Name: Determining Data Value
Price: 65 GBP
Availability: InStock

<p>In the UK and elsewhere, machine learning and artificial intelligence algorithms are increasingly being used to analyse vast amounts of data to identify patterns indicative of fraudulent activity. These advanced systems can detect anomalies and potential fraud in real-time, often much faster than traditional methods. The use of predictive analytics helps financial institutions and law enforcement stay one step ahead of fraudsters</p><p>In this course as you navigate through the content, you'll gain a nuanced understanding of the various dimensions of fraud, from its overarching concepts to the specific intricacies of banking frauds. We unveil the cloak of deception, leading you through the labyrinth of fraud prevention strategies and the intricacies of detection methods.</p><p>This course goes beyond the theoretical, providing you with tangible insights into the tangible evidence that underpins fraud investigations. </p><p>This course is not just a compilation of information; it's a journey that equips you with the tools to proactively combat fraud in a constantly evolving landscape. Join us and emerge as a vigilant guardian against the pervasive threat of fraudulent activities.</p><p><strong>Key Features</strong></p><ul><li>CPD Accredited</li><li>FREE PDF + Hardcopy certificate</li><li>Fully online, interactive course</li><li>Self-paced learning and laptop, tablet and smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum</strong></p><ul><li>Module 01: An Overview of Fraud</li><li>Module 02: Types of Fraud</li><li>Module 03: Banking Frauds</li><li>Module 04: Fraud Prevention</li><li>Module 05: Fraud Detection</li><li>Module 06: Fraud Evidence</li><li>Module 07: Fraud Response</li><li>Module 08: Fraud Investigations</li><li>Module 09: Bribery</li><li>Module 10: Laws &amp; Legislation and Court Room</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Identify various fraud types for proactive prevention.</li><li>Implement effective strategies to detect and investigate banking frauds.</li><li>Analyse and present compelling fraud evidence in legal contexts.</li><li>Develop a robust response plan to mitigate the impact of fraud.</li><li>Navigate bribery challenges through informed decision-making.</li><li>Demonstrate a comprehensive understanding of relevant laws and legislation.</li></ul><p><strong>Accreditation</strong></p><p>This course is <strong>CPD Quality Standards</strong> (<strong>CPD QS)</strong> accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certificate</strong></p><p>After completing this course, you will get a FREE Digital Certificate from Training Express.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Finance professionals seeking fraud expertise.</li><li>Law enforcement personnel focusing on financial crimes.</li><li>Risk management specialists enhancing fraud prevention skills.</li><li>Legal professionals involved in fraud-related cases.</li><li>Auditors aiming to strengthen fraud detection capabilities.</li><li>Compliance officers ensuring adherence to anti-fraud measures.</li><li>Corporate investigators tackling internal fraud challenges.</li><li>Students pursuing a career in fraud detection and prevention.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Fraud Analyst</li><li>Financial Investigator</li><li>Compliance Officer</li><li>Forensic Accountant</li><li>Risk Management Specialist</li><li>Banking Security Consultant</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Training Express

Fraud Detection and Prevention Insights Course

General Admission

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><b data-stringify-type="bold" style="color: rgb(209, 210, 211); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(209, 210, 211);"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Welcome to Remote Sensing Technology: Applications and Analysis. In this course, you&#39;ll learn:</span></b></p><ol style="color: rgb(236, 236, 236); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(236, 236, 236);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Introduction to Remote Sensing:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Definition and principles of remote sensing</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Historical overview and evolution</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Importance and relevance in contemporary scientific and industrial applications</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Electromagnetic Spectrum and Remote Sensing:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding the electromagnetic spectrum</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Interaction of electromagnetic waves with Earth&#39;s surface</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Spectral bands and their significance in remote sensing</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Platforms and Sensors:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Various platforms used in remote sensing (satellites, aircraft, drones)</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Types of sensors (passive and active)</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Selection criteria based on application requirements</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Image Interpretation:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Basics of image interpretation</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Identifying features, patterns, and anomalies</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Integration of ancillary data for accurate interpretation</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Remote Sensing Data Analysis:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Pre-processing techniques for satellite imagery</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Image enhancement and classification methods</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Quantitative analysis using GIS tools</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Applications of Remote Sensing in the UK:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Land cover and land use mapping</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Environmental monitoring and conservation</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Urban planning and infrastructure development</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Agriculture and forestry applications</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Disaster management and risk assessment</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Future Trends in Remote Sensing:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Emerging technologies and sensors</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Integration with artificial intelligence and machine learning</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Advancements in spatial and temporal resolution</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Global collaboration and data sharing initiatives</li></ul></li></ol>

John Academy

Technology

Explore the world of Remote Sensing Technology with our comprehensive course on applications and analysis. Gain insights into the electromagnetic spectrum, platforms, and sensors, and master image interpretation and data analysis techniques. Discover real-world applications in the UK and unlock future trends shaping this dynamic field. Elevate your skills in environmental monitoring, GIS, and cutting-edge technology. Join us on a journey through the skies and harness the power of Remote Sensing for diverse scientific and industrial applications. Enroll now for a transformative learning experience!

Remote Sensing Technology: Applications and Analysis

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for the following participants:<br />Individuals planning to deploy applications and create application environments on Google Cloud<br />Developers, systems operations professionals, and solution architects getting started with Google Cloud.<br />Executives and business decision makers evaluating the potential of Google Cloud to address their business needs.</p>Overview<p>This course teaches participants the following skills:<br />Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake<br />Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more<br />Manage and monitor applications<br />Explain feature and pricing model differences</p><p>This course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants will get details of similarities, differences, and initial how-tos quickly.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud</h4><ul><li>Explain the advantages of Google Cloud.</li><li>Define the components of Google's network infrastructure,including: Points of presence, data centers, regions, and zones.</li><li>Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS)</li></ul><h4>Getting Started with Google Cloud</h4><ul><li>Identify the purpose of projects on Google Cloud Platform.</li><li>Understand how AWS?s resource hierarchy differs from Google Cloud?s.</li><li>Understand the purpose of and use cases for Identity and Access Management.</li><li>Understand how AWS IAM differs from Google Cloud IAM.</li><li>List the methods of interacting with Google Cloud Platform.</li><li>Launch a solution using Cloud Marketplace.</li></ul><h4>Virtual Machines in the Cloud</h4><ul><li>Identify the purpose and use cases for Google Compute Engine.</li><li>Understand the basics of networking in Google Cloud Platform.</li><li>Understand how Amazon VPC differs from Google VPC.</li><li>Understand the similarities and differences between Amazon EC2 and Google Compute Engine.</li><li>Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS.</li><li>Deploy applications using Google Compute Engine.</li></ul><h4>Storage in the Cloud</h4><ul><li>Understand the purpose of and use cases for: Cloud Storage,Cloud SQL, Cloud Bigtable and Cloud Datastore.</li><li>Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage.</li><li>Compare Google Cloud?s managed database services with Amazon RDS and Amazon Aurora.</li><li>Learn how to choose among the various storage options on Google Cloud Platform.</li><li>Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery.</li><li></li></ul><h4>Containers in the Cloud</h4><ul><li>Define the concept of a container and identify uses for containers.</li><li>Identify the purpose of and use cases for Google Container Engine and Kubernetes.</li><li>Understand how Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE.</li><li>Provision a Kubernetes cluster using Kubernetes Engine.</li><li>Deploy and manage Docker containers using kubectl</li></ul><h4>Applications in the Cloud</h4><ul><li>Understand the purpose of and use cases for Google App Engine.</li><li>Contrast the App Engine Standard environment with the App Engine Flexible environment.</li><li>Understand how App Engine differs from Amazon Elastic Beanstalk.</li><li>Understand the purpose of and use cases for Google Cloud Endpoints.</li><li></li></ul><h4>Developing, Deploying and Monitoring in the Cloud</h4><ul><li>Understand options for software developers to host their source code.</li><li>Understand the purpose of template-based creation and management of resources.</li><li>Understand how Cloud Deployment Manager differs from AWS CloudFormation.</li><li>Understand the purpose of integrated monitoring, alerting, and debugging.</li><li>Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail.</li><li>Create a Deployment Manager deployment.</li><li>Update a Deployment Manager deployment.</li><li>View the load on a VM instance using Google Monitoring.</li></ul><h4>Big Data and Machine Learning in the Cloud</h4><ul><li>Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.</li><li>Understand how Google Cloud BigQuery differs from AWS Data Lake.</li><li>Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus.</li><li>Understand how Google Cloud?s machine-learning APIs differ from AWS's.</li><li>Load data into BigQuery from Cloud Storage.</li><li>Perform queries using BigQuery to gain insight into data.</li></ul><h4>Summary and Review</h4><ul><li>Review the products that make up Google Cloud and remember how to choose among them </li><li>Understand next steps for training and certification</li><li>Understand, at a high level, the process of migrating from AWS to Google Cloud.</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Google Cloud Fundamentals for AWS Professionals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Google Cloud Fundamentals for AWS Professionals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

AWS Certified Cloud Practitioner

Google Suite

Google Cloud

Google Workspace

AWS (Amazon Web Services)

Google Digital Marketing

Google Cloud Fundamentals for AWS Professionals

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills.<br />Discover the power of functional programming and learn techniques that will make your code more efficient, maintainable, and enjoyable to write.<br />Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library.<br />Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance.<br />Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process.<br />Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects, adding a touch of innovation and intelligence to your Scala solutions.<br />If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.</p><p>Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects.</p></div><div id="course-pills-2"><h4>Introduction to Scala </h4><ul><li>Scala features and benefits </li><li>Comparing Scala with Java and other OO languages </li><li>Installing Scala and setting up the development environment </li></ul><h4>Object-Oriented Programming in Scala</h4><ul><li>Classes and objects </li><li>Traits, mixins, and inheritance </li><li>Companion objects and factories </li><li>Encapsulation and polymorphism </li></ul><h4>Functional Programming Basics</h4><ul><li>Pure functions and referential transparency </li><li>Higher-order functions and currying </li><li>Immutability and persistent data structures </li><li>Pattern matching and recursion </li></ul><h4>Having Fun with Functional Data Structures </h4><ul><li>Lists, sets, and maps in Scala </li><li>Folding and reducing operations </li><li>Stream processing and lazy evaluation </li><li>For-comprehensions </li></ul><h4>Building Web Applications in Functional Style</h4><ul><li>Introduction to Play Framework </li><li>Functional web routing and request handling </li><li>JSON handling with Play-JSON Middleware and functional composition </li></ul><h4>Connecting to a Database</h4><ul><li>Introduction to Slick library </li><li>Database configuration and setup </li><li>Querying and updating with Slick </li><li>Transactions and error handling</li></ul><h4>Building Scalable and Extensible Components</h4><ul><li>Modular architecture and design patterns </li><li>Dependency injection with MacWire </li><li>Type classes and type-level programming </li><li>Implicit parameters and conversions</li></ul><h4>Concurrency Programming &amp; Akka </h4><ul><li>Introduction to Akka framework and Actor model </li><li>Actor systems and message passing </li><li>Futures and Promises </li><li>Supervision and fault tolerance </li></ul><h4>Building Confidence with Testing </h4><ul><li>Introduction to ScalaTest and ScalaCheck </li><li>Unit testing and property-based testing </li><li>Test-driven development in Scala </li><li>Mocking and integration testing</li></ul><h4>Interoperability between Scala and Java</h4><ul><li>Calling Java code from Scala </li><li>Using Java libraries in Scala projects </li><li>Converting Java collections to Scala collections </li><li>Writing Scala code that can be called from Java </li></ul><h4>Using Generative AI and GPT Technologies in Scala Programming</h4><ul><li>Overview of GPT and generative AI </li><li>Integrating GPT with Scala applications </li><li>Use cases and practical examples</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Fast Track to Scala Programming Essentials for OO / Java Developers  (TTSCL2104) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Fast Track to Scala Programming Essentials for OO / Java Developers  (TTSCL2104) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Scala

Java

Computing Skills

Programming Fundamentals

Programming Languages

Fast Track to Scala Programming Essentials for OO / Java Developers  (TTSCL2104)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This class is intended for experienced developers who are responsible for managing big data transformations including:<br />Extracting, loading, transforming, cleaning, and validating data.<br />Designing pipelines and architectures for data processing.<br />Creating and maintaining machine learning and statistical models.<br />Querying datasets, visualizing query results and creating reports</p>Overview<p>Design and build data processing systems on Google Cloud Platform.<br />Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.<br />Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.<br />Derive business insights from extremely large datasets using Google BigQuery.<br />Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.<br />Enable instant insights from streaming data</p><p>Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.</p></div><div id="course-pills-2"><h4>Introduction to Data Engineering</h4><ul><li>Explore the role of a data engineer.</li><li>Analyze data engineering challenges.</li><li>Intro to BigQuery.</li><li>Data Lakes and Data Warehouses.</li><li>Demo: Federated Queries with BigQuery.</li><li>Transactional Databases vs Data Warehouses.</li><li>Website Demo: Finding PII in your dataset with DLP API.</li><li>Partner effectively with other data teams.</li><li>Manage data access and governance.</li><li>Build production-ready pipelines.</li><li>Review GCP customer case study.</li><li>Lab: Analyzing Data with BigQuery.</li></ul><h4>Building a Data Lake</h4><ul><li>Introduction to Data Lakes.</li><li>Data Storage and ETL options on GCP.</li><li>Building a Data Lake using Cloud Storage.</li><li>Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.</li><li>Securing Cloud Storage.</li><li>Storing All Sorts of Data Types.</li><li>Video Demo: Running federated queries on Parquet and ORC files in BigQuery.</li><li>Cloud SQL as a relational Data Lake.</li><li>Lab: Loading Taxi Data into Cloud SQL.</li></ul><h4>Building a Data Warehouse</h4><ul><li>The modern data warehouse.</li><li>Intro to BigQuery.</li><li>Demo: Query TB+ of data in seconds.</li><li>Getting Started.</li><li>Loading Data.</li><li>Video Demo: Querying Cloud SQL from BigQuery.</li><li>Lab: Loading Data into BigQuery.</li><li>Exploring Schemas.</li><li>Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.</li><li>Schema Design.</li><li>Nested and Repeated Fields.</li><li>Demo: Nested and repeated fields in BigQuery.</li><li>Lab: Working with JSON and Array data in BigQuery.</li><li>Optimizing with Partitioning and Clustering.</li><li>Demo: Partitioned and Clustered Tables in BigQuery.</li><li>Preview: Transforming Batch and Streaming Data.</li></ul><h4>Introduction to Building Batch Data Pipelines</h4><ul><li>EL, ELT, ETL.</li><li>Quality considerations.</li><li>How to carry out operations in BigQuery.</li><li>Demo: ELT to improve data quality in BigQuery.</li><li>Shortcomings.</li><li>ETL to solve data quality issues.</li></ul><h4>Executing Spark on Cloud Dataproc</h4><ul><li>The Hadoop ecosystem.</li><li>Running Hadoop on Cloud Dataproc.</li><li>GCS instead of HDFS.</li><li>Optimizing Dataproc.</li><li>Lab: Running Apache Spark jobs on Cloud Dataproc.</li></ul><h4>Serverless Data Processing with Cloud Dataflow</h4><ul><li>Cloud Dataflow.</li><li>Why customers value Dataflow.</li><li>Dataflow Pipelines.</li><li>Lab: A Simple Dataflow Pipeline (Python/Java).</li><li>Lab: MapReduce in Dataflow (Python/Java).</li><li>Lab: Side Inputs (Python/Java).</li><li>Dataflow Templates.</li><li>Dataflow SQL.</li></ul><h4>Manage Data Pipelines with Cloud Data Fusion and Cloud Composer</h4><ul><li>Building Batch Data Pipelines visually with Cloud Data Fusion.</li><li>Components.</li><li>UI Overview.</li><li>Building a Pipeline.</li><li>Exploring Data using Wrangler.</li><li>Lab: Building and executing a pipeline graph in Cloud Data Fusion.</li><li>Orchestrating work between GCP services with Cloud Composer.</li><li>Apache Airflow Environment.</li><li>DAGs and Operators.</li><li>Workflow Scheduling.</li><li>Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.</li><li>Monitoring and Logging.</li><li>Lab: An Introduction to Cloud Composer.</li></ul><h4>Introduction to Processing Streaming Data</h4><ul><li>Processing Streaming Data.</li></ul><h4>Serverless Messaging with Cloud Pub/Sub</h4><ul><li>Cloud Pub/Sub.</li><li>Lab: Publish Streaming Data into Pub/Sub.</li></ul><h4>Cloud Dataflow Streaming Features</h4><ul><li>Cloud Dataflow Streaming Features.</li><li>Lab: Streaming Data Pipelines.</li></ul><h4>High-Throughput BigQuery and Bigtable Streaming Features</h4><ul><li>BigQuery Streaming Features.</li><li>Lab: Streaming Analytics and Dashboards.</li><li>Cloud Bigtable.</li><li>Lab: Streaming Data Pipelines into Bigtable.</li></ul><h4>Advanced BigQuery Functionality and Performance</h4><ul><li>Analytic Window Functions.</li><li>Using With Clauses.</li><li>GIS Functions.</li><li>Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.</li><li>Performance Considerations.</li><li>Lab: Optimizing your BigQuery Queries for Performance.</li><li>Optional Lab: Creating Date-Partitioned Tables in BigQuery.</li></ul><h4>Introduction to Analytics and AI</h4><ul><li>What is AI?.</li><li>From Ad-hoc Data Analysis to Data Driven Decisions.</li><li>Options for ML models on GCP.</li></ul><h4>Prebuilt ML model APIs for Unstructured Data</h4><ul><li>Unstructured Data is Hard.</li><li>ML APIs for Enriching Data.</li><li>Lab: Using the Natural Language API to Classify Unstructured Text.</li></ul><h4>Big Data Analytics with Cloud AI Platform Notebooks</h4><ul><li>What's a Notebook.</li><li>BigQuery Magic and Ties to Pandas.</li><li>Lab: BigQuery in Jupyter Labs on AI Platform.</li></ul><h4>Production ML Pipelines with Kubeflow</h4><ul><li>Ways to do ML on GCP.</li><li>Kubeflow.</li><li>AI Hub.</li><li>Lab: Running AI models on Kubeflow.</li></ul><h4>Custom Model building with SQL in BigQuery ML</h4><ul><li>BigQuery ML for Quick Model Building.</li><li>Demo: Train a model with BigQuery ML to predict NYC taxi fares.</li><li>Supported Models.</li><li>Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.</li><li>Lab Option 2: Movie Recommendations in BigQuery ML.</li></ul><h4>Custom Model building with Cloud AutoML</h4><ul><li>Why Auto ML?</li><li>Auto ML Vision.</li><li>Auto ML NLP.</li><li>Auto ML Tables.</li></ul></div>

Google Data Analytics

Data Engineering

Big Data Analytics

Data Science

Data Skills

Data Engineering on Google Cloud

Packt

Cloud Computing

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

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

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

Google Cloud Fundamentals - Core Infrastructure

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

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

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

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

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

One Education

Stock Trading Diploma Course

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

The Data Governance Coach 

Data Management

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

Determining Data Value

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

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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 & Development

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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