Name: PLC and SCADA Training
Price: 39 GBP
Availability: InStock

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Master Google Analytics in No Time! Admission Gifts <br /><br /></strong><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Learn to Master Google Analytics in No Time! Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Learn to Master Google Analytics in No Time! bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Learn to Master Google Analytics in No Time! Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Learn to Master Google Analytics in No Time! Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 02: </strong>Diploma in Data Analysis Fundamentals</li><li><strong>Course 03: </strong>Excel Pivot Tables for Data Reporting</li><li><strong>Course 04: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 05: </strong>Receptionist Skills</li><li><strong>Course 06: </strong>Research Methods in Business</li><li><strong>Course 07: </strong>Strategic Planning and Analysis for Marketing</li><li><strong>Course 08: </strong>Create Your First Responsive Website</li><li><strong>Course 09: </strong>Quick Data Science Approach from Scratch</li><li><strong>Course 10: </strong>Business Intelligence and Data Mining</li><li><strong>Course 11: </strong>Fundamentals of Business Analysis</li><li><strong>Course 12: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 13: </strong>Marketing Strategies for Business</li><li><strong>Course 14: </strong>Excel Data Analysis</li><li><strong>Course 15: </strong>Statistical Analysis</li><li><strong>Course 16: </strong>Public Speaking</li><li><strong>Course 17: </strong>Minute-Taking Executive Training</li><li><strong>Course 18: </strong>Ultimate Social Media Marketing course</li><li><strong>Course 19: </strong>Microsoft Power BI - Master Power BI in 90 Minutes!</li><li><strong>Course 20: </strong>How To Do Keyword Research For SEO &amp; Ranking On Google</li><li><strong>Course 21: </strong>PowerBI Formulas</li><li><strong>Course 22: </strong>The Ultimate SEO Blueprint - Easily Ranking #1 On Google</li><li><strong>Course 23: </strong>Digital Marketing for Beginners to Advanced</li><li><strong>Course 24: </strong>Complete Communication Skills Master Class for Life</li><li><strong>Course 25: </strong>Python Course</li><li><strong>Course 26: </strong>Digital Marketing Starting an Online E-Commerce Store</li><li><strong>Course 27: </strong>Social Media Marketing-The Step by Step Guide</li><li><strong>Course 28: </strong>Complete Online Marketing &amp; Advertising Course</li><li><strong>Course 29: </strong>Learn How To Optimize Your Google My Business Page In 2016</li><li><strong>Course 30: </strong>Time Management Training - Online Course</li></ul><p>With Learn to Master Google Analytics in No Time!, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Learn to Master Google Analytics in No Time! today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Learn to Master Google Analytics in No Time!?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Learn to Master Google Analytics in No Time!</li><li>Get <strong>a free student ID card</strong> with Learn to Master Google Analytics in No Time!</li><li><strong>Lifetime access</strong> to the Learn to Master Google Analytics in No Time! course materials</li><li>Get instant access to this Learn to Master Google Analytics in No Time! course</li><li>Learn Learn to Master Google Analytics in No Time! from anywhere in the world</li><li>24/7 tutor support with the Learn to Master Google Analytics in No Time! course.</li></ul> <p><strong>Start your learning journey straightaway with our Learn to Master Google Analytics in No Time! Training!</strong></p><p>Learn to Master Google Analytics in No Time! premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Learn to Master Google Analytics in No Time! is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Learn to Master Google Analytics in No Time! course. After passing the Learn to Master Google Analytics in No Time! exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><strong>This Learn to Master Google Analytics in No Time! course is ideal for:</strong></p><ul><li>Students seeking mastery in Learn to Master Google Analytics in No Time!</li><li>Professionals seeking to enhance Learn to Master Google Analytics in No Time! skills</li><li>Individuals looking for a Learn to Master Google Analytics in No Time!-related career.</li><li>Anyone passionate about Learn to Master Google Analytics in No Time!</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Learn to Master Google Analytics in No Time!</strong> 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 <strong>Learn to Master Google Analytics in No Time!</strong> bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Academy for Health and Fitness

Online Course Marketing

Online Course Creation

Trainers Course

Google Digital Marketing

Learn to Master Google Analytics in No Time!

General Admission

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Software Engineering: Software Programming, Web Design &amp; Development Admission Gifts <br /><br /></strong><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Coding with Scratch</li><li><strong>Course 02: </strong>C# Programming - Beginner to Advanced</li><li><strong>Course 03: </strong>Python Programming Bible</li><li><strong>Course 04: </strong>Machine Learning with Python Course</li><li><strong>Course 05: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 06: </strong>Advanced Diploma in PHP Web Development with MySQL, GitHub &amp; Heroku</li><li><strong>Course 07: </strong>JavaScript Fundamentals</li><li><strong>Course 08: </strong>Node JS: API Development with Swagger</li><li><strong>Course 09: </strong>Refactor Javascript</li><li><strong>Course 10: </strong>Penetration Testing with OWASP ZAP</li><li><strong>Course 11: </strong>Advanced SAS Programming Using MacrosSQL</li><li><strong>Course 12: </strong>How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution)</li><li><strong>Course 13: </strong>Three.js &amp; WebGL 3D Programming</li><li><strong>Course 14: </strong>Stripe with C#</li><li><strong>Course 15: </strong>C++ Development: The Complete Coding Guide</li><li><strong>Course 16: </strong>Bootstrap - Responsive Web Design</li><li><strong>Course 17: </strong>Masterclass Bootstrap 5 Course - Responsive Web Design</li><li><strong>Course 18: </strong>Diploma in User Experience UI/UX Design (Advanced)</li><li><strong>Course 19: </strong>HTML Web Development Crash Course</li><li><strong>Course 20: </strong>CSS Web Development</li><li><strong>Course 21: </strong>.NET Core API Development</li><li><strong>Course 22: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 23: </strong>The Ultimate WordPress for Beginners Step-by-Step Blueprint</li><li><strong>Course 24: </strong>Using WordPress for Affiliate Marketing</li><li><strong>Course 25: </strong>WordPress 5.0 for Beginners to Advanced</li><li><strong>Course 26: </strong>WordPress and SEO Online Course</li><li><strong>Course 27: </strong>How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li><strong>Course 28: </strong>Make a WordPress WooCommerce Amazon Affiliate Store</li><li><strong>Course 29: </strong>WordPress Security Mastery</li><li><strong>Course 30: </strong>How To Build a WordPress Membership Site With Memberpress</li></ul><p>With Software Engineering: Learn All About Software, Programming, Web Design &amp; Development, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Software Engineering: Learn All About Software, Programming, Web Design &amp; Development today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</li><li>Get <strong>a free student ID card</strong> with Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</li><li><strong>Lifetime access</strong> to the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course materials</li><li>Get instant access to this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course</li><li>Learn Software Engineering: Learn All About Software, Programming, Web Design &amp; Development from anywhere in the world</li><li>24/7 tutor support with the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course.</li></ul> <p><strong>Start your learning journey straightaway with our Software Engineering: Learn All About Software, Programming, Web Design &amp; Development Training!</strong></p><p>Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course. After passing the Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 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 <strong>Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</strong> 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 <strong>Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</strong> bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Learning & Development

Service Design Courses (SDC)

Software Engineering

Software Development

Software Engineering: Software Programming, Web Design & Development - CPD Certified

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications.<br />Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions.<br />Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications.<br />Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights.<br />Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data.<br />Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis.</p><p>Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems.</p></div><div id="course-pills-2"><h4>Introduction to Scala</h4><ul><li>Brief history and motivation</li><li>Differences between Scala and Java</li><li>Basic Scala syntax and constructs</li><li>Scala's functional programming features</li></ul><h4>Introduction to Apache Spark</h4><ul><li>Overview and history</li><li>Spark components and architecture</li><li>Spark ecosystem</li><li>Comparing Spark with other big data frameworks</li></ul><h4>Basics of Spark Programming SparkContext and SparkSession</h4><ul><li>Resilient Distributed Datasets (RDDs)</li><li>Transformations and Actions</li><li>Working with DataFrames</li></ul><h4>Spark SQL and Data Sources</h4><ul><li>Spark SQL library and its advantages</li><li>Structured and semi-structured data sources</li><li>Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.)</li><li>Data manipulation using SQL queries</li></ul><h4>Basic RDD Operations</h4><ul><li>Creating and manipulating RDDs</li><li>Common transformations and actions on RDDs</li><li>Working with key-value data</li></ul><h4>Basic DataFrame and Dataset Operations</h4><ul><li>Creating and manipulating DataFrames and Datasets</li><li>Column operations and functions</li><li>Filtering, sorting, and aggregating data</li></ul><h4>Introduction to Spark Streaming</h4><ul><li>Overview of Spark Streaming</li><li>Discretized Stream (DStream) operations</li><li>Windowed operations and stateful processing</li></ul><h4>Performance Optimization Basics</h4><ul><li>Best practices for efficient Spark code</li><li>Broadcast variables and accumulators</li><li>Monitoring Spark applications</li></ul><h4>Integrating External Libraries and Tools, Spark Streaming</h4><ul><li>Using popular external libraries, such as Hadoop and HBase</li><li>Integrating with cloud platforms: AWS, Azure, GCP</li><li>Connecting to data storage systems: HDFS, S3, Cassandra, etc.</li></ul><h4>Introduction to Machine Learning Basics</h4><ul><li>Overview of machine learning</li><li>Supervised and unsupervised learning</li><li>Common algorithms and use cases</li></ul><h4>Introduction to Spark MLlib</h4><ul><li>Overview of Spark MLlib</li><li>MLlib's algorithms and utilities</li><li>Data preparation and feature extraction</li></ul><h4>Linear Regression and Classification</h4><ul><li>Linear regression algorithm</li><li>Logistic regression for classification</li><li>Model evaluation and performance metrics</li></ul><h4>Clustering Algorithms</h4><ul><li>Overview of clustering algorithms</li><li>K-means clustering</li><li>Model evaluation and performance metrics</li></ul><h4>Collaborative Filtering and Recommendation Systems</h4><ul><li>Overview of recommendation systems</li><li>Collaborative filtering techniques</li><li>Implementing recommendations with Spark MLlib</li></ul><h4>Introduction to Graph Processing</h4><ul><li>Overview of graph processing</li><li>Use cases and applications of graph processing</li><li>Graph representations and operations</li><li>Introduction to Spark GraphX</li><li>Overview of GraphX</li><li>Creating and transforming graphs</li><li>Graph algorithms in GraphX</li></ul><h4>Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala</h4><ul><li>Overview of generative AI technologies</li><li>Integrating GPT with Spark and Scala</li><li>Practical applications and use cases Bonus Topics / Time Permitting</li></ul><h4>Introduction to Spark NLP</h4><ul><li>Overview of Spark NLP Preprocessing text data</li><li>Text classification and sentiment analysis</li></ul><h4>Putting It All Together</h4><ul><li>Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.</li></ul></div>

Nexus Human

Pyspark

Data Engineering

Apache Spark

Big Data Analytics

Data Science

Data Skills

Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)

<p><strong>Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><p>Turn your skills into a competitive advantage that is recognised globally and endorsed for quality by QLS, the UK's most reputable high-quality course auditor.</p><p>Hone your skills in data analytics, big data, machine learning, data pipelines, and artificial intelligence! Create infrastructure for collecting, managing, and transforming raw data into interpretable information by data scientists and business analysts. Develop your ability to create data processing solutions, make data accessible, and evaluate and optimise organisational performance. Enrol to advance your Cloud career and show your proficiency in one of the most sought-after industries in the market.</p><p>This premium QLS endorsed Data Engineering with Google BigQuery &amp; Google Cloud course comes with intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunity and a globally recognised certification all-together. Thus, successful completion of our Data Engineering with Google BigQuery &amp; Google Cloud course will give you limitless opportunities anywhere and everywhere, since the value of QLS endorsed courses are quite high, to say the least!!</p><p>What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest of materials from this exquisite Data Engineering with Google BigQuery &amp; Google Cloud course, enabling you to open up a vast world of opportunities for you to grasp and gain success from. For the price point, this course is actually an absolute steal! It's an investment you absolutely do not want to miss out on!</p><p>So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as, unquestionably, the most skilled one in your workplace!</p><p><strong>Learning Outcomes for Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud:</strong></p><ol><li>Develop a comprehensive understanding of data engineering principles and concepts.</li><li>Utilise Google BigQuery and Google Cloud for efficient data warehousing and analysis.</li><li>Explore and manipulate data effectively using BigQuery for insightful data-driven decisions.</li><li>Apply data flow techniques to design and optimise data pipelines.</li><li>Implement virtual data solutions using views for efficient data organisation and retrieval.</li><li>Create visually appealing and informative data visualisations to communicate insights effectively.</li></ol><p><strong>Why Learn with us: </strong></p><p>We, as one of the leading e-learning service providers across the globe, strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong><strong> Includes:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><ul><li><strong>Unit 01: </strong>Introduction<strong><br /></strong></li><li><strong>Unit 02: </strong>Introducing Data Warehouse &amp; BigQuery<strong><br /></strong></li><li><strong>Unit 03: </strong>First Exploration<strong><br /></strong></li><li><strong>Unit 04: </strong>Data Flow Basic<strong><br /></strong></li><li><strong>Unit 05: </strong>Diving into BigQuery<strong><br /></strong></li><li><strong>Unit 06: </strong>Virtual Data using View<strong><br /></strong></li><li><strong>Unit 07: </strong>Data Visualisation<strong><br /></strong></li><li><strong>Unit 08: </strong>Intermediate Query</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the Data Engineering with Google BigQuery &amp; Google Cloud certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education.</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">
                            <p>Our course is open for everyone. This Data Engineering with Google BigQuery &amp; Google Cloud is ideal for those who aspire to gain in-depth knowledge &amp; efficient skill to embark into the relevant sector as a promising potential.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal requirements needed for this Data Engineering with Google BigQuery &amp; Google Cloud course. So learners do not require any prior qualifications to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p><strong>Here are few job roles related to Data Engineering with Google BigQuery &amp; Google Cloud </strong>(Salary-per annum):</p><ol><li>Data Engineer (£50,000 - £55,000)</li><li>Data Analyst: (£38,000 - £40,000)</li><li>Data Scientist (£40,000 - £70,000)</li><li>Data Architect (£80,000 - £90,000)</li><li>Cloud Data Engineer (£50,000 - £80,000)</li><li>Big Data Engineer (£50,000 - £80,000)</li></ol>
                        </div>

Kingston Open College

ELS (Effective Learning Service)

ICDL (International Certification of Digital Literacy)

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Level 6 Diploma in Data Engineering with Google BigQuery & Google Cloud - QLS Endorsed

<strong>Build next-gen programming skills using RxJava 3.0 and Reactive Streams</strong>
        <p>In this course, you'll learn about Reactive programming and libraries such as RxJava and Reactor that are designed to keep the application responsive and make the system more resilient.
The course starts by helping you understand the fundamentals of RxJava to make it easy for you to learn advanced topics such as the Reactive Manifesto, callbacks, callback hell, sync vs async, concurrent vs parallel, and the observer design pattern. As you advance, you'll also dive into the concepts of observable and observers, operators, combining observables, and replaying caching and subjects. You'll then explore concurrency and parallelism and get to grips with buffering, throttling, and switching. 
By the end of the course, you'll have developed a solid understanding of Reactive programming concepts and RxJAVA.

All the code and supporting files for this course are available here: https://github.com/PacktPublishing/Reactive-Programming-in-Java-Using-RxJava-3.x-ReactiveX</p>
        <h2>What You Will Learn</h2><p>Get to grips with Reactive programming fundamentals<br>Explore RxJava 3 and its features<br>Understand the implementation of Java Reactive Streams<br>Learn about the observer design pattern<br>Explore the different Streams in Java with the help of coding examples</p>
        <h2>Audience</h2><p>If you want to learn RxJava and Reactive programming and use them to create amazing applications, this course is for you.</p>
        <h2>Approach</h2><p>A hands-on course that will help you learn the concepts of RxJava 3.0 and Reactive Streams with the help of coding examples.</p>
        <h2>Key Features</h2><p>Become proficient in Reactive programming using RXJava 3.x * Explore different Reactive Streams and learn about their implementation * Understand how different operators and observables work in Java</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/reactive-programming-in-java-using-rxjava-3.x-reactivex</p>
        <h2>About the Author</h2><h3>Basics Strong</h3><p>Basics Strong is a team of technocrats from IITs who focus on solving problems using technology. They work on mission-critical projects in AI, machine learning, and BlockChain as a domain and use Java, Python, JavaScript, and a lot of tools and technologies. They love to code and program.

The team believes that a strong foundation in the basics of programming concepts can help you solve any technical problem and excel in your career. Therefore, they create courses that help you build your basics and come up with ways to make complicated concepts easy to learn. All their courses are carefully crafted to include hands-on examples and comprehensive working files for practical learning.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Reactive Programming Introduction</h4><p></p><br><h4>3. Hello RxJava!</h4><p></p><br><h4>4. The Observable And Observers</h4><p></p><br><h4>5. Operators</h4><p></p><br><h4>6. Combining Observables</h4><p></p><br><h4>7. Subjects, Replaying And Caching : Ways To Multicast</h4><p></p><br><h4>8. Concurrency And Parallelization</h4><p></p><br><h4>9. Buffering, Throttling, Switching</h4><p></p><br><h4>10. Flowable and Backpressure</h4><p></p><br><h4>11. Streams and Parallel Streams</h4><p></p>

Packt

Java

Programming

Build next-gen programming skills using RxJava 3.0 and Reactive Streams

Reactive Programming in Java Using RxJava 3.x: ReactiveX

<strong>Learn to design a resilient and scalable cloud solution using traffic load balancers, availability sets, and scale sets</strong>
        <p>Cloud computing is one of the biggest technology revolutions in the IT industry, spreading at the speed of light all over the world. A growing number of business companies and organizations are looking for ways to migrate their applications onto the cloud or build new cloud-native applications from scratch on top of cloud infrastructure. The demand for more skilled people in the arena of cloud computing is increasing every day across multiple industries. Companies are looking for cloud experts!

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

Level 3 is all about resilience and scalability. 

In level 1, we learned to build an end-to-end cloud solution on the Microsoft Azure platform; 
In level 2, we learned to monitor the performance and health of our cloud solution. 

Now it is time to learn how to design a system that will be more resilient to failures and better able to accommodate unpredictable capacity demands.

All the code files are placed at https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-3</p>
        <h2>What You Will Learn</h2><p>Master the main functions, types, and rules of a Traffic Load Balancer<br>Deploy and manage Azure network and application LBs<br>How to design a more resilient cloud solution<br>Understand the fault and update domain concepts<br>Deploy and manage Azure availability sets<br>Perform performance optimization with capacity scaling<br>Use vertical and horizontal scaling<br>Deploy and manage Azure scale sets</p>
        <h2>Audience</h2><p>This course is for IT Professionals who are keen to gain a strong understanding of Cloud Computing and want to enhance their service and deployment models. Cloud administrators, IT and cloud experts, and DevOps engineers will also benefit from the course.</p>
        <h2>Approach</h2><p>This exhaustive course is packed with step-by-step instructions, practical examples, and helpful advice.</p>
        <h2>Key Features</h2><p>A comprehensive overview of the Microsoft Azure platform * Practice while building an end-to-end demo system * The third level in Becoming a Cloud Expert training program</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/becoming-a-cloud-expert---microsoft-azure-iaas---level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome!</p><p style='margin: 0'>Getting Started: Welcome!</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Getting Started: Course Objectives and Structure</p></td></tr></tbody></table><br><h4>2. Traffic Load Balancing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Traffic Load Balancing: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Load Balancer?</p><p style='margin: 0'>Traffic Load Balancing: What is a Load Balancer?</p></td></tr><tr><td style='padding: 15px;'><p>3. Network and Application LBs</p><p style='margin: 0'>Traffic Load Balancing: Network and Application LBs</p></td></tr><tr><td style='padding: 15px;'><p>4. Public and Internal LBs</p><p style='margin: 0'>Traffic Load Balancing: Public and Internal LBs</p></td></tr><tr><td style='padding: 15px;'><p>5. Azure Load Balancer</p><p style='margin: 0'>Traffic Load Balancing: Azure Load Balancer</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - Setup a Web Servers Cluster</p><p style='margin: 0'>Traffic Load Balancing: Demo - Setup a Web Servers Cluster</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Azure Load Balancer</p><p style='margin: 0'>Traffic Load Balancing: Demo - Azure Load Balancer</p></td></tr></tbody></table><br><h4>3. Azure Availability Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Azure Availability Sets: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Resiliency and Availability</p><p style='margin: 0'>Azure Availability Sets: Resiliency and Availability</p></td></tr><tr><td style='padding: 15px;'><p>3. Azure Redundancy Options</p><p style='margin: 0'>Azure Availability Sets: Azure Redundancy Options</p></td></tr><tr><td style='padding: 15px;'><p>4. Availability Set, Fault and Update Domains</p><p style='margin: 0'>Azure Availability Sets: Availability Set, Fault and Update Domains</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Availability Sets</p><p style='margin: 0'>Azure Availability Sets: Demo - Availability Sets.</p></td></tr></tbody></table><br><h4>4. Azure VMs Scale Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Azure VMs Scale Sets: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Performance Optimization and Capacity Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Performance Optimization and Capacity Scaling</p></td></tr><tr><td style='padding: 15px;'><p>3. Vertical and Horizontal Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Vertical and Horizontal Scaling</p></td></tr><tr><td style='padding: 15px;'><p>4. Automatic Scaling</p><p style='margin: 0'>Azure VMs Scale Sets: Automatic Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. What is an Azure Scale Set?</p><p style='margin: 0'>Azure VMs Scale Sets: What is an Azure Scale Set?</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - Web Server as Source VM Image</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Web Server as Source VM Image</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Create and Configure a Scale Set</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Create and Configure a Scale Set</p></td></tr><tr><td style='padding: 15px;'><p>8. Demo - Testing the Scale Set</p><p style='margin: 0'>Azure VMs Scale Sets: Demo - Testing the Scale Set</p></td></tr></tbody></table><br><h4>5. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Summary</p><p style='margin: 0'>Course Summary: Course Summary</p></td></tr></tbody></table>

Cloud Computing

Microsoft Azure

Learn to design a resilient and scalable cloud solution using traffic load balancers, availability sets, and scale sets

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 3

<p>Digital banking has rapidly transformed the banking sector, and its adoption is increasing at an unprecedented pace. It refers to the use of digital technology to provide financial services to customers, and it has revolutionised the way people conduct their financial transactions. Digital banking has enabled customers to access banking services from anywhere, at any time, and through any device, making banking convenient, fast, and efficient. It has transformed the banking sector, making it more customer-centric and competitive.</p><p>The growth of digital banking has been fuelled by the proliferation of internet banking, mobile banking, and the concept of direct banking, enabling customers to manage their finances without visiting a physical bank branch. Social media has also played a significant role in digital banking, as it provides a platform for banks to interact with their customers, offer support and guidance, and promote their services. The arrival of fintech firms has further disrupted the banking sector, introducing innovative technologies such as artificial intelligence, machine learning, blockchain, and big data, which have transformed the way banks operate.</p><p><strong>Learning Outcomes:</strong></p><p>By the end of this course, students will be able to:</p><ul><li>Understand the evolution of digital banking and its impact on the banking sector</li><li>Analyse the difference between traditional banking and new-gen banking</li><li>Evaluate the role of internet banking, mobile banking, and direct banking in digital banking</li><li>Assess the impact of social media on banking and the arrival of fintech firms</li><li>Explore the innovative technologies used in digital banking, such as AI, ML, blockchain, and big data</li></ul><p>Digital banking has emerged as a new paradigm in the banking industry. This course covers the story of digital banking, highlighting the evolution from traditional banking to new-gen banking. It covers the proliferation of internet banking, mobile banking, and the concept of direct banking, providing an overview of the different types of digital banking services available. The course also examines the use of social media in banking and the arrival of fintech firms, exploring their impact on the banking sector.</p><p>The course delves into innovative technologies, such as artificial intelligence, machine learning, blockchain, and big data, providing an understanding of their use in digital banking. It also covers the illustrative CIO wishlist, highlighting the key technologies required to complement or enable comprehensive digital banking. Students will gain a comprehensive understanding of the digital banking landscape and how it is transforming the banking sector.</p><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <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">
                            <p>The course is ideal for highly motivated individuals or teams who want to enhance their professional skills and efficiently skilled employees.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>Completion of this course can lead to a range of career opportunities in the banking industry, including:</p><ul><li>Digital Banking Consultant (£35,000 - £70,000)</li><li>Digital Marketing Manager (£25,000 - £50,000)</li><li>Fintech Business Analyst (£30,000 - £60,000)</li><li>Data Analyst - Banking (£25,000 - £50,000)</li><li>Customer Service Advisor - Digital Banking (£18,000 - £25,000)</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Digital Banking for Beginners Course

<p>ð <strong>Unleash Your Potential with the Information Enterprise Launchpad Course! ð</strong></p><p>Are you ready to soar to new heights in the digital landscape? Introducing the <strong>Information Enterprise Launchpad</strong>, your ultimate guide to transforming information into success! ð</p><p> </p><p>In today's fast-paced world, information is power. Imagine having the skills and knowledge to harness that power and turn it into a thriving enterprise. Whether you're a seasoned entrepreneur or a budding business enthusiast, this course is designed to propel you forward.</p><p> </p><p><strong>Key Features:</strong></p><ol><li><strong>Strategic Information Management:</strong> Learn to sift through the digital noise and extract valuable insights that can shape your business strategy. We'll guide you through the art of data analysis and interpretation.</li><li><strong>Innovative Business Models:</strong> Discover cutting-edge business models that leverage information as a core asset. From data monetization to creating a digital ecosystem, we'll explore diverse approaches that fit your unique vision.</li><li><strong>Digital Marketing Mastery:</strong> Gain a competitive edge in the digital landscape with our in-depth digital marketing modules. Learn to optimize your online presence, create compelling content, and engage with your audience effectively.</li><li><strong>Technology Integration:</strong> Stay ahead of the curve by understanding how to integrate the latest technologies into your enterprise. From AI and machine learning to blockchain, we'll demystify complex concepts and show you practical applications.</li><li><strong>Entrepreneurial Mindset Development:</strong> Cultivate the mindset of a successful entrepreneur. Overcome challenges, embrace innovation, and develop resilience in the face of uncertainty.</li><li><strong>Expert-Led Sessions:</strong> Benefit from insights shared by industry experts and successful entrepreneurs who have mastered the art of leveraging information for business growth.</li></ol><p> </p><p><strong>What You'll Gain:</strong></p><p>â <strong>Actionable Strategies:</strong> Walk away with actionable strategies that you can implement immediately to enhance your business.</p><p>â <strong>Networking Opportunities:</strong> Connect with like-minded individuals and industry experts through our exclusive community forums and networking events.</p><p>â <strong>Lifetime Access:</strong> Enjoy lifetime access to course materials and updates, ensuring that you stay at the forefront of the ever-evolving digital landscape.</p><p>â <strong>Certificate of Completion:</strong> Receive a prestigious certificate upon successfully completing the course, showcasing your expertise in information enterprise.</p><p> </p><p>Don't miss your chance to revolutionize your approach to business in the digital age. The Information Enterprise Launchpad is your ticket to success!</p><p> </p><p>ð¥ Enroll now and embark on a journey to transform information into enterprise excellence! ð¥</p><div><div><div><h3>
Course Curriculum</h3></div></div></div>

Compete High

Information Enterprise Launchpad

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.</p>Overview<p>You will be able to<br />Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'<br />Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description<br />Explain the Benefits of Artificial Intelligence (AI)<br />Describe how we Learn from Data - Functionality, Software and Hardware<br />Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together<br />Describe a ''Learning from Experience'' Agile Approach to Projects</p><p>Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI)</p></div><div id="course-pills-2"><h4>Ethical and Sustainable Human and Artificial Intelligence (AI)</h4><ul><li>Recall the General Definition of Human and Artificial Intelligence (AI)</li><li>Describe what are Ethics and Trustworthy Artificial Intelligence (AI)</li><li>Describe the Three Fundamental Areas of Sustainability and the United NationÃ¯s Seventeen Sustainability Goals</li><li>Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution'</li><li>Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI)</li></ul><h4>Artificial Intelligence (AI) and Robotics</h4><ul><li>Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description</li><li>Describe what a Robot is</li><li>Describe what an intelligent Robot is</li></ul><h4>Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks</h4><ul><li>Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations</li><li>Explain the Benefits of Artifical Intelligence (AI)</li><li>Describe the Challenges of Artificial Intelligence (AI) Projects</li><li>Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects</li><li>List Opportunities for Artificial Intelligence (AI)</li><li>Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs)</li></ul><h4>Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice</h4><ul><li>Describe how we Learn from Data - Functionality, Software and Hardware</li><li>Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsÃ¯Functionality</li></ul><h4>The Management, Roles and Responsibilities of Humans and Machines</h4><ul><li>Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together</li><li>List Future Directions of Humans and Machines Working Together</li><li>Describe a ''Learning from Experience'' Agile Approach to Projects</li></ul></div>

Microsoft AI-900

AI (Artificial Intelligence)

EXIN BCS Artificial Intelligence Foundation

<p>If you aim to enhance your PLC and SCADA Training skills, our comprehensive PLC and SCADA Training course is perfect for you. Designed for success, this PLC and SCADA Training course covers everything from basics to advanced topics in PLC and SCADA Training.</p><p>Each lesson in this PLC and SCADA Training course is crafted for easy understanding, enabling you to become proficient in PLC and SCADA Training. Whether you are a beginner or looking to sharpen your existing skills, this PLC and SCADA Training is the ideal choice.</p><p>With our PLC and SCADA Training exclusive bundle, you will get<strong> a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. </strong></p><p><strong>Courses are Included in This PLC and SCADA Training Bundle:</strong></p><ul><li><strong>Course 01: </strong>PLC Programming Using Logixpro Simulator</li><li><strong>Course 02: </strong>Allen Bradley Micro 850 PLC, Panelview HMI</li><li><strong>Course 03: </strong>HMI Panelview 800 Configuring and Programming</li><li><strong>Course 04: </strong>VFD 120 Volt Powerflex 4M Configuring and Programming</li><li><strong>Course 05: </strong>Industry 4.0 - Automation &amp; Applications</li><li><strong>Course 06: </strong>Data Science &amp; Machine Learning with Python</li></ul><p><strong>Why Choose Our PLC and SCADA Training Course?</strong></p><ul><li>FREE PLC and SCADA Training certificate accredited</li><li>Get a free student ID card with PLC and SCADA Training Training</li><li>Get instant access to this PLC and SCADA Training course.</li><li>Learn PLC and SCADA Training from anywhere in the world</li><li>The PLC and SCADA Training is affordable and simple to understand</li><li>The PLC and SCADA Training is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the PLC and SCADA Training course materials</li><li>The PLC and SCADA Training comes with 24/7 tutor support</li></ul><p>So enrol now in this PLC and SCADA Training Today to advance your career!</p> <p>Start your learning journey straightaway!</p><p>This PLC and SCADA Training's curriculum has been designed by PLC and SCADA Training experts with years of PLC and SCADA Training experience behind them. The PLC and SCADA Training course is extremely dynamic and well-paced to help you understand PLC and SCADA Training with ease. You'll discover how to master the PLC and SCADA Training skill while exploring relevant and essential topics.</p><p><strong>Assessment Process</strong></p><p>Once you have completed all the courses in the PLC and SCADA Training bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        60 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 PLC and SCADA Training bundle is suitable for everyone.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This PLC and SCADA Training bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited  Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited  Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

CSCS (Construction Skills Certification Scheme)

Programmable Logic Controller (PLC)

Professional Skills Course (PSC)

Scada

PLC and SCADA Training

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

Microsoft AZ-900

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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					<div><p>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.

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        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

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

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