485 Machine Learning (ML) courses

This Python BootCamp is Instructor-led, Practical. In the12-week Python course, learn start to in-depth, leading to a good Python career. -------------------------------------------------------------------------------- PYTHON BOOTCAMP: This 12-week Python Boot Camp is a practical, instructor-lead program, covering Python from start to in-depth. You will be fully fluent and confident as a Python programmer. If you have more questions goto https://pcworkshopslondon.co.uk/contact.html  [http://pcworkshopslondon.co.uk/contact.html], Or contact us on training@pcworkshopslondon.co.uk [https://pcworkshopslondon.co.uk/] Customise dates, course outline, arrange installments [https://pcworkshopslondon.co.uk/contact.htm] This course will give you enough practical experience and practical projects to code, to give you full confidence to enter into the coding profession.    Duration: 3 months: * 1 Python class per week, * Plus pratical work, * Plus personal trainer-mentor for 1-1 training, * Plus e-learning materials. Final project : Practical to upload to GitHub and show-case Date and times, choose: * Fridays in London or Online , 10am - 5pm, * or Saturdays in London or Online , 12noon-6pm, * or negotiate your date Study level: Start from beginners level to in-depth, ready to work professionally. Virtual attendance:  online instructor-led  Download: Anaconda.com Pre-requisites: General computer literacy. Oracle Qualification: PCWorkshops Python Programmer Certificate Payments:  You may apply to pay in installments for this Python Training course COURSE OUTLINE Week 1 - 4: Essentials 1. Python Coding Basics 2. Object Oriented programming: Python Object Orientated programming (OOP) 3. UX Principles: UX Principles and applying it on Front-ends with TKinter 4. Specialised topics: Dates, Localization, Strings, Maths Operation , Random Number, Lambdas Week 5 - 10 : All about data 1. Python Data Structures: Lists, Tuples, Sets, Exceptions, I/O Fundamentals , Reading and Writing file 2. Database: Database principles and SQL. Database Project: Python database connections and creating a database driven project 3. Data Analytics: Numpy. Pandas for data analytics and data queries. 4. Data Analytics: Pandas data cleaning and restructuring, interacticting with Excel, Csv, Json,etc. 5. Data visualisation: MatPlotLib 6. Prediction: Machine Learning Basics Week 11 and 12: The final touch 1. Python Concurrency and Multi-threading: Threads vs. Processes, Threading Module, Threading Event, Stop a Thread, Daemon Threads, Thread-safe Queue, Thread Pools, Locks 2. Python Unit Testing 3. Optional : Replace Week 11 or 12 with Scraping using Python, ot Tkinter Front-ends INCLUDED: * PCWorkshops Python Course Certificate on completion. * Python Course notes. * Practical Course exercises, Code Examples, Online Materials. * After the course, continuous assistance with practical and preparation for exams * Max group size on this is 4. * Portfolio: Post your Python project online. * Exam preparation and interview questions MORE ABOUT THE ONLINE CLASSROOM *  Attend from your internet connection *  Instructor-led, get instant answers to your questions *  Fully interactive *  Work clearly explained with demonstrations and examples *  Practical exercises to be tried out by delegate WHAT YOU WILL GAIN: * Skills & knowledge: Python coding knowledge and understanding with good practical application   * Certification: Internal PCWorkshops Python certificate * Portfolio: You will have an online portfolio of Python projects  * Experience: Our comprehensive practical makes you job ready -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Duration 1 Days 6 CPD hours This course is intended for The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. Prerequisites Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically: * Experience using computers and the internet. * Interest in use cases for AI applications and machine learning models. * A willingness to learn through hands-on exp... 1 - FUNDAMENTAL AI CONCEPTS * Understand machine learning * Understand computer vision * Understand natural language processing * Understand document intelligence and knowledge mining * Understand generative AI * Challenges and risks with AI * Understand Responsible AI 2 - FUNDAMENTALS OF MACHINE LEARNING * What is machine learning? * Types of machine learning * Regression * Binary classification * Multiclass classification * Clustering * Deep learning * Azure Machine Learning 3 - FUNDAMENTALS OF AZURE AI SERVICES * AI services on the Azure platform * Create Azure AI service resources * Use Azure AI services * Understand authentication for Azure AI services 4 - FUNDAMENTALS OF COMPUTER VISION * Images and image processing * Machine learning for computer vision * Azure AI Vision 5 - FUNDAMENTALS OF FACIAL RECOGNITION * Understand Face analysis * Get started with Face analysis on Azure 6 - FUNDAMENTALS OF OPTICAL CHARACTER RECOGNITION * Get started with Vision Studio on Azure 7 - FUNDAMENTALS OF TEXT ANALYSIS WITH THE LANGUAGE SERVICE * Understand Text Analytics * Get started with text analysis 8 - FUNDAMENTALS OF QUESTION ANSWERING WITH THE LANGUAGE SERVICE * Understand question answering * Get started with the Language service and Azure Bot Service 9 - FUNDAMENTALS OF CONVERSATIONAL LANGUAGE UNDERSTANDING * Describe conversational language understanding * Get started with conversational language understanding in Azure 10 - FUNDAMENTALS OF AZURE AI SPEECH * Understand speech recognition and synthesis * Get started with speech on Azure 11 - FUNDAMENTALS OF AZURE AI DOCUMENT INTELLIGENCE * Explore capabilities of document intelligence * Get started with receipt analysis on Azure 12 - FUNDAMENTALS OF KNOWLEDGE MINING WITH AZURE COGNITIVE SEARCH * What is Azure Cognitive Search? * Identify elements of a search solution * Use a skillset to define an enrichment pipeline * Understand indexes * Use an indexer to build an index * Persist enriched data in a knowledge store * Create an index in the Azure portal * Query data in an Azure Cognitive Search index 13 - FUNDAMENTALS OF GENERATIVE AI * What is generative AI? * Large language models * What is Azure OpenAI? * What are copilots? * Improve generative AI responses with prompt engineering 14 - FUNDAMENTALS OF AZURE OPENAI SERVICE * What is generative AI * Describe Azure OpenAI * How to use Azure OpenAI * Understand OpenAI's natural language capabilities * Understand OpenAI code generation capabilities * Understand OpenAI's image generation capabilities * Describe Azure OpenAI's access and responsible AI policies 15 - FUNDAMENTALS OF RESPONSIBLE GENERATIVE AI * Plan a responsible generative AI solution * Identify potential harms * Measure potential harms * Mitigate potential harms * Operate a responsible generative AI solution ADDITIONAL COURSE DETAILS: Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals 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 AI-900T00 - Microsoft Azure AI Fundamentals 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.

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is any IT, facilities or data centre professional who works in and around the data centre and has the responsibility to achieve and improve efficiency and environmental sustainability, whilst maintaining the availability and manageability of the data centre. Overview After completion of the course the participant will be able to: Understand the impact of data centres on the environment Describe the various environmental/energy management standards Understand the purpose and goals of the legally binding international treaties on climate change Implement various sustainable performance metrics and how to use them in the data centre environment Manage data centre environmental sustainability using international standards Set up the measurement, monitoring and reporting of energy usage Use power efficiency indicators in a variety of data centre designs Use best practices for energy savings in the electrical infrastructure and in the mechanical (cooling) infrastructure Use best practices for energy savings for the ICT equipment and data storage Understand the importance of water management and waste management Understand the different ways to use sustainable energy in the data centre Get practical tips and innovative ideas to make a data centre more sustainable The CDESS© course is aimed at providing knowledge of the standards and guidelines related to environmental sustainability, and how to move your data centre (existing or new) to a more environmentally sustainable design and operations. IMPACT OF DATA CENTRES ON THE ENVIRONMENT * Predictions in 2010 * Current situation * Outlook and commitments WHAT IS ENVIRONMENTAL SUSTAINABILITY * The importance of sustainability * Senior management commitment * Environmental sustainability framework * Sustainability policies * Performance standards and metrics * Information policies * Transparency * Awareness * Service charging models ENVIRONMENTAL MANAGEMENT * Environmental sustainability framework (ISO 14001) * Standards and guidelines ? ISO 50001 / ISO 30134 * Measurement and categories * Baselining * Trend analysis * Reporting POWER EFFIðCIENCY INDICATORS * Various eðfficiency indicators * Power Usage Effectiveness (PUE) * PUE measurement levels * Factors affecting PUE * Measurement points and intervals * PUE in mixed source environments * Measuring PUE in a mixed-use building * PUE reporting * Impact of PUE after optimising IT load ELECTRICAL ENERGY SAVINGS (ELECTRICAL) * Identifying the starting point for saving energy * Sizing of power * DC power * Generators * UPS systems * Power Factor (PF) * Energy savings on lighting ELECTRICAL ENERGY SAVINGS (MECHANICAL) * Energy savings on the cooling infrastructure * Temperature and humidity setpoints * Various energy eðcient cooling technologies * Energy savings on the airflow * Liquid cooling * Energy reusage * PUE, ERE/ERF and Control Volume ELECTRICAL ENERGY SAVINGS (ICT) * Procurement * IT equipment energy eðfficiency * ITEEsv, SMPE, SMPO * IT equipment utilisation * Server virtualisation * Open compute project ELECTRICAL ENERGY SAVINGS (DATA STORAGE) * Data management * Data storage management * Data storage equipment effiðciency WATER MANAGEMENT * Water Usage Effectiveness (WUE) * Improving WUE * Water usage at the power generation source * Energy Water Intensity Factor (EWIF) WASTE MANAGEMENT * Waste management policies * Life-cycle assessment (Cradle to the grave) * 3 R?s for waste management * Reduce * Reuse * Second-hand market * Recycle SUSTAINABLE ENERGY USAGE * Sustainable energy sources * Power purchase agreements * Energy attribute certificates * Renewable Energy Factor (REF) * Matching renewable energy supply and demand * Sustainable energy storage * Carbon trading AUTOMATED ENVIRONMENTAL MANAGEMENT SYSTEMS * Use of AI and machine learning * Load migration * Data Centre Infrastructure Management (DCIM) solutions

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 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

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!