Medway

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Overview</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is an 8-hour course aimed to help you learn what is meant by Data Science, what it involves and how it is often done, then to complete a small practical project to use some of the techniques and to get an idea of whether this is something that you might want to take further.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How you will learn</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will be coached and mentored to complete the<em fr-original-style="" style="box-sizing: border-box;"> CISCO Skills for All </em>online course<em fr-original-style="" style="box-sizing: border-box;"> "Introduction to Data Science"</em> over a series of 3 two-hour, one per week. sessions with an opportunity to continue the learning in between sessions.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each week there will be an hour long workshop introducing a data tool (spreadsheet, visualisation, story-telling) and giving you practical exercises to do.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will choose a data set that interests you and, in an final 2 hour session, you will complete a small practical project with your chosen dataset and one or more of the tools introduced in previous weeks.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Topics</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will find a list of topics covered in the online course here</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Achievements</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will earn CISCO badges</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Introduction to Data Science</li><li fr-original-style="" style="box-sizing: border-box;">Basics of Analytics</li><li fr-original-style="" style="box-sizing: border-box;">Data Collection</li><li fr-original-style="" style="box-sizing: border-box;">Artificial Intelligence and Machine Learning Concepts</li><li fr-original-style="" style="box-sizing: border-box;">Data Careers</li></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">and a statement of achievement from futureCodersSE CIC, with a shared link to your project to add to your CV or LinkedIn profile.</span></div><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div>

futureCoders SE

Data Storytelling

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.

Introduction to Data Science - free for Medway residents

General admission

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course does not have any technical knowledge prerequisites for the learners, besides being proficient in using a computer and the Internet. IT and/or AI knowledge is a benefit but not a hard requirement. Given the rapid development of AI and the broad range of its applications in everyday life, it is crucial for anyone to attend this course to update their digital skills in an ever-changing world. It is expected that all learners have registered for a free account of OpenAI ChatGPT at https://chat.openai.com.</p>Overview<p>Discover how AI relates to other 4th industrial revolution technologies<br />Learn about AI, ML, and associated cognitive services<br />Overview of AI development frameworks, tools and services<br />Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail</p><p>The core aim of this ?AI for beginners? course is to introduce its audience to Artificial Intelligence (AI) and Machine Learning (ML) technologies and allow them to understand the practical applications of AI in their everyday personal and professional life. Moreover, the course aims to provide a handful of demos and hands-on exercises to allow the learners to familiarize themselves with usage scenarios of OpenAI ChatGPT and other Generative AI (GenAI) models. The content of this course has been created primarily by using the OpenAI ChatGPT model.</p></div><div id="course-pills-2"><h4>AI theoretical concepts.</h4><ul><li>Introduction to AI, ML, and associated cognitive services (Computer vision, Natural language processing, Speech analysis, Decision making).</li><li>How AI relates to other 4th industrial revolution technologies (cloud computing, edge computing, internet of things, blockchain, metaverse, robotics, quantum computing).</li><li>AI model classification by utilizing mind maps and the distinctive role of Gen AI models.</li><li>Introduction to the OpenAI ChatGPT model and alternative generative AI models. Familiarization with the basics of the ChatGPT interface (https://chat.openai.com). </li><li>Talking about Responsible AI: Security, privacy, compliance, copyright, legal challenges, and ethical implications.</li></ul><h4>AI practical applications</h4><ul><li>Overview of AI development frameworks, tools and services. AI aggregators review.</li><li>Hand-picked AI tool demos:</li><li>a.Workplace productivity and the case of Microsoft 365 Copilot.</li><li>b.The content creation industry. Create text, code, images, audio and video with Gen AI.</li><li>c.Redefining the education sector with AI-powered learning.</li><li>Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail:</li><li>a.Prompting and plugin demos.</li><li>b.Code interpreter demos.</li><li>Closing words. Discussion with an AI model on the future of AI.</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI for beginners training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI for beginners course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Generative AI

NLP Practitioner

AI Art Generation

Trainers Course

Microsoft AI-900

AI (Artificial Intelligence)

AI for beginners

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems.<br />A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.</p>Overview<p>In this course, you will use R to perform common data science tasks.You will:<br />Set up an R development environment and execute simple code.<br />Perform operations on atomic data types in R, including characters, numbers, and logicals.<br />Perform operations on data structures in R, including vectors, lists, and data frames.<br />Write conditional statements and loops.<br />Structure code for reuse with functions and packages.<br />Manage data by loading and saving datasets, manipulating data frames, and more.<br />Analyze data through exploratory analysis, statistical analysis, and more.<br />Create and format data visualizations using base R and ggplot2.<br />Create simple statistical models from data.</p><p>In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable.<br /><br />This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.</p></div><div id="course-pills-2"><h4>Lesson 1: Setting Up R and Executing Simple Code</h4><ul><li>Topic A: Set Up the R Development Environment</li><li>Topic B: Write R Statements</li></ul><h4>Lesson 2: Processing Atomic Data Types</h4><ul><li>Topic A: Process Characters</li><li>Topic B: Process Numbers</li><li>Topic C: Process Logicals</li></ul><h4>Lesson 3: Processing Data Structures</h4><ul><li>Topic A: Process Vectors</li><li>Topic B: Process Factors</li><li>Topic C: Process Data Frames</li><li>Topic D: Subset Data Structures</li></ul><h4>Lesson 4: Writing Conditional Statements and Loops</h4><ul><li>Topic A: Write Conditional Statements</li><li>Topic B: Write Loops</li></ul><h4>Lesson 5: Structuring Code for Reuse</h4><ul><li>Topic A: Define and Call Functions</li><li>Topic B: Apply Loop Functions</li><li>Topic C: Manage R Packages</li></ul><h4>Lesson 6: Managing Data in R</h4><ul><li>Topic A: Load Data</li><li>Topic B: Save Data</li><li>Topic C: Manipulate Data Frames Using Base R</li><li>Topic D: Manipulate Data Frames Using dplyr</li><li>Topic E: Handle Dates and Times</li></ul><h4>Lesson 7: Analyzing Data in R</h4><ul><li>Topic A: Examine Data</li><li>Topic B: Explore the Underlying Distribution of Data</li><li>Topic C: Identify Missing Values</li></ul><h4>Lesson 8: Visualizing Data in R</h4><ul><li>Topic A: Plot Data Using Base R Functions</li><li>Topic B: Plot Data Using ggplot2</li><li>Topic C: Format Plots in ggplot2</li><li>Topic D: Create Combination Plots</li></ul><h4>Lesson 9: Modeling Data in R</h4><ul><li>Topic A: Create Statistical Models in R</li><li>Topic B: Create Machine Learning Models in R</li></ul></div>

Online Course Creation

Data Engineering

R (programming language)

Data Science

Statistical Computing

Data Skills

R Programming for Data Science (v1.0)

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			<h2>Overview
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					<p><span>The demand for skilled cybersecurity professionals is soaring in today's digital landscape. </span><b>The CompTIA CySA+ Cybersecurity Analyst (CS0-002) course is your gateway to a lucrative and rewarding career in this high-demand industry.</b></p><p><span>This course delves deep into various aspects of cybersecurity, </span><b>from threat analysis and vulnerability identification to incident response and digital forensics. It's designed to ensure you're ready to excel in the field.</b><span> This course covers various</span><b> topics, including threat intelligence, vulnerability identification, incident response, and forensics analysis. </b></p><p><b>With 60+ hours of engaging content, our expert instructors will equip you with the knowledge and skills required to excel in the CompTIA CySA+ certification exam and kickstart your career in cybersecurity.</b></p><p><span>Enrol in the CompTIA CySA+ Cybersecurity Analyst (CS0-002) course today and secure your future in this high-demand industry!</span></p>					</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>CompTIA CySA+ Cybersecurity Analyst (CS0-002)</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>CompTIA CySA+ Cybersecurity Analyst (CS0-002) </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>22 sections • 96 lectures • 11:35:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction: 00:02:00</p></div><div><div><p>•All about the Exam: 00:08:00</p></div><div><div><p>•What's New on the CompTIA CySA+ Exam?: 00:05:00</p></div><div><div><p>•Meet the Instructors: 00:02:00</p></div><div><div><p>•Thinking like the Enemy: 00:09:00</p></div><div><div><p>•Tools of the Trade: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intelligence Sources and Confidence Levels: 00:08:00</p></div><div><div><p>•Threat Indicators and Actors: 00:08:00</p></div><div><div><p>•Threat Trends: 00:07:00</p></div><div><div><p>•Intelligence Cycle and ISACs: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Attack Frameworks: 00:06:00</p></div><div><div><p>•Threat Research: 00:11:00</p></div><div><div><p>•Threat Modeling and Intelligence Sharing: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Vulnerability Identification: 00:07:00</p></div><div><div><p>•Scanning Parameters and Criteria: 00:09:00</p></div><div><div><p>•Scanning Special Considerations: 00:06:00</p></div><div><div><p>•Validation: 00:03:00</p></div><div><div><p>•Remediation and Mitigation: 00:08:00</p></div><div><div><p>•Inhibitors to Remediation: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Web Applications Scanners, Part 1: 00:10:00</p></div><div><div><p>•Web Applications Scanners, Part 2: 00:05:00</p></div><div><div><p>•Scanning: 00:06:00</p></div><div><div><p>•Configuring and Executing Scans: 00:08:00</p></div><div><div><p>•Vulnerability Scanning: 00:10:00</p></div><div><div><p>•Reverse Engineering: 00:08:00</p></div><div><div><p>•Enumeration: 00:06:00</p></div><div><div><p>•Wireless Assessment Tools: 00:08:00</p></div><div><div><p>•Cloud Assessment Tools: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Mobile and IoT: 00:10:00</p></div><div><div><p>•Embedded and Firmware Systems (RTOS, SoC, and FPGA): 00:09:00</p></div><div><div><p>•Access and Vehicles Risk: 00:08:00</p></div><div><div><p>•Automation and Control Risk: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Cloud Models: 00:07:00</p></div><div><div><p>•Remote Service Invocation (FaaS, IaC, API): 00:10:00</p></div><div><div><p>•Cloud Vulnerabilities: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Injection and Overflow Attacks: 00:09:00</p></div><div><div><p>•Injection and Overflow Attacks: 00:09:00</p></div><div><div><p>•Exploits: 00:08:00</p></div><div><div><p>•Application Vulnerabilities, Part 1: 00:08:00</p></div><div><div><p>•Application Vulnerabilities, Part 2: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Network Architecture and Asset Management: 00:09:00</p></div><div><div><p>•Protecting Your Territory: 00:05:00</p></div><div><div><p>•Identity and Access Management: 00:11:00</p></div><div><div><p>•Encryption and Active Defense: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Platforms: 00:07:00</p></div><div><div><p>•SOA and DevSecOps: 00:09:00</p></div><div><div><p>•Secure Software Development: 00:08:00</p></div><div><div><p>•Best Coding Practices: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Trusted Hardware: 00:10:00</p></div><div><div><p>•Hardware Encryption: 00:04:00</p></div><div><div><p>•Hardware Security: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Analytics: 00:10:00</p></div><div><div><p>•Endpoint Security: 00:08:00</p></div><div><div><p>•Recon Results, Part 1: 00:13:00</p></div><div><div><p>•Recon Results, Part 2: 00:05:00</p></div><div><div><p>•Impact Analysis: 00:05:00</p></div><div><div><p>•Collective Tools: 00:09:00</p></div><div><div><p>•Query Writing: 00:07:00</p></div><div><div><p>•E-mail Analysis, Part 1: 00:10:00</p></div><div><div><p>•E-mail Analysis, Part 2: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Permissions: 00:09:00</p></div><div><div><p>•Firewalls: 00:08:00</p></div><div><div><p>•Intrusion Prevention Rules: 00:05:00</p></div><div><div><p>•DLP and Endpoint Detection: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Threat Hunting and the Hypothesis: 00:06:00</p></div><div><div><p>•Threat Hunting Process: 00:07:00</p></div><div><div><p>•Results and Benefits: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Workflow and Scripting: 00:07:00</p></div><div><div><p>•API and Malware Signature Creation: 00:08:00</p></div><div><div><p>•Threat Feeds and Machine Learning: 00:06:00</p></div><div><div><p>•Protocols, Standards, and Software Engineering: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•IR Roles and Responsibilities: 00:08:00</p></div><div><div><p>•IR Active Preparation: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Incident Response Process: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Network Symptoms: 00:04:00</p></div><div><div><p>•Host Symptoms: 00:08:00</p></div><div><div><p>•Application Symptoms: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Digital Forensics: 00:10:00</p></div><div><div><p>•Seizure and Acquisitions: 00:05:00</p></div><div><div><p>•Forensics Acquisition Tools: 00:09:00</p></div><div><div><p>•Mobile, Virtualization, and Cloud: 00:06:00</p></div><div><div><p>•Forensics Analysis, Part 1: 00:04:00</p></div><div><div><p>•Forensics Analysis, Part 2: 00:08:00</p></div><div><div><p>•Packet Capture: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Privacy and Security: 00:06:00</p></div><div><div><p>•Nontechnical Controls: 00:09:00</p></div><div><div><p>•Technical Controls: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Business Impact Analysis: 00:05:00</p></div><div><div><p>•Risk Identification: 00:05:00</p></div><div><div><p>•Risk Calculation and Communication: 00:06:00</p></div><div><div><p>•Training: 00:04:00</p></div><div><div><p>•Supply Chain Assessment: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Frameworks: 00:13:00</p></div><div><div><p>•Policies and Procedures: 00:05:00</p></div><div><div><p>•Controls and Procedures: 00:08:00</p></div><div><div><p>•Verification: 00:06:00</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>

Apex Learning

Certified in Cybersecurity (CC)

Certificate in Information Security Management Principles (CISMP)

Cyber Security

Cyber Security Foundation

CompTIA A+

CompTIA Security+

CompTIA CySA+ Cybersecurity Analyst (CS0-002)

General Admission

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.</p>Overview<p>By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.</p><p>The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.</p></div><div id="course-pills-2"><h4>Data Preparation and Cleaning</h4><ul><li>Data Models and Structured Data</li><li>pandas</li><li>Data Manipulation</li></ul><h4>Data Exploration and Visualization</h4><ul><li>Identifying the Right Attributes</li><li>Generating Targeted Insights</li><li>Visualizing Data</li></ul><h4>Unsupervised Learning: Customer Segmentation</h4><ul><li>Customer Segmentation Methods</li><li>Similarity and Data Standardization</li><li>k-means Clustering</li></ul><h4>Choosing the Best Segmentation Approach</h4><ul><li>Choosing the Number of Clusters</li><li>Different Methods of Clustering</li><li>Evaluating Clustering</li></ul><h4>Predicting Customer Revenue Using Linear Regression </h4><ul><li>Understanding Regression</li><li>Feature Engineering for Regression</li><li>Performing and Interpreting Linear Regression</li></ul><h4>Other Regression Techniques and Tools for Evaluation</h4><ul><li>Evaluating the Accuracy of a Regression Model</li><li>Using Regularization for Feature Selection</li><li>Tree-Based Regression Models</li></ul><h4>Supervised Learning: Predicting Customer Churn</h4><ul><li>Classification Problems</li><li>Understanding Logistic Regression</li><li>Creating a Data Science Pipeline</li></ul><h4>Fine-Tuning Classification Algorithms</h4><ul><li>Support Vector Machine</li><li>Decision Trees</li><li>Random Forest</li><li>Preprocessing Data for Machine Learning Models</li><li>Model Evaluation</li><li>Performance Metrics</li></ul><h4>Modeling Customer Choice</h4><ul><li>Understanding Multiclass Classification</li><li>Class Imbalanced Data</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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>

Online Course Marketing

Big Data Analytics

Marketing Analytics

Data Science for Marketing Analytics

<p>Embark on a transformative journey into the realm of programming with our <strong>Intermediate Python Coding</strong> course. Picture yourself delving deeper into the world of Python, a language known for its versatility and efficiency. This course begins with a refresher introduction, setting a solid foundation before advancing to more complex concepts. It's designed not just to teach but to immerse you in the intricacies of Python. From understanding the fundamentals of classes and methods to unraveling the complexities of Object-Oriented Programming (OOP), each section is a step towards mastering this powerful programming language. Whether you're looking to enhance your coding skills for professional growth or personal satisfaction, this course bridges the gap between basic understanding and advanced proficiency.</p>
<p>As you progress, you'll explore the sophisticated elements of Python, including inheritance, polymorphism, encapsulation, and abstraction. These concepts are not just taught theoretically; you'll see them come to life through practical applications, especially in the creation of Python games. This hands-on approach ensures that you're not just learning concepts but also applying them in real-world scenarios. The course also delves into Python's extensive libraries as you learn about modules, packages, and data handling with Pandas. Completing the course with error and exception handling, you emerge not just as someone who can code but as a problem-solver who can navigate through challenges and create efficient, elegant solutions.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Gain a deeper understanding of Python classes, methods, and OOP principles.</li>
<li>Develop skills in implementing inheritance, polymorphism, encapsulation, and abstraction in Python.</li>
<li>Create interactive Python games and applications to apply coding skills practically.</li>
<li>Learn to manage and utilise Python modules, packages, and the Pandas library.</li>
<li>Master error and exception handling in Python for robust coding.</li>
</ul>
<h3><b>Why choose this Intermediate Python Coding<span> course</span>?</b></h3>
<ol>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b> and <b>CIQ </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
<li>Unlock career resources for CV improvement, interview readiness, and job success.</li>
</ol>
<h3><b>Who is this Intermediate Python Coding<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Programmers looking to advance from basic to intermediate Python skills.</li>
<li>Computer science students seeking a deeper understanding of Python.</li>
<li>Software developers aiming to enhance their proficiency in Python.</li>
<li>Data analysts and scientists interested in leveraging Python's capabilities.</li>
<li>Hobbyists and tech enthusiasts keen on developing Python applications.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Software Developer</strong>: £30,000 - £60,000</li>
<li><strong>Data Analyst</strong>: £25,000 - £50,000</li>
<li><strong>Python Developer</strong>: £28,000 - £55,000</li>
<li><strong>Machine Learning Engineer</strong>: £32,000 - £70,000</li>
<li><strong>Data Scientist</strong>: £35,000 - £75,000</li>
<li><strong>Back-end Developer</strong>: £27,000 - £53,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Beginner to Intermediate Python Coding</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Beginner to Intermediate Python Coding</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Section 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Course Introduction</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Course Curriculum</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>How to get Pre-requisites</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started on Windows, Linux or Mac</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>How to ask Great Questions</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: Class</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Class</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create a Class</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Calling a Class Object</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Class Parameters - Objects</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Access Modifiers(theory)</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Methods</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to methods</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create a method</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Method with parameters</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Method default parameter</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Multiple parameters.</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Method return keyword.</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Method Overloading.</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: OOPs Object-Oriented Programming</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to OOPs</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classes and Objects</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Class Constructors</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assessment Test1</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Solution for Assessment Test1</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: Inheritance and Polymorphism</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Inheritance</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getter and Setter Methods</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polymorphism</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assessment Test2</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Solution for Assessment Test2</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Encapsulation and Abstraction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Access Modifiers (public, protected, private)</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Encapsulation</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Abstraction</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 07: Python Games for Intermediate</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dice Game</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Card and Deck Game Playing</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: Modules and Packages</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>PIP command installations</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Modules</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Naming Module</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Built-in Modules</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List Packages</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 09: Working Files with Pandas</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>Reading CSV files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Writing CSV files</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 10: Error and ExceptionHandling</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Errors - Types of Errors</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Try - ExceptExceptions Handling</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating User-Defined Message</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Try-Except-FinallyBlocks</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Beginner to Intermediate Python Coding

Packt

Web Development

Python

Learn Python with 200+ real-world examples implemented on PyCharm. Ace functions, classes, objects, dictionary, lists, sets, and tuples. The course also covers math, statistics, and random modules. This is the ideal course for you if you want to advance your Python programming skills or switch to Python programming.

The Complete Python Course (2024)

Are you someone wishing to learn the skills to analyze data professionally using Power BI? Do you want to learn to create impressive reports? Then this is the right course for you! Learn the complete workflow in Power BI from A-to-Z and advanced analytics with DAX-calculated columns and measures, forecasting, and advanced analytics.

Microsoft Power BI - The Complete Masterclass [2023 EDITION]

Java

Arduino

Start your journey with the amazing Arduino development platform

Crazy about Arduino: Your End-to-End Workshop - Level 1

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p></p><p><span>This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3.</span></p><p></p>Overview<p></p><p><span>In this course, you will expand your Python proficiencies. You will:</span></p><br /><p><span>Select an object-oriented programming approach for Python applications.</span></p><br /><p><span>Create object-oriented Python applications.</span></p><br /><p><span>Create a desktop application.</span></p><br /><p><span>Create a data-driven application.</span></p><br /><p><span>Create and secure web service-connected applications.</span></p><br /><p><span>Program Python for data science.</span></p><br /><p><span>Implement unit testing and exception handling.</span></p><br /><p><span>Package an application for distribution.</span></p><br /><p><span> </span></p><p></p><p>Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications.</p></div><div id="course-pills-2"><h4>Selecting an Object-Oriented Programming Approach for Python Applications</h4><ul><li>Topic A: Implement Object-Oriented Design</li><li>Topic B: Leverage the Benefits of Object-Oriented Programming</li><li></li></ul><h4>Creating Object-Oriented Python Applications</h4><ul><li>Topic A: Create a Class</li><li>Topic B: Use Built-in Methods</li><li>Topic C: Implement the Factory Design Pattern</li><li></li></ul><h4>Creating a Desktop Application</h4><ul><li>Topic A: Design a Graphical User Interface (GUI)</li><li>Topic B: Create Interactive Applications</li><li></li></ul><h4>Creating Data-Driven Applications</h4><ul><li>Topic A: Connect to Data</li><li>Topic B: Store, Update, and Delete Data in a Database</li><li></li></ul><h4>Creating and Securing a Web Service-Connected App</h4><ul><li>Topic A: Select a Network Application Protocol</li><li>Topic B: Create a RESTful Web Service</li><li>Topic C: Create a Web Service Client</li><li>Topic D: Secure Connected Applications</li><li></li></ul><h4>Programming Python for Data Science</h4><ul><li>Topic A: Clean Data with Python</li><li>Topic B: Visualize Data with Python</li><li>Topic C: Perform Linear Regression with Machine Learning</li><li></li></ul><h4>Implementing Unit Testing and Exception Handling</h4><ul><li>Topic A: Handle Exceptions</li><li>Topic B: Write a Unit Test</li><li>Topic C: Execute a Unit Test</li><li></li></ul><h4>Packaging an Application for Distribution</h4><ul><li>Topic A: Create and Install a Package</li><li>Topic B: Generate Alternative Distribution Files</li><li></li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Advanced Programming Techniques with Python 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 Advanced Programming Techniques with Python 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>

Computer Programming

Professional Skills Course (PSC)

Continuous Professional Development (CPD)

Advanced Programming Techniques with Python

<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>

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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