector that technologies will never replace, then our </span><b>Welding Training</b><span> course is the perfect trade for you. </span></p><p><span>The detailed </span><b>Welding Training</b><span> course will provide you with the knowledge of fundamental welding principles and process. From preparing metals for fabrication to basic welding techniques, this welding training course has it all. It will help you to grow expertise in using up to date and standard welding practices. Also, it will help you to understand the mechanism of welding instruments and tools. This course will prepare you to understand various safety hazards and provide tactics to overcome them. </span><span>So what are you waiting for? Get up and enrol today in our </span><b>Welding Training</b><span> course and acquire knowledge and skillset to flourish your career in the welding profession.</span></p></div>
				</div>
				</div>
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		</div>
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			</div>
		</section>
				<section>
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				</div>
				<div>
				<h2>Learning outcome</h2>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Gain fundamental knowledge of welding.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn about various welding types of equipment and their use.</span>
									</li>
								<li>
											<span>
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										<span>Acquire skill sets of practical and modern welding techniques.</span>
									</li>
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											<span>
													</span>
										<span>Learn to weld different types of metals.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Know about health and safety precautions in the welding profession. </span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
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				</div>
				<div>
				<h2>Requirements</h2>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Access to any internet-enabled smart device.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<h2>Why should I take this course?</h2>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>1+ hours of on-demand video lectures and downloadable resources.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will receive a completion certificate upon completing the course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will learn the researched and proven approach adopted by successful salespeople to transform their careers.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will be able to incorporate various practical sales techniques successfully and understand your customers better.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Module 1: Introduction to Welding</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to Welding</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 2: Getting Familiar with Metals</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Familiar with Metals</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 3: Exploring Welding Methods</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Exploring Welding Methods</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 4: Knowing the Welding Metals</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Knowing the Welding Metals</td><td><span>🕐 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 5: Preparing Steel for Welding</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Preparing Steel for Welding</td><td><span>🕐 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 6: Working with Stainless Steel</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working with Stainless Steel</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 7: Working with Aluminium and Other Metals</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working with Aluminium and Other Metals</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>*Welding Tutorials*</strong></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 8: MIG Welding Tutorial</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>MIG Welding Tutorial</td><td><span>🕐 00:57:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 9: TIG Welding Tutorial</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>TIG Welding Tutorial</td><td><span>🕐 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 10: Arc Welding Tutorial</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Arc Welding Tutorial</td><td><span>🕐 01:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>*Welding Hazards and Safety*</strong></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 11: Illness Caused by Welding Fume and Gases</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Illness Caused by Welding Fume and Gases</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 12: Welding Fume - Reducing the Risk</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Welding Fume - Reducing the Risk</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 13: Hearing Loss and Vibration White Finger</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Hearing Loss and Vibration White Finger</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 14: Other Safety Issues</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Other Safety Issues</td><td><span>🕐 00:26:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 15: Protection during Welding</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Protection during Welding</td><td><span>🕐 00:40:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 16: Safety in Gas Welding</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Safety in Gas Welding</td><td><span>🕐 00:34:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

MIG Welding

Weld

MMA Welding

Welder

Welding

Lead Welding

Welding Training

NextGen Learning

CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Attendee roles might include:<br />Software Developers/Programmers<br />Data Scientists<br />Machine Learning Engineers<br />AI Researchers<br />User Interface (UI) and User Experience (UX) Designers<br />Technical Product Managers<br />Technical Team Leads</p>Overview<p>Working in an interactive learning environment, led by our engaging AI expert you'll:<br />Develop a strong foundational understanding of generative AI techniques and their applications in software development.<br />Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.<br />Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.<br />Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.<br />Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.<br />Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.</p><p>Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts.  Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment.</p></div><div id="course-pills-2">Generative AI represents an exhilarating frontier in artificial intelligence, specializing in the creation of new data instances, imitation of real data, and content generation. Its remarkable capabilities facilitate automated content creation, enriched user experiences, and groundbreaking solutions across diverse industries, ultimately fueling efficiency and transcending technological limits. By harnessing the power of generative AI, developers can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations, empowering them to construct highly efficient and custom solutions for a wide range of applications. Designed for experienced programmers, Turbocharge Your Code! Generative AI Boot Camp for Developers is a three-day workshop-style course that teaches you the latest skills and tools required to master generative AI models, transforming the way you approach software development. In today's fast-paced technological landscape, generative AI has emerged as a game-changer, with leading companies like NVIDIA, OpenAI, and Google leveraging its capabilities to push the boundaries of innovation. By learning how to harness the power of generative models such as GANs, VAEs, and Transformer models, you will be able to generate code, documentation, and tests, enhance user interfaces, and create dynamic content that adapts to user needs. Our comprehensive curriculum covers everything from the fundamentals of generative AI to advanced techniques and ethical considerations, including hands-on labs where you will develop and deploy custom models using state-of-the-art AI tools and libraries like TensorFlow and Hugging Face Transformers. Throughout the course you'll focus on practical application and collaboration, building confidence with personalized guidance and real-time feedback from our expert live instructor. Upon completion, you will be equipped with the knowledge and experience necessary to develop and implement innovative generative AI models across various industries, improving existing products, creating new applications, and gaining highly-valuable skills in the rapidly advancing field of AI.</div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Generative Design

AI Art Generation

Generative Adversarial Networks (GAN)

AI (Artificial Intelligence)

Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is appropriate for advanced users, system administrators and web site administrators who want to use Python to support their server installations, as well as anyone else who wants to automate or simplify common tasks with the use of Python scripts. Students can apply the course skills to use Python in basic web development projects or automate or simplify common tasks with the use of Python scripts.</p>Overview<p>This skills-focused course is about 50% hands-on lab to lecture ratio, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Working in a hands-on learning environment led by our expert instructor, you'll learn how to:<br />Create working Python scripts following best practices<br />Use python data types appropriately<br />Read and write files with both text and binary data<br />Search and replace text with regular expressions<br />Work with with the standard library and its work-saving modules<br />Create 'real-world', professional Python applications<br />Know when to use collections such as lists, dictionaries, and sets<br />Work with Pythonic features such as comprehensions and iterators<br />Write robust code using exception handling</p><p>Introduction to Python Programming Basics is a hands-on Python programming course that teaches you the key skills you?ll need to get started with programming in Python to a solid foundational level.  The start of the course will lead you through writing and running basic Python scripts, and then guide you through how to use more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of  Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting. This course provides you with an excellent kick start for users new to Python and scripting, enabling you to quickly use basic Python skills on the job in a variety of ways.  You?ll be able use Python in basic web development projects, or use it to automate or simplify common tasks with the use of Python scripts. The course also serves as a solid primer course / foundation for continued Python study in support for next level web development with Python, using Python in DevOps, Python for data science / machine learning or Python for systems admin or networking support.</p></div><div id="course-pills-2"><h4>Python Quick View</h4><ul><li>What is Python?</li><li>Python timeline</li><li>Advantages/disadvantages</li><li>Installing Python</li><li>Getting help</li></ul><h4>The Python Environment</h4><ul><li>Starting Python</li><li>Using the interpreter</li><li>Running a Python script</li><li>Editors and IDEs</li></ul><h4>Getting Started with Python</h4><ul><li>Using variables</li><li>Builtin functions</li><li>String data</li><li>Numberic data</li><li>Converting types</li><li>Console input/output</li><li>Command line parameters</li></ul><h4>Flow Control</h4><ul><li>About flow control</li><li>The if statement</li><li>Relational and Boolean operators</li><li>while loops</li><li>Exiting from loops</li></ul><h4>Array Types</h4><ul><li>About array types</li><li>Lists and list methods</li><li>Tuples</li><li>Indexing and slicing</li><li>Iterating through a sequence</li><li>Sequence functions, keywords, and operators</li><li>List comprehensions and generators</li></ul><h4>Working with Files</h4><ul><li>File overview</li><li>Opening a text file</li><li>Reading a text file</li><li>Writing to a text file</li></ul><h4>Dictionaries and Sets</h4><ul><li>About dictionaries</li><li>Creating dictionaries</li><li>Iterating through a dictionary</li><li>About sets</li><li>Creating sets</li><li>Working with sets</li></ul><h4>Functions</h4><ul><li>Defining functions</li><li>Returning values</li><li>Parameters and arguments</li><li>Variable scope</li></ul><h4>Sorting</h4><ul><li>The sorted() function</li><li>Custom sort keys</li><li>Lambda functions</li><li>Sorting in reverse</li><li>Using min() and max()</li></ul><h4>Errors and Exception Handling</h4><ul><li>Exceptions</li><li>Using try/catch/else/finally</li><li>Handling multiple exceptions</li><li>Ignoring exceptions</li></ul><h4>Modules and Packages</h4><ul><li>Creating Modules</li><li>The import statement</li><li>Module search path</li><li>Using packages</li><li>Function and module aliases</li></ul><h4>Getting Started with Object Oriented Programming and Classes</h4><ul><li>About object-oriented programming</li><li>Defining classes</li><li>Constructors</li><li>Understanding self</li><li>Properties</li><li>Instance Methods and data</li><li>Class methods and data</li><li>Inheritance</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Introduction to Python Programming Basics (TTPS4800) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Introduction to Python Programming Basics (TTPS4800) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Computing Skills

Programming Fundamentals

Computer Programming

Computer Skills

Python Scripting

Introduction to Python Programming Basics (TTPS4800)

OnlinePMCourses

Artificial Intelligence is here.

What does it mean for Project Management and Project Managers?
I delivered a special live briefing on 1 November 2022, to answer the questions I was asking:

🤖 What is Artificial Intelligence?

🦾 What is the role of AI in Project Management?

⚠️ What are the issues surrounding AI?

🧩 What do Project Managers need to do to prepare ourselves?

⚖️ And ended with my assessment of Artificial Intelligence in Project Management

Artificial Intelligence & Project Management

Master the Skills Now

<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

Packt

Data Science

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

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Machine Learning - Use in Flutter

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

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

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

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

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

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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