Name: Remote Sensing in ArcGIS
Price: 100 GBP
Availability: InStock

<p><strong>** 13-in-1 Data Analysis Bundle **</strong></p><p><strong>Unleash Your Full Data Analysis Potential with Our Comprehensive Bundle!</strong></p><p>Are you ready to elevate your data analysis skills to a whole new level? Look no further! Our Data Analysis Bundle is the ultimate solution you've been searching for.</p><p>In today's data-driven world, being proficient in a single aspect of data analysis is just not enough. That's why we've carefully curated a bundle of courses that complement each other seamlessly, creating a holistic and powerful learning experience.</p><p><strong>The Data Analysis Bundle includes the following courses:</strong></p><ol><li>Diploma in Data Analytics with Tableau</li><li>Data Analytics</li><li>Google Data Studio: Data Analytics</li><li>Big Data Analytics with PySpark Power BI and MongoDB</li><li>Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li>Learn MySQL from Scratch for Data Science and Analytics</li><li>SQL for Data Science, Data Analytics and Data Visualization</li><li>Data Analysis In Excel</li><li>Diploma in Data Analysis Fundamentals</li><li>Business Data Analysis</li><li>Data Science &amp; Machine Learning with Python</li><li>R Programming for Data Science</li><li>Learn Python, JavaScript, and Microsoft SQL for Data science</li></ol><p><strong>Course Significant Points:</strong></p><ul><li>Quality Licence Scheme Endorsed</li><li>CPD Accredited Course</li><li>Unlimited Retake Exam &amp; Tutor Support</li><li>Easy Accessibility to the Course Materials</li><li>100% Learning Satisfaction Guarantee</li><li>Lifetime Access &amp; 24/7 Support</li><li>Self-paced online course Modules</li><li>Covers to Explore Multiple Job Positions</li></ul><p><strong>How is the course assessed?</strong></p><p>To assess your learning, you have to complete the assignment questions provided at the end of the course. You have to score at least 60% to pass the exam and to qualify for Quality Licence Scheme endorsed and CPD-accredited certificates. After passing the assignment exam, you will be able to apply for a certificate.</p><p><strong>Meet the Endorsement</strong></p><p>The Quality Licence Scheme has been designed specifically to recognise high-quality courses. In addition, the QLS certificate enriches your CV and recognises your quality study on the relevant subject.</p><p><strong>Meet the Accreditation</strong></p><p>CPD Quality Standards (CPD QS) accreditation assures the Data Analysis with Excel course training and learning activities are relevant, reliable, and up to date.</p><p><strong>**Enrol Today and Unleash Your Full Data Analysis Potential!** </strong></p><p>The Data Analysis Bundle is a comprehensive collection of courses designed to equip individuals with the skills and knowledge necessary for a successful career in data analysis. From foundational concepts to advanced techniques and tools, this bundle covers a wide range of topics in the field of data analysis. Whether you're a beginner looking to get started or a professional seeking to enhance your skills, these courses provide a well-rounded education in data analysis.</p><p><strong>**Diploma in Data Analytics with Tableau**</strong></p><ul><li>Introduction To The Course</li><li>Project 1: Discount Mart (Sales And Profit Analytics)</li><li>Project 2: Green Destinations (HR Analytics)</li><li>Project 3: Superstore (Sales Agent Tracker)</li><li>Northwind Trade (Shipping Analytics)</li><li>Project 5: Tesla (Stock Price Analytics)</li><li>Bonus: Introduction To Database Concepts</li><li>Tableau Stories</li></ul><p><strong>**Data Analytics**</strong></p><ul><li>Introduction To The World Of Data</li><li>Basics Of Data Analytics</li><li>Statistics For Data Analytics</li><li>Actions Taken In The Data Analysis Process</li><li>Gathering The Right Information</li><li>Storing Data</li><li>Data Mining</li><li>Excel For Data Analytics</li><li>Tools For Data Analytics</li><li>Data-Analytic Thinking</li><li>Data Visualisation That Clearly Describes Insights</li><li>Data Visualisation Tools</li></ul><p><strong>**Google Data Studio: Data Analytics**</strong></p><ul><li>Introduction</li><li>Google Sheets</li><li>Google Data Studio</li></ul><p><strong>**Big Data Analytics with PySpark Tableau Desktop and MongoDB**</strong></p><ul><li>Introduction</li><li>Setup And Installations</li><li>Data Processing With PySpark And MongoDB</li><li>Machine Learning With PySpark And MLlib</li><li>Creating The Data Pipeline Scripts</li><li>Tableau Data Visualization</li><li>Source Code</li></ul><p><strong>**Learn MySQL from Scratch for Data Science and Analytics**</strong></p><ul><li>Getting Started</li><li>SQL Server Setting Up</li><li>SQL Database Basics</li><li>SQL DML (Data Manipulation Language)</li><li>SQL DDL (Data Definition Language)</li><li>SQL DCL (Data Control Language)</li><li>SQL Statement Basic</li><li>Filtering Data Rows</li><li>Aggregate Functions For Data Analysis</li><li>SQL Data Analyticstatements</li><li>SQL Group By Statement</li><li>JOINS</li><li>SQL Constraints</li><li>Views</li><li>Advanced SQL Functions</li><li>SQL Stored Procedures</li><li>Import &amp; Export Data</li><li>Backup And Restore Database</li></ul><p><strong>**Data Analysis In Excel**</strong></p><ul><li>Modifying A Worksheet</li><li>Working With Lists</li><li>Analyzing Data</li><li>Visualizing Data With Charts</li><li>Using PivotTables And PivotCharts</li><li>Working With Multiple Worksheets And Workbooks</li><li>Using Lookup Functions And Formula Auditing</li><li>Automating Workbook Functionality</li><li>Creating Sparklines And Mapping Data</li><li>Forecasting Data</li></ul><p><strong>**Diploma in Data Analysis Fundamentals**</strong></p><ul><li>Introduction</li><li>Agenda And Principles Of Process Management</li><li>The Voice Of The Process</li><li>Working As One Team For Improvement</li><li>Exercise: The Voice Of The Customer</li><li>Tools For Data Analysis</li><li>The Pareto Chart</li><li>The Histogram</li><li>The Run Chart</li><li>Exercise: Presenting Performance Data</li><li>Understanding Variation</li><li>The Control Chart</li><li>Control Chart Example</li><li>Control Chart Special Cases</li><li>Interpreting The Control Chart</li><li>Control Chart Exercise</li><li>Strategies To Deal With Variation</li><li>Using Data To Drive Improvement</li><li>A Structure For Performance Measurement</li><li>Data Analysis Exercise</li><li>Course Project</li><li>Test Your Understanding</li></ul><p><strong>**Business Data Analysis**</strong></p><ul><li>Introduction To Business Analysis</li><li>Business Environment</li><li>Business Processes</li><li>Business Analysis Planning And Monitoring</li><li>Strategic Analysis And Product Scope</li><li>Solution Evaluation</li><li>Investigation Techniques</li><li>Ratio Analysis</li><li>Stakeholder Analysis And Management</li><li>Process Improvement With Gap Analysis</li><li>Documenting And Managing Requirements</li><li>Business Development And Succession Planning</li><li>Planning &amp; Forecasting Operations</li><li>Business Writing Skills</li></ul><p><strong>**Data Science &amp; Machine Learning with Python**</strong></p><ul><li>Course Overview &amp; Table Of Contents</li><li>Introduction To Machine Learning</li><li>System And Environment Preparation</li><li>Learn Basics Of Python - Assignment<br /><ul><li>Functions</li><li>Data Structures</li><li>NumPy Array</li><li>NumPy Data</li><li>NumPy Arithmetic</li><li>Learn Basics Of Matplotlib</li></ul></li><li>Learn Basics Of Pandas</li><li>Understanding The CSV Data File</li><li>Load And Read CSV Data File Using Python Standard Library<br /><ul><li>Using NumPy</li><li>Using Pandas</li></ul></li><li>Dataset Summary - Peek, Dimensions And Data Types<br /><ul><li>Class Distribution And Data Summary</li><li>Explaining Correlation</li><li>Explaining Skewness - Gaussian And Normal Curve</li></ul></li><li>Dataset Visualization - Using Histograms</li><li>Dataset Visualization - Using Density Plots</li><li>Dataset Visualization - Box And Whisker Plots</li><li>Multivariate Dataset Visualization - Correlation Plots</li><li>Multivariate Dataset Visualization - Scatter Plots</li><li>Data Preparation (Pre-Processing) - Introduction<br /><ul><li>Re-Scaling Data</li><li>Standardizing Data</li><li>Normalizing Data</li><li>Binarizing Data</li></ul></li><li>Feature Selection - Introduction<br /><ul><li>Uni-Variate Part</li><li>Recursive Feature Elimination</li><li>Principal Component Analysis (PCA)</li><li>Feature Importance</li></ul></li><li>Refresher Session - The Mechanism Of Re-Sampling, Training And Testing</li><li>Algorithm Evaluation Techniques - Introduction<br /><ul><li>Train And Test Set</li><li>K-Fold Cross Validation</li><li>Leave One Out Cross Validation</li><li>Repeated Random Test-Train Splits</li></ul></li><li>Algorithm Evaluation Metrics - Introduction<br /><ul><li>Classification Accuracy</li><li>Log Loss</li><li>Area Under ROC Curve</li><li>Confusion Matrix</li><li>Classification Report</li><li>Mean Absolute Error - Dataset Introduction</li><li>Mean Absolute Error</li><li>Mean Square Error</li><li>R Squared</li></ul></li><li>Classification Algorithm Spot Check - Logistic Regression<br /><ul><li>Linear Discriminant Analysis</li><li>K-Nearest Neighbors</li><li>Naive Bayes</li><li>CART</li><li>Support Vector Machines</li></ul></li><li>Regression Algorithm Spot Check - Linear Regression<br /><ul><li>Ridge Regression</li><li>Lasso Linear Regression</li><li>Elastic Net Regression</li><li>K-Nearest Neighbors</li><li>CART</li><li>Support Vector Machines (SVM)</li></ul></li><li>Compare Algorithms</li><li>Pipelines</li><li>Performance Improvement: Ensembles - Voting<br /><ul><li>Bagging</li><li>Boosting</li></ul></li><li>Performance Improvement: Parameter Tuning Using Grid Search</li><li>Performance Improvement: Parameter Tuning Using Random Search</li><li>Export, Save And Load Machine Learning Models : Pickle</li><li>Export, Save And Load Machine Learning Models : Joblib</li><li>Finalizing A Model - Introduction And Steps</li><li>Finalizing A Classification Model - The Pima Indian Diabetes Dataset</li><li>Quick Session: Imbalanced Data Set - Issue Overview And Steps</li><li>Iris Dataset : Finalizing Multi-Class Dataset</li><li>Finalizing A Regression Model - The Boston Housing Price Dataset</li><li>Real-Time Predictions: Using The Pima Indian Diabetes Classification Model</li><li>Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset</li><li>Real-Time Predictions: Using The Boston Housing Regression Model</li></ul><p><strong>**R Programming for Data Science**</strong></p><ul><li>Data Science Overview</li><li>R And RStudio</li><li>Introduction To Basics</li><li>Vectors</li><li>Matrices</li><li>Factors</li><li>Data Frames</li><li>Lists</li><li>Relational Operators</li><li>Logical Operators</li><li>Conditional Statements</li><li>Loops</li><li>Functions</li><li>R Packages</li><li>The Apply Family - Lapply</li><li>The Apply Family - Sapply &amp; Vapply</li><li>Useful Functions</li><li>Regular Expressions</li><li>Dates And Times</li><li>Getting And Cleaning Data</li><li>Plotting Data In R</li><li>Data Manipulation With Dplyr</li></ul><p><strong>**Learn Python, JavaScript, and Microsoft SQL for Data science**</strong></p><ul><li>JavaScript Introduction</li><li>JavaScript Basics</li><li>JavaScript Operators</li><li>JavaScript Conditional Statements</li><li>JavaScript Control Flow Statements</li><li>JavaScript Functions</li><li>JavaScript Error Handling</li><li>JavaScript Client-Side Validations</li><li>Python Introduction</li><li>Python Basic</li><li>Python Strings</li><li>Python Operators</li><li>Python Data Structures</li><li>Python Conditional Statements</li><li>Python Control Flow Statements</li><li>Python Core Games</li><li>Python Functions</li><li>Python Args, KW Args For Data Science</li><li>Python Project</li><li>Python Object Oriented Programming [OOPs]</li><li>Python Methods</li><li>Python Class And Objects</li><li>Python Inheritance And Polymorphism</li><li>Python Encapsulation And Abstraction</li><li>Python OOPs Games</li><li>Python Modules And Packages</li><li>Python Error Handling</li><li>Microsoft SQL[MS] Introduction</li><li>MS SQL Statements</li><li>MS SQL Filtering Data</li><li>MS SQL Functions</li><li>MS SQL Joins</li><li>MS SQL Advanced Commands</li><li>MS SQL Structure And Keys</li><li>MS SQL Queries</li><li>MS SQL Structure Queries</li><li>MS SQL Constraints</li><li>MS SQL Backup And Restore</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><strong>Data Analysis course is ideal for:</strong></p><ul><li>Professionals working in fields such as finance, business, and healthcare who need to analyse data and make decisions based on their findings.</li><li>Students and researchers who want to develop their skills in data analysis and visualisation using Excel.</li><li>Individuals who are looking to pursue a career in data analysis and want to gain a foundational understanding of Excel and its capabilities.</li><li>Business owners and entrepreneurs who want to learn how to use Excel to analyse and interpret data for their own businesses.</li><li>Anyone who wants to improve their data analysis skills for personal or professional purposes and gain proficiency in Excel as a tool for data analysis.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no specific prerequisites to enrol in Data analysis course. <strong>Anyone and everyone</strong> can take this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The course will assist you in establishing a solid understanding of data analysis. After the course, you will be able to explore career options such as:</p><ul><li><strong>Data Analyst </strong>(Average annual salary £35,000)</li><li><strong>Research Analyst</strong> (Average annual salary £28,552)</li><li><strong>Business Analyst</strong> (Average annual salary £42,618)</li><li><strong>Risk Analyst </strong>(Average annual salary £41,340)</li><li><strong>Financial Analyst</strong> (Average annual salary £50,300)</li></ul>
                        </div>

One Education

Data Analyst : Data Analyst Course

General Admission

<p><strong>Do you want to learn software development but don't know where to start? Are you looking for a career change, but don't know how to get your foot in the door? If so, then our Software Development Training program is perfect for you!</strong></p><p>Our comprehensive Software Development Training offers more than just coding skills; we provide you with personalised mentoring, job opportunities, and unwavering support throughout your professional journey.</p><p>At studyhub, we understand that becoming a successful software developer involves more than just learning programming languages. That's why our program goes beyond the basics and focuses on empowering you with the tools, knowledge, and confidence to excel in this ever-evolving field. We will help you until you find a job, so you won't have to worry about a thing. Just gain knowledge and learn skills, getting you a job is our duty.</p><p>We are committed to helping you succeed in your career. That's why we offer our program with a 100% satisfaction guarantee. Don't wait any longer! Enrol in our Software Development Training program today and start your journey to a successful career in software development!</p><p><strong>Our mission is simple:</strong> to be your trusted partner and assist you every step of the way until you land the job of your dreams.<br /><br /></p><p><strong>Why Choose Us</strong></p><p>Here's what makes our Software Development Training Program stand out from the crowd:</p><ul><li><strong>Personalised Job Search Assistance:</strong> We're in this together! Our dedicated team will work tirelessly to support your entire job searching process. From crafting a standout resume to submitting it to top companies, we'll ensure you have a compelling application that gets noticed.</li></ul><ul><li><strong>Expertly Crafted CV:</strong> Your resume is your ticket to the interview room. Our professional resume writers will create a customised CV highlighting your unique skills and experiences. This will maximise your chances of standing out among the competition.</li></ul><ul><li><strong>Strategic Placement:</strong> We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals.</li></ul><ul><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Software Development field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.</li></ul><ul><li><strong>Comprehensive Skill Development:</strong> Our program is designed to equip you with the most sought-after skills in the Software Development sector. From mastering cutting-edge tools to honing your Software Development prowess, we'll ensure you have the knowledge and expertise to excel in any Software-driven environment.</li></ul><ul><li><strong>Ongoing Support:</strong> We understand that the journey to landing your dream job doesn't end with placement. That's why our commitment to your success extends well beyond your initial training. Our support team will be available to answer your questions, provide guidance, and assist you as you progress in your career.</li></ul><p><strong>Here are the 6 courses we will provide once you enrol in the program:</strong></p><ul><li>C# (C-Sharp)</li><li>Refactor Javascript Course</li><li>Python Course</li><li>PHP Web Development with MySQL; GitHub &amp; Heroku</li><li>ASP.Net MVC and Entity Framework Course</li><li>Machine Learning</li></ul><p>These courses will help you to develop your knowledge and skills to become a successful Software Developer.</p> <p>The Program is completed in 5 easy steps:</p><p><strong>Step 1 - Enrol in the program and start learning from the courses we provide</strong></p><p>After you enrol in this Software Development Training Program, you will get lifetime access to 6 premium courses related to Software Development. These courses will teach you the knowledge and skills required to become a successful Software Developer. Our customer service team will help you and keep in contact with you every step of the way.</p><p><strong>Step 2 - Complete your courses and get certifications</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks for the first course and 80% for the rest relevant courses. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 3 - Get a consultation session from a professional Software Developer</strong></p><p>Take your Software Development skills to new heights with a one-on-one consultation session led by a seasoned professional. Gain invaluable insights, expert tips, and tailored advice that will propel your career forward.</p><p><strong>Step 4 - Complete the CV and attend the interview</strong></p><p>Once you've successfully obtained the certifications, our team of professionals will build you a compelling CV and LinkedIn profile. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. Kickstart your Software Development career with a starting salary ranging from <strong>•30,000 to •50,000</strong> annually. Step into the Software Development industry with the assurance of a promising future.</p><p><strong>Step 5 - We will not leave you until you get a job</strong></p><p>Our commitment to your success goes above and beyond. We won't stop until you land that dream job. With personalised support, expert guidance, and unwavering dedication, we'll be by your side until you secure the perfect opportunity. Your job search becomes our mission, ensuring you have the best chance at a successful career in Software Development.</p><p>Don't let your dream career as a Software Developer remain out of reach. You just have to learn and gain knowledge and skills. Finding a job is our task! We will remove most of your worries and help you focus on only developing your skills. So join our Software Development Training Program today, and let us be your trusted partner on your journey to success.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Software Development Training program is ideal for:</p><ul><li>Aspiring software developers seeking comprehensive training and mentorship.</li><li>Career changers seeking to transition into the software development field.</li><li>Recent graduates aiming to enhance their technical skills and improve their employability.</li><li>Professionals in related fields who want to expand their knowledge and delve into software development.</li><li>Individuals passionate about coding and eager to turn their hobby into a fulfilling profession.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completing our program, you'll be equipped to pursue various software development roles in the UK, such as software engineer, web developer, mobile app developer, database administrator, software tester, or software project manager. With the demand for skilled developers constantly on the rise, you'll have ample opportunities to thrive and grow in your chosen career path.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Student ID Card </h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Studyhub UK

Software Engineer

Learning & Development

Online Course Creation

Software Developer

Computing & Software Development Tools

Software Development

Software Development - IT Job Ready Program + Career Support & Money Back Guarantee

<p>AI can create art! There are AI algorithms and models that have been trained to generate artwork, music, and even poetry. Dive deep into the world of AI, starting from the basics and progressing to cutting-edge technologies. From understanding the mathematics that underpin AI to exploring machine learning, deep learning, and real-world applications, this course equips you with the knowledge you need to thrive in this dynamic field. Whether you're a tech enthusiast or a professional looking to enhance your skills, this course is your gateway to a future where AI shapes the world.</p><p><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>FREE PDF + Hardcopy certificate</li><li>Fully online, interactive course</li><li>Self-paced learning and laptop, tablet and smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum </strong></p><ul><li>Module 01 : Introduction to Artificial Intelligence</li><li>Module 02 : Mathematics for AI</li><li>Module 03 : Knowledge Representation in AI - Part 1</li><li>Module 04 : Knowledge Representation in AI - Part 2</li><li>Module 05 : Machine Learning - Part 1</li><li>Module 06 : Machine Learning - Part 2</li><li>Module 07 : Deep Learning</li><li>Module 08 : Natural Language Processing</li><li>Module 09 : Computer Vision</li><li>Module 10 : Robotics</li><li>Module 11 : Building AI Applications</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Grasp the fundamentals of artificial intelligence and its applications.</li><li>Develop a strong mathematical foundation for AI algorithms.</li><li>Master knowledge representation techniques in AI.</li><li>Explore the principles and applications of machine learning.</li><li>Dive into the world of deep learning and its use in AI.</li><li>Understand the core concepts of natural language processing and computer vision.</li></ul><p><strong>Accreditation</strong></p><p>This course is <strong>CPD Quality Standards</strong> (<strong>CPD QS)</strong> accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Technology enthusiasts eager to delve into AI.</li><li>Students pursuing a career in AI and machine learning.</li><li>Professionals seeking to upskill in the AI domain.</li><li>Engineers and programmers interested in AI development.</li><li>Entrepreneurs exploring AI for business solutions.</li><li>Anyone with a curiosity about the future of artificial intelligence.</li><li>Graduates looking to enhance their tech-related knowledge.</li><li>Innovators with an interest in robotics and AI applications.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>AI Research Scientist</li><li>Machine Learning Engineer</li><li>Data Scientist</li><li>Natural Language Processing Engineer</li><li>Computer Vision Specialist</li><li>Robotics Software Engineer</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Training Express

Foundations of Artificial Intelligence Course

Packt

Python

Deep Learning

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

The Complete Self-Driving Car Course - Applied Deep Learning

<h2>Building Data Science Products? Think Business First</h2>
<p>Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.</p>
<p>This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs.</p>
<p>Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.</p>
<p>Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.</p>

IIL Europe Ltd

Building Data Science Products? Think Business First

<strong>This course begins with establishing the motivation for reinforcement learning and then progresses on to equipping you with all the necessary theory. Each section of the course helps you not only understand the fundamentals of RL but also gain necessary coding skills by taking you through exercises. By the end of the course, you will be able to complete a project using the OpenAI Gym toolkit.</strong>
        <p>Although introduced academically decades ago, the recent developments in the field of reinforcement learning have been phenomenal. Domains such as self-driving cars, natural language processing, healthcare industry, online recommender systems, and so on have already seen how RL-based AI agents can bring tremendous gains.

This course will help you get started with reinforcement learning first by establishing the motivation for this field and then covering all the essential topics, such as Markov Decision Processes, policy and rewards, model-free learning, temporal difference learning, and so on. 
Each topic is accompanied by exercises and complementing analysis to help you gain practical and tangible coding skills.

By the end of this course, not only will you have gained the necessary understanding to implement RL in your projects but also implemented an actual Frozenlake project using the OpenAI Gym toolkit. 

All resources and code files are placed here: https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-Explained-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Understand the motivation for reinforcement learning<br>Understand all the elements of a Markov Decision Process<br>Learn how to model uncertainty of the environments<br>Solve Markov Decision Processes<br>Implement temporal difference learning and Q-learning in Python<br>Execute the Frozenlake project using the OpenAI Gym toolkit</p>
        <h2>Audience</h2><p>This course is designed for beginners in the field of data science and machine learning. Anyone who wants to learn RL and apply it in realistic projects would benefit from this course.</p>
        <h2>Approach</h2><p>Through carefully designed modules, simple-to-understand theory, engaging hands-on exercises, and realistic implementations of Reinforcement Learning (RL) in projects, this course will help you master RL. This course covers the most advanced and up-to-date methods in RL.</p>
        <h2>Key Features</h2><p>Gain an understanding of all theoretical concepts related to reinforcement learning * Master learning models such as model-free learning, Q-learning, temporal difference learning  * Model the uncertainty of the environment, environment stochastic policies, and environment value functions</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-Explained-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of 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 Course and Instructor</h4><p></p><br><h4>2. Motivation Reinforcement Learning</h4><p></p><br><h4>3. Terminology  of Reinforcement Learning</h4><p></p><br><h4>4. GridWorld Example</h4><p></p><br><h4>5. Markov Decision Process Prerequisites</h4><p></p><br><h4>6. Elements of Markov Decision Process</h4><p></p><br><h4>7. More on Reward</h4><p></p><br><h4>8. Solving MDP</h4><p></p><br><h4>9. Value Approximation</h4><p></p><br><h4>10. Temporal Differencing-Q Learning</h4><p></p><br><h4>11. TD Lambda</h4><p></p><br><h4>12. Project Frozenlake (Open AI Gym)</h4><p></p>

This course begins with establishing the motivation for reinforcement learning and then progresses on to equipping you with all the necessary theory. Each section of the course helps you not only understand the fundamentals of RL but also gain necessary coding skills by taking you through exercises. By the end of the course, you will be able to complete a project using the OpenAI Gym toolkit.

Reinforcement Learning with Python Explained for Beginners

<strong>Pre-process and Analyze Satellite Remote Sensing Data with Free Software</strong>
        <p>Enroll in my latest course on how to learn all about Basic Satellite Remote Sensing or perhaps you have prior experiences in GIS or tools like R and QGIS? You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain proficiency in satellite remote sensing data analysis.

This course is hands-on training with real remote sensing data with open source tools! This course provides a foundation to carry out practical, real-life remote sensing analysis tasks in popular and free software frameworks with real spatial data. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis. Why Should You Take My Course? The author is an Oxford University MPhil (Geography and Environment), graduate. She also completed a Ph.D. at Cambridge University (Tropical Ecology and Conservation). She has several years of experience in analyzing real-life spatial remote sensing data from different sources and producing publications for international peer-reviewed journals. 

In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer. This course will ensure you learn and put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis. Remote sensing software tools are very expensive, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using a number of popular, open-source GIS tools such as R, QGIS, GRASS, and ESA-SNAP.  All of which are in great demand in the geospatial sector and improving your skills in these is a plus for you. You will also learn about the different sources of remote sensing data there are and how to obtain these free of charge and process them using free software.

All the code and supporting files for this course are available at -
https://github.com/PacktPublishing/Satellite-Remote-Sensing-Data-Bootcamp-with-Opensource-Tools</p>
        <h2>What You Will Learn</h2><p>Download different types of satellite remote sensing data for free<br>Have a thorough knowledge of remote sensing- theoretical concepts and applications<br>Implement pre-processing techniques using R and QGIS<br>Carry out the unsupervised classification of satellite remote sensing data<br>Carry out the supervised classification of satellite remote sensing data<br>Implement machine learning algorithms on satellite remote sensing data in R<br>Carry out habitat suitability mapping using remote sensing and machine learning<br>Use other freely available software tools such as Google Earth Engine and SNAP for RS data analysis</p>
        <h2>Audience</h2><p>This course is for people with prior experience of working spatial data such as GIS analysts, Ecologists, Forestry and Conservation Practioners, Geographers, and Geologists.</p>
        <h2>Approach</h2><p>This is an introductory course, i.e. we will focus on learning the most important and widely encountered remote sensing data processing and analyzing tasks in R, QGIS, GRASS and ESA-SNAP.</p>
        <h2>Key Features</h2><p>A good starter course to explain the fundamentals & properly understand the applications of remote sensing * Short lectures and practicals which are good to ensure the attention of the audience is still there.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/satellite-remote-sensing-data-bootcamp-with-opensource-tools</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Satellite Remote Sensing Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to the Course and Instructor</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Introduction to the Course and Instructor</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Remote Sensing?</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: What is Remote Sensing?</p></td></tr><tr><td style='padding: 15px;'><p>3. Different Types of Remote Sensing Data</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Different Types of Remote Sensing Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Different Tools for Working with Remote Sensing-Start with R and QGIS</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Different Tools for Working with Remote Sensing-Start with R and QGIS</p></td></tr><tr><td style='padding: 15px;'><p>5. Get Started with SNAP Toolbox-Brief Introduction</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Get Started with SNAP Toolbox-Brief Introduction</p></td></tr><tr><td style='padding: 15px;'><p>6. Get Started with GRASS GIS-Brief Introduction</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Get Started with GRASS GIS-Brief Introduction</p></td></tr><tr><td style='padding: 15px;'><p>7. Conclusions to Section 1</p><p style='margin: 0'>Introduction to Satellite Remote Sensing Data Analysis: Conclusions to Section 1</p></td></tr></tbody></table><br><h4>2. Introduction to Optical Remote Sensing Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Principles Behind Collection of Optical Remote Sensing Data</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Principles Behind Collection of Optical Remote Sensing Data</p></td></tr><tr><td style='padding: 15px;'><p>2. Different Types of Optical Remote Sensing Data</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Different Types of Optical Remote Sensing Data</p></td></tr><tr><td style='padding: 15px;'><p>3. Downloading and Viewing Landsat Data</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Downloading and Viewing Landsat Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Different Landsat Sensors</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Different Landsat Sensors</p></td></tr><tr><td style='padding: 15px;'><p>5. Downloading and Viewing Optical Data via QGIS</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Downloading and Viewing Optical Data via QGIS</p></td></tr><tr><td style='padding: 15px;'><p>6. Conclusions to Section 2</p><p style='margin: 0'>Introduction to Optical Remote Sensing Data: Conclusions to Section 2</p></td></tr></tbody></table><br><h4>3. Pre-Processing Optical Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why is Pre-Processing Needed for Optical Data?</p><p style='margin: 0'>Pre-Processing Optical Data: Why is Pre-Processing Needed for Optical Data?</p></td></tr><tr><td style='padding: 15px;'><p>2. Implementing Atmospheric Correction on Landsat Data in R</p><p style='margin: 0'>Pre-Processing Optical Data: Implementing Atmospheric Correction on Landsat Data in R</p></td></tr><tr><td style='padding: 15px;'><p>3. QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin</p><p style='margin: 0'>Pre-Processing Optical Data: QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin</p></td></tr><tr><td style='padding: 15px;'><p>4. Atmospherically Corrected Outputs from QGIS</p><p style='margin: 0'>Pre-Processing Optical Data: Atmospherically Corrected Outputs from QGIS</p></td></tr><tr><td style='padding: 15px;'><p>5. What Can Pre-Processed Satellite Data Be Used For?</p><p style='margin: 0'>Pre-Processing Optical Data: What Can Pre-Processed Satellite Data Be Used For?</p></td></tr><tr><td style='padding: 15px;'><p>6. Conclusions to Section 3</p><p style='margin: 0'>Pre-Processing Optical Data: Conclusions to Section 3</p></td></tr></tbody></table><br><h4>4. The Many Uses of Optical Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Stacking and Unstacking Bands in QGIS</p><p style='margin: 0'>The Many Uses of Optical Data: Stacking and Unstacking Bands in QGIS</p></td></tr><tr><td style='padding: 15px;'><p>2. Band Maths in R and QGIS</p><p style='margin: 0'>The Many Uses of Optical Data: Band Maths in R and QGIS</p></td></tr><tr><td style='padding: 15px;'><p>3. Texture Indices-Theory</p><p style='margin: 0'>The Many Uses of Optical Data: Texture Indices-Theory</p></td></tr><tr><td style='padding: 15px;'><p>4. Texture Indices-GRASS GIS</p><p style='margin: 0'>The Many Uses of Optical Data: Texture Indices-GRASS GIS</p></td></tr><tr><td style='padding: 15px;'><p>5. Texture Indices-ESA SNAP</p><p style='margin: 0'>The Many Uses of Optical Data: Texture Indices-ESA SNAP</p></td></tr><tr><td style='padding: 15px;'><p>6. Tasseled Cap Transformations-theory</p><p style='margin: 0'>The Many Uses of Optical Data: Tasseled Cap Transformations-theory</p></td></tr><tr><td style='padding: 15px;'><p>7. Tasseled Cap Transformations-GRASS GIS</p><p style='margin: 0'>The Many Uses of Optical Data: Tasseled Cap Transformations-GRASS GIS</p></td></tr><tr><td style='padding: 15px;'><p>8. Vegetation Indices in GRASS GIS</p><p style='margin: 0'>The Many Uses of Optical Data: Vegetation Indices in GRASS GIS</p></td></tr><tr><td style='padding: 15px;'><p>9. Vegetation Indices using RStoolbox</p><p style='margin: 0'>The Many Uses of Optical Data: Vegetation Indices using RStoolbox</p></td></tr><tr><td style='padding: 15px;'><p>10. Dimension Reduction-theory</p><p style='margin: 0'>The Many Uses of Optical Data: Dimension Reduction-theory</p></td></tr><tr><td style='padding: 15px;'><p>11. Dimension Reduction-QGIS</p><p style='margin: 0'>The Many Uses of Optical Data: Dimension Reduction-QGIS</p></td></tr><tr><td style='padding: 15px;'><p>12. Dimension Reduction-GRASS GIS</p><p style='margin: 0'>The Many Uses of Optical Data: Dimension Reduction-GRASS GIS</p></td></tr><tr><td style='padding: 15px;'><p>13. Conclusion to Section 4</p><p style='margin: 0'>The Many Uses of Optical Data: Conclusion to Section 4</p></td></tr></tbody></table><br><h4>5. Classification of Remote Sensing Satellite Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Theory of Unsupervised Classification</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Theory of Unsupervised Classification</p></td></tr><tr><td style='padding: 15px;'><p>2. Unsupervised Classification-ESA SNAP</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Unsupervised Classification-ESA SNAP</p></td></tr><tr><td style='padding: 15px;'><p>3. Theory of Supervised Classification</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Theory of Supervised Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Supervised Classification in QGIS: Preliminary Steps</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Supervised Classification in QGIS: Preliminary Steps</p></td></tr><tr><td style='padding: 15px;'><p>5. Classification and Post Classification Accuracy in QGIS</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Classification and Post Classification Accuracy in QGIS</p></td></tr><tr><td style='padding: 15px;'><p>6. Machine Learning Theory</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Machine Learning Theory</p></td></tr><tr><td style='padding: 15px;'><p>7. Create Training Data in QGIS</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Create Training Data in QGIS</p></td></tr><tr><td style='padding: 15px;'><p>8. Apply Machine Learning Techniques on Satellite Data</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Apply Machine Learning Techniques on Satellite Data</p></td></tr><tr><td style='padding: 15px;'><p>9. Conclusion to Section 5</p><p style='margin: 0'>Classification of Remote Sensing Satellite Data: Conclusion to Section 5</p></td></tr></tbody></table><br><h4>6. Introduction to Active Remote Sensing Data: Synthetic Aperture Radar</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Use Active Remote Sensing Data?</p><p style='margin: 0'>Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Why Use Active Remote Sensing Data?</p></td></tr><tr><td style='padding: 15px;'><p>2. Obtain ALOS PALSAR Data</p><p style='margin: 0'>Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Obtain ALOS PALSAR Data</p></td></tr><tr><td style='padding: 15px;'><p>3. Pre-processing of ALOS PALSAR data</p><p style='margin: 0'>Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Pre-processing of ALOS PALSAR data</p></td></tr><tr><td style='padding: 15px;'><p>4. Filtering for Speckles</p><p style='margin: 0'>Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Filtering for Speckles</p></td></tr><tr><td style='padding: 15px;'><p>5. Obtain back-scatter values from ALOS PALSAR data</p><p style='margin: 0'>Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Obtain back-scatter values from ALOS PALSAR data</p></td></tr></tbody></table>

Web Development

Pre-process and Analyze Satellite Remote Sensing Data with Free Software

Satellite Remote Sensing Data Bootcamp With Opensource Tools

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.</strong>
        <p>TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs.

In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision.

By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.</p>
        <h2>What You Will Learn</h2><p>Understand the concept of convolution<br>Integrate convolution into neural networks<br>Apply CNNs to several image recognition datasets, both small and large<br>Learn best practices for designing CNN architectures<br>Learn about batch normalization and data augmentation<br>Learn how to preform text preprocessing</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2.<br><br>One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks. The course is well-balanced with theory that explains the CNN concepts and hands-on coding exercises for practical understanding.</p>
        <h2>Key Features</h2><p>Learn how to use TensorFlow 2 to build Convolutional Neural Networks (CNNs) * The course covers Natural Language Processing (NLP) and transfer learning for Computer Vision * Explains how to apply CNNs to NLP</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Convolutional Neural Networks (CNNs)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Convolution? (Part 1)</p><p style='margin: 0'>In this video, we will get introduced to the concept of convolution.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Convolution? (Part 2)</p><p style='margin: 0'>In this video, we will look at a new perspective on convolution to understand how it works.</p></td></tr><tr><td style='padding: 15px;'><p>3. What Is Convolution? (Part 3)</p><p style='margin: 0'>In this video, you will learn how to view convolution as matrix multiplication.</p></td></tr><tr><td style='padding: 15px;'><p>4. Convolution on Color Images</p><p style='margin: 0'>In this video, we will understand convolution on color images.</p></td></tr><tr><td style='padding: 15px;'><p>5. CNN Architecture</p><p style='margin: 0'>In this video, we will understand the CNN architecture.</p></td></tr><tr><td style='padding: 15px;'><p>6. CNN Code Preparation</p><p style='margin: 0'>In this video, we will look at the code preparation we will use for CNN and understand it.</p></td></tr><tr><td style='padding: 15px;'><p>7. CNN for Fashion MNIST</p><p style='margin: 0'>In this video, we will start with implementing CNN with the fashion MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>8. CNN for CIFAR-10</p><p style='margin: 0'>In this video, we will work on image classification with the CIFAR-10 dataset.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Augmentation</p><p style='margin: 0'>In this video, you will learn about data augmentation.</p></td></tr><tr><td style='padding: 15px;'><p>10. Batch Normalization</p><p style='margin: 0'>In this video, you will learn about batch normalization.</p></td></tr><tr><td style='padding: 15px;'><p>11. Improving CIFAR-10 Results</p><p style='margin: 0'>In this video, you will learn how to improve CIFAR-10 results using data augmentation and batch normalization.</p></td></tr><tr><td style='padding: 15px;'><p>12. Suggestion Box</p><p style='margin: 0'>In this video, we will look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Natural Language Processing (NLP)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Embeddings</p><p style='margin: 0'>In this video, we will understand the text as sequence data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (NLP)</p><p style='margin: 0'>In this video, we will look at the code preparation we will use for NLP and understand it.</p></td></tr><tr><td style='padding: 15px;'><p>3. Text Preprocessing</p><p style='margin: 0'>In this video, we will move into action and understand how to do text preprocessing using Colab notebook.</p></td></tr><tr><td style='padding: 15px;'><p>4. CNNs for Text</p><p style='margin: 0'>In this video, we will understand how we can use CNNs for text sequences.</p></td></tr><tr><td style='padding: 15px;'><p>5. Text Classification with CNNs</p><p style='margin: 0'>In this video, we will move into action and work on text classification with CNNs.</p></td></tr></tbody></table><br><h4>4. Transfer Learning for Computer Vision</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Transfer Learning Theory</p><p style='margin: 0'>In this video, we will get introduced to the concept of transfer learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Some Pre-Trained Models (VGG, ResNet, Inception, MobileNet)</p><p style='margin: 0'>In this video, we will understand VGG, ResNet, Inception, and MobileNet.</p></td></tr><tr><td style='padding: 15px;'><p>3. Large Datasets and Data Generators</p><p style='margin: 0'>In this video, you will learn how to work with large image datasets in TensorFlow and Keras with data generators.</p></td></tr><tr><td style='padding: 15px;'><p>4. 2 Approaches to Transfer Learning</p><p style='margin: 0'>In this video, we will discuss the two approaches used for transfer learning, which we will use in the next videos.</p></td></tr><tr><td style='padding: 15px;'><p>5. Transfer Learning Code (Part 1)</p><p style='margin: 0'>In this video, we will dive into action and look at codes for transfer learning with data augmentation.</p></td></tr><tr><td style='padding: 15px;'><p>6. Transfer Learning Code (Part 2)</p><p style='margin: 0'>In this video, we will dive into action and look at codes for transfer learning without data augmentation.</p></td></tr></tbody></table>

Computer Vision

Data Science

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.

Deep Learning - Convolutional Neural Networks with TensorFlow

<p><strong>48-Hour Knowledge Knockdown! Prices Reduced Like Never Before!</strong></p><p>Step into the world of geospatial technology with our exclusive QGIS Cartography and ArcGIS bundle. Discover the power of spatial analysis and remote sensing while learning to create smart maps, web scraping, and data visualisation. Whether you're a beginner or an experienced professional, this bundle offers everything you need to enhance your skillset and advance your career.</p><p>As a bonus, the QGIS Cartography course is QLS-endorsed, ensuring that you receive a hardcopy certificate upon completion, enhancing your credibility in the field. The remaining ten courses are CPD-QS accredited, providing you with industry-recognised certification.</p><p>Our courses are designed to be accessible to everyone, with no prior experience or technical knowledge required. With our easy-to-follow lectures, you can learn at your own pace and on your own schedule. You'll have access to comprehensive resources, including course materials and support from our expert team.</p><p>The QGIS Cartography and ArcGIS bundle is the perfect choice for anyone looking to expand their knowledge and enhance their career prospects in the geospatial industry. With our top-rated courses, you'll gain invaluable skills that will set you apart from the competition. So why wait? Enrol now and take the first step towards a brighter future!</p><p><strong>This </strong><strong>QGIS Cartography and ArcGIS</strong><strong> Bundle Package includes:</strong></p><ul><li><strong>Course 01:</strong> Diploma in QGIS Cartography at QLS Level 4</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01: </strong>Spatial Analysis in ArcGIS</li><li><strong>Course 02: </strong>Remote Sensing in ArcGIS</li><li><strong>Course 03: </strong>ArcGIS for Hydrology</li><li><strong>Course 04: </strong>Spatial Data Visualization and Machine Learning in Python Level 4</li><li><strong>Course 05: </strong>Create Smart Maps in Python and Leaflet Level 3</li><li><strong>Course 06:</strong> Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</li><li><strong>Course 07: </strong>Python Data Science</li><li><strong>Course 08: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong>Course 09: </strong>Maps in R Shiny and Leaflet</li><li><strong>Course 10: </strong>The Basics of ArcGIS for Hydrology</li></ul><p><strong>Why Prefer This </strong><strong>QGIS Cartography and ArcGIS</strong><strong> Bundle?</strong></p><ul><li>You will receive a completely free certificate from the Quality Licence Scheme</li><li>Option to purchase 10 additional certificates accredited by CPD</li><li>Get a free Student ID Card - (£10 postal charges will be applicable for international delivery)</li><li>Free assessments and immediate success results</li><li>24/7 Tutor Support</li></ul> <p><strong>After taking this </strong><strong>QGIS Cartography and ArcGIS</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Understand the basics of QGIS Cartography and ArcGIS software</li><li>Learn to perform spatial analysis and remote sensing in ArcGIS</li><li>Develop skills in creating smart maps and web scraping with Python and Leaflet</li><li>Learn data visualisation and machine learning in Python</li><li>Gain expertise in hydrology with ArcGIS</li><li>Create maps in R Shiny and Leaflet</li><li>Use Python for spatial analysis in ArcGIS</li></ul><p><strong>***Curriculum breakdown of QGIS Cartography</strong><strong>***</strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02: </strong>Labels<strong><br /></strong></li><li><strong>Section 03: </strong>Symbology<strong><br /></strong></li><li><strong>Section 04: </strong>Base Layers<strong><br /></strong></li><li><strong>Section 05: </strong>Project Setup<strong><br /></strong></li><li><strong>Section 06: </strong>Water Styling<strong><br /></strong></li><li><strong>Section 07: </strong>Land Styling</li></ul><p><strong>How is the </strong><strong>QGIS Cartography and ArcGIS </strong><strong>Bundle Assessment Process?</strong></p><p>You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment.</p><p>You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Professionals seeking to enhance their skills in geospatial technology</li><li>Students interested in a career in GIS or related fields</li><li>Individuals looking to expand their knowledge in QGIS Cartography and ArcGIS software</li><li>Anyone seeking industry-recognized certification</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This bundle will be beneficial for anyone looking to pursue a career as:</p><ul><li>GIS Analyst: £21,000 - £42,000 per annum</li><li>GIS Technician: £18,000 - £32,000 per annum</li><li>GIS Manager: £28,000 - £65,000 per annum</li><li>Remote Sensing Analyst: £25,000 - £50,000 per annum</li><li>Data Scientist: £25,000 - £80,000 per annum</li><li>Hydrologist: £25,000 - £50,000 per annum</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Diploma in QGIS Cartography at QLS Level 4</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>After successfully completing thecourse, you can order an original hardcopy Certificate of Achievement endorsed by the <strong>Quality Licence Scheme</strong>. This certificate will be delivered to your home, free of charge.</p><p>To obtain the additional Quality Licence Scheme Endorsed Certificate, you will need to pay the corresponding fee based on the level of the courses:</p><ul><li>Level 1: £59</li><li>Level 2: £79</li><li>Level 3: £89</li><li>Level 4: £99</li><li>Level 5: £119</li><li>Level 6: £129</li><li>Level 7: £139</li></ul> </div>
        <h4>CPD QS Accredited Certificate </h4>
        <p>
            Digital certificate -
            £10
        </p>
            <div> <p>Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS and will cost you £10 for each course. Also, the certificates are recognised throughout the UK and internationally.</p> </div>
        <h4>CPD QS Accredited Certificate </h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div> <p>International students are subject to a £10 delivery fee for their orders, based on their location.</p> </div>

                        </div>

Academy for Health and Fitness

QGIS Cartography and ArcGIS

<p><strong> 24-Hour Knowledge Knockdown! Prices Reduced Like Never Before </strong></p><p>Imagine you are soaring high above the earth, observing the world from a bird's-eye view. Remote Sensing in ArcGIS bundle could be your ticket to experiencing the world in a way you never have before. In the UK alone, the geospatial industry contributes over <strong>£11 billion to the economy</strong>. With 11 courses, this Remote Sensing in ArcGIS bundle is a one-stop shop for all your GIS and remote sensing needs.</p><p>The bundle's crown jewel is the Remote Sensing in ArcGIS course, which is QLS-endorsed and comes with a hardcopy certificate. With courses covering everything from QGIS Cartography to Business Intelligence and Data Mining Diploma, this bundle is a comprehensive guide to all things remote sensing. You'll learn how to create smart maps in Python, perform spatial analysis in ArcGIS, and even use web scraping to map dam levels. The possibilities are endless!</p><p><strong>This Remote Sensing in ArcGIS </strong><strong>Bundle Package includes:</strong></p><p><strong>Course 01:</strong> Award in Remote Sensing in ArcGIS at QLS Level 2</p><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01: </strong>QGIS Cartography</li><li><strong>Course 02: </strong>Spatial Data Visualization and Machine Learning in Python Level 4</li><li><strong>Course 03:</strong> Create Smart Maps in Python and Leaflet Level 3</li><li><strong>Course 04:</strong> Spatial Analysis in ArcGIS</li><li><strong>Course 05: </strong>ArcGIS for Hydrology</li><li><strong>Course 06: </strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</li><li><strong>Course 07: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong>Course 08: </strong>The Basics of ArcGIS for Hydrology</li><li><strong>Course 09: </strong>Python Data Science</li><li><strong>Course 10: </strong>Business Intelligence and Data Mining Diploma</li></ul><p>Don't miss this opportunity to expand your knowledge and skills in remote sensing. Enrol in the Remote Sensing in ArcGIS bundle today and take your first step towards exploring the world from a whole new perspective.</p><p><strong>Why Choose Us?</strong></p><ul><li>Get a<strong> free QLS endorsed Certificate</strong> upon completion of Remote Sensing in ArcGIS</li><li>Get a<strong> free student ID card</strong> with Remote Sensing in ArcGIS Training</li><li>The Remote Sensing in ArcGIS is affordable and simple to understand</li><li>This course is entirely online, interactive lesson with voiceover audio</li><li><strong>Lifetime access </strong>to the Remote Sensing in ArcGIS course materials</li><li>The Remote Sensing in ArcGIS comes with 24/7 tutor support</li></ul> <p><strong>After taking this Remote Sensing in ArcGIS </strong><strong>bundle courses, you will be able to learn:</strong></p><ul><li>Gain expertise in remote sensing and GIS technologies.</li><li>Understand the fundamentals of spatial data analysis and visualisation.</li><li>Learn to create maps using Python and Leaflet.</li><li>Explore machine learning techniques for spatial data analysis.</li><li>Get hands-on experience in ArcGIS for hydrology.</li><li>Understand web scraping and mapping using Python and Leaflet.</li><li>Develop proficiency in data mining, business intelligence, and Python data science.</li></ul><p><strong>***Curriculum breakdown </strong><strong>***</strong></p><ul><li><strong>Module 01: </strong>Remote sensing, satellite images, spectral bands introduction</li><li><strong>Module 02:</strong> Layers stacking satellite images</li><li><strong>Module 03: </strong>Georeferencing satellite images</li><li><strong>Module 04:</strong> Introduction to geoprocessing raster tools</li><li><strong>Module 05:</strong> Raster Analysis Functions</li><li><strong>Module 06</strong>: Georeferencing toposheet</li><li><strong>Module 07:</strong> Site suitability using weighted overlay analysis - part 1</li><li><strong>Module 08: </strong>Site suitability using weighted overlay analysis - part 2</li><li><strong>Module 09: </strong>Watershed Delineation from DEM</li><li><strong>Module 10: </strong>Unsupervised classification</li></ul><p><strong>Assessment Process</strong></p><p>You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the <strong>Award in Remote Sensing in ArcGIS at QLS Level 2 </strong>exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This bundle is ideal for:</p><ul><li>Students seeking mastery in this field</li><li>Professionals seeking to enhance their skills</li><li>Anyone who is passionate about this topic</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Remote Sensing in ArcGIS doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Remote Sensing in ArcGIS bundle will be beneficial for anyone looking to pursue a career as:</p><ul><li>GIS Analyst</li><li>Remote Sensing Specialist</li><li>Spatial Data Scientist</li><li>Geospatial Project Manager</li><li>GIS Consultant</li><li>GIS Team Leader</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Award in Remote Sensing in ArcGIS at QLS Level 2</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>CPD QS Accredited Certificate </h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>International students are subject to a £10 delivery fee for their orders, based on their location.</p></div>
        <h4>CPD QS Accredited PDF Certificate </h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Remote Sensing in ArcGIS

<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>

Nexus Human

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

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>

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>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 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>

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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