481 Machine Learning (ML) courses delivered Online

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Duration 2 Days 12 CPD hours This course is intended for The target audience for the DevOps Foundation course includes Management, Operations, Developers, QA and Testing professionals such as: Individuals involved in IT development IT operations or IT service management. Individuals who require an understanding of DevOps principles. IT professionals working within, or about to enter, an Agile Service Design Environment The following IT roles: Automation Architects, Application Developers, Business Analysts, Business Managers, Business Stakeholders, Change Agents, Consultants, DevOps Consultants, DevOps Engineers, Infrastructure Architect, Integration Specialists, IT Directors, IT Managers, IT Operations, IT Team Leaders, Lean Coaches, Network Administrators, Operations Managers, Project Managers, Release Engineers, Software Developers, Software Tester/QA, System Administrators, Systems Engineers, System Integrators, Tool Providers. Overview The learning objectives for DevOps Foundation include an understanding of: DevOps objectives and vocabulary Benefits to the business and IT Principles and practices including Continuous Integration, Continuous Delivery, testing, security and the Three Ways DevOps relationship to Agile, Lean and ITSM Improved workflows, communication and feedback loops Automation practices including deployment pipelines and DevOps toolchains Scaling DevOps for the enterprise Critical success factors and key performance indicators Real-life examples and results The DevOps Foundation course provides a baseline understanding of key DevOps terminology to ensure everyone is talking the same language and highlights the benefits of DevOps to support organizational success. Learners will gain an understanding of DevOps, the cultural and professional movement that stresses communication, collaboration, integration, and automation to improve the flow of work between software developers and IT operations professionals. This course prepares you for the DevOps Foundation (DOFD) certification. EXPLORING DEVOPS * Defining DevOps * Why Does DevOps Matter? * CORE DEVOPS PRINCIPLES * The Three Ways * The First Way * The Theory of Constraints * The Second Way * The Third Way * Chaos Engineering * Learning Organizations KEY DEVOPS PRACTICES * Continuous Testing, Integration, Delivery, Deployment * Site Reliability & Resilience Engineering * DevSecOps * ChatOps * Kanban BUSINESS AND TECHNOLOGY FRAMEWORKS * Agile * ITSM * Lean * Safety Culture * Learning Organizations * Continuous Funding CULTURE, BEHAVIORS & OPERATING MODELS * Defining Culture * Cultural Debt * Behavioral Models * Organizational maturity models AUTOMATION & ARCHITECTING DEVOPS TOOLCHAINS * CI/CD * Cloud, Containers, and Microservices * AI and Machine Learning * Automation * DevOps Toolchains MEASUREMENT, METRICS, AND REPORTING * The Importance of Measurement * DevOps Metrics - Speed, Quality, Stability, Culture * Change lead/cycle time * Value Driven Metrics SHARING, SHADOWING AND EVOLVING * DevOps in the Enterprise * Roles * DevOps Leadership * Organizational Considerations * Getting Started * Challenges, Risks, and Critical Success Factors ADDITIONAL COURSE DETAILS: Nexus Humans DevOps Foundation (DevOps Institute) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. 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. While we feel this is the best course for the DevOps Foundation (DevOps Institute) 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. 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.

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.

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Thinking about learning more about Artificial Intelligence? The BCS Foundation Certificate in Artificial Intelligence is the advanced version of our Essentials Course Artificial Intelligence and includes more detail and insights about algebraic equations, vector calculus and schematics used in artificial intelligence and machine learning for you to learn how this new technology works.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. SOLVING BUSINESS PROBLEMS USING AI AND ML * Topic A: Identify AI and ML Solutions for Business Problems * Topic B: Formulate a Machine Learning Problem * Topic C: Select Approaches to Machine Learning PREPARING DATA * Topic A: Collect Data * Topic B: Transform Data * Topic C: Engineer Features * Topic D: Work with Unstructured Data TRAINING, EVALUATING, AND TUNING A MACHINE LEARNING MODEL * Topic A: Train a Machine Learning Model * Topic B: Evaluate and Tune a Machine Learning Model BUILDING LINEAR REGRESSION MODELS * Topic A: Build Regression Models Using Linear Algebra * Topic B: Build Regularized Linear Regression Models * Topic C: Build Iterative Linear Regression Models BUILDING FORECASTING MODELS * Topic A: Build Univariate Time Series Models * Topic B: Build Multivariate Time Series Models BUILDING CLASSIFICATION MODELS USING LOGISTIC REGRESSION AND K-NEAREST NEIGHBOR * Topic A: Train Binary Classification Models Using Logistic Regression * Topic B: Train Binary Classification Models Using k-Nearest Neighbor * Topic C: Train Multi-Class Classification Models * Topic D: Evaluate Classification Models * Topic E: Tune Classification Models BUILDING CLUSTERING MODELS * Topic A: Build k-Means Clustering Models * Topic B: Build Hierarchical Clustering Models BUILDING DECISION TREES AND RANDOM FORESTS * Topic A: Build Decision Tree Models * Topic B: Build Random Forest Models BUILDING SUPPORT-VECTOR MACHINES * Topic A: Build SVM Models for Classification * Topic B: Build SVM Models for Regression BUILDING ARTIFICIAL NEURAL NETWORKS * Topic A: Build Multi-Layer Perceptrons (MLP) * Topic B: Build Convolutional Neural Networks (CNN) * Topic C: Build Recurrent Neural Networks (RNN) OPERATIONALIZING MACHINE LEARNING MODELS * Topic A: Deploy Machine Learning Models * Topic B: Automate the Machine Learning Process with MLOps * Topic C: Integrate Models into Machine Learning Systems MAINTAINING MACHINE LEARNING OPERATIONS * Topic A: Secure Machine Learning Pipelines * Topic B: Maintain Models in Production

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

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

COURSE OVERVIEW 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. The Machine Learning for Apps Level 4 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. 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. This best selling Machine Learning for Apps Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning for Apps Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Machine Learning for Apps Level 4 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 Machine Learning for Apps Level 4 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. After successfully completing the Machine Learning for Apps Level 4, 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 for Apps Level 4 will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Machine Learning for Apps Level 4 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.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.