572 Courses

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Picture this: A vast cloud realm, brimming with data, all ready to weave tales of insight and wisdom. Welcome aboard our 'Microsoft Azure Machine Learning' odyssey! This isn't just a course; it's a canvas where technology meets art. Unravel the mysteries of Azure Machine Learning Studio, dance with data modules, and play with predictions about incomes and the price tags of fancy cars. Ready for a whirlwind romance with data? Learning Outcomes Become best pals with Microsoft Azure Machine Learning Studio - it's cooler than it sounds! Play detective with a variety of puzzling machine learning modules. Predict tomorrow! Well, not literally, but we'll dabble in forecasting incomes and car prices. Revel in the drama of data as we churn stories from raw numbers. Flip through the fascinating pages of notebooks in Azure, a bibliophile's techy delight. Why choose this Microsoft Azure Machine Learning course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Microsoft Azure Machine Learning Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Microsoft Azure Machine Learning course for? Tech aficionados itching to ride the cloud wave. Curious data wizards eager to add Azure spells to their grimoire. Project champs on a quest for the perfect cloud platform. Code artists seeking to stitch machine learning into their digital tapestry. Future stars of tech, dreaming big and aiming higher. Career path Azure Machine Learning Maestro - £45,000 - £65,000 Data Alchemist - £50,000 - £72,000 Cloud Castle Architect - £70,000 - £92,000 Machine Learning Magician - £55,000 - £78,000 Storyteller of Stats - £60,000 - £85,000 Azure Artisan - £40,000 - £60,000 Prerequisites This Microsoft Azure Machine Learning does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Microsoft Azure Machine Learning was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Azure Machine Learning Module 01: Introduction to Microsoft Azure Machine Learning Studio and Administration 00:22:00 Module 02: Various Modules in Machine Learning 00:13:00 Module 03: Prediction of Income (Automated Tutorial) 00:33:00 Module 04: Prediction of Automobile Price using Linear Regression Algorithm 00:59:00 Module 05: Dataset Processing and Analysis (Sample-1) 00:57:00 Module 06: Cross Validation for Regression (Sample-2) 00:28:00 Module 07: Clustering Group Iris data (Sample-3) 00:17:00 Module 08: Introduction on Notebook in Microsoft Azure Machine Learning Studio 00:18:00 Assignment Assignment - Microsoft Azure Machine Learning 00:00:00

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

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.

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Machine Learning and other AI: Are You Ready? Machine Learning is the latest 'hot' title in computing and Artificial Intelligence. It sounds new but is influencing your life already. Machine Learning and AI will affect more and more of your life as they mature and more enabling technologies intersect with them. Machine Learning will change many disciplines and careers, overcoming scale issues, enabling better knowledge and insights, and augmenting many professions. Are you ready? This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

In today's data-driven world, 82% of businesses demand Data Science professionals - and they're willing to pay for it! With average global salaries of Data Scientists and Machine Learning experts surpassing $120,000 annually, it's clear: the future belongs to those who understand data. Our Advanced Diploma in Data Science & Machine Learning - Level 7 is meticulously designed to offer profound knowledge and skills . This Data Science & Machine Learning bundle encompasses a comprehensive selection, from the nuances of Statistics & Probability to the intriguing applications of Machine Learning in Flutter. Also, this comprehensive Data Science & Machine Learning bundle ensures you're well-equipped with the essential knowledge required in today's competitive global market. By opting for this Data Science & Machine Learning advanced diploma, you're not just learning - you're investing in a future filled with opportunities, lucrative incomes, and immense professional demand globally. Data Science & Machine Learning Course Learning Outcomes Grasp the fundamentals of Statistics & Probability. Acquire proficiency in Python for Data Science. Understand R programming in data analytics context. Dive deep into MySQL and its applications in Data Science. Master SQL techniques for Data Visualization and Analytics. Lay a strong foundation in basic machine learning concepts. Navigate through Azure Machine Learning and its utilities. This Data Science & Machine Learning bundle course, however, is designed to be your compass, guiding you through the intricacies of the most pivotal topics. With the Advanced Diploma, you delve deep into the mathematical bedrock of data science through Statistics & Probability. Python and R, the stalwarts of data programming, are unravelled, offering insights into their unique capabilities. SQL's unmatched prowess in data visualisation and analytics is highlighted, while Azure provides a window into the world of cloud-based machine learning. Lastly, understand the synergy of machine learning and mobile applications through Flutter. The benefits? A holistic understanding, positioning you ahead in the academic sphere and offering a competitive edge should you venture into research or further studies. Courses in this Data Science & Machine Learning bundle: Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 Data Science & Machine Learning with Python R Programming for Data Science Learn MySQL from Scratch for Data Science and Analytics SQL for Data Science, Data Analytics and Data Visualization Machine Learning Basics "Azure Machine Learning" Machine Learning use in Flutter, The Complete Guide Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the Data Science & Machine Learning course as proof of their achievement. You can order the endorsed certificate for only £129 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this Data Science & Machine Learning course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the Data Science & Machine Learning course has been reviewed and approved by the QLS and meets the highest quality standards. CPD 180 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Science & Machine Learning bundle is ideal for: Aspiring Data Scientists. Statisticians looking to expand into Machine Learning. Developers keen on integrating Machine Learning in apps. Research scholars in data-driven fields. Requirements To enrol in this Data Science & Machine Learning course, all you need is a basic understanding of the English Language and an internet connection. Career path Data Scientist Machine Learning Engineer Data Analyst SQL Database Administrator Research Scientist (Data Specialization) Flutter Developer with ML Integration Certificates 8 CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - Included Delivery Charge: Inside the UK: £3.99 Outside the UK: £9.99 QLS Endorsed Hard Copy Certificate Hard copy certificate - Included Delivery Charge: Inside the UK: Free Outside the UK: £9.99 Other CPD Accredited Hard Copy Certificate Hard copy certificate - £9.99 Free Courses Hard Copy Certificates Are £9.99 Each. Delivery Charge: Inside the UK: Free Outside the UK: £9.99

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.

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Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.