1728 AI courses in Nottingham delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning 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 Deep Learning 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.

Course Duration: Half-day or full-day (can also be delivered as a 3-part virtual workshop series) Target Audience: Professionals in non-technical roles (e.g. executive assistants, HR, marketing, project managers, operations staff, trainers, and admin support) who want to use generative AI to enhance their work—without needing coding skills. Course Objectives By the end of this course, participants will be able to: Understand what generative AI is and how it works in plain language. Identify use cases relevant to their role or industry. Use popular generative AI tools (like ChatGPT, Gemini, and Copilot) confidently. Write effective prompts to get better, more relevant results. Apply AI ethically and responsibly in the workplace. Course Outline Module 1: Demystifying Generative AI What is generative AI? (Plain language explanation) How AI models like ChatGPT, Copilot, and Gemini work Examples of what generative AI can (and can’t) do Myths, risks, and benefits of AI in non-tech roles Module 2: Everyday Use Cases for Professionals Time-saving applications: Drafting emails, reports, meeting summaries Creating checklists, plans, or SOPs Enhancing creativity: Brainstorming ideas for events, campaigns, or training Writing social media posts, newsletters, or job ads Organising information: Summarising documents Structuring spreadsheets or creating templates Supporting communication: Improving tone and clarity Translating or simplifying content Module 3: Prompting Essentials What is a “prompt” and why does it matter? How to write clear, specific, and effective prompts Prompting frameworks (e.g., “Role–Task–Goal” method) Live practice: transforming a vague prompt into a powerful one Troubleshooting: when AI gives poor answers and how to improve them Module 4: Hands-On Exploration Try-it-yourself exercises using ChatGPT or Gemini (guided) Real workplace examples and team challenges Create an AI-generated email, checklist, or idea list Optional: use industry-specific examples (e.g., HR, admin, events, comms) Module 5: Responsible AI Use at Work Understanding AI limitations and biases Protecting privacy and sensitive data When not to use AI Workplace policies and guidelines (customisable for your organisation) Ethical use: attribution, transparency, and human review Module 6: Getting Started in Your Role Tools overview: free vs paid options (ChatGPT, Microsoft Copilot, Gemini) Building your own AI toolkit Tips for staying up to date as tools evolve 30-day challenge: how to build AI into your daily workflow Delivery Style Highly interactive, practical, and low-jargon No coding or tech background required Hands-on demos, guided practice, and scenario-based activities Group discussion and peer learning Course Materials Provided Quick-start guide: Top 10 prompts for non-tech roles AI Prompt Library for your job type Do’s and Don’ts for ethical use of AI at work “AI in Action” workbook with examples and checklists Personal AI Action Plan Optional Add-ons Team-based AI hackathon (mini workplace challenge) Tailored follow-up webinar for Q&A and deeper use cases Co-branded playbook for organisational AI use Integration with digital transformation or innovation initiatives

Duration 4 Days 24 CPD hours This course is intended for 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. Overview Working in a hands-on learning environment led by our expert instructor you'll: Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills. Discover the power of functional programming and learn techniques that will make your code more efficient,maintainable, and enjoyable to write. Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library. Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance. Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process. 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. 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. 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. Introduction to Scala Scala features and benefits Comparing Scala with Java and other OO languages Installing Scala and setting up the development environment Object-Oriented Programming in Scala Classes and objects Traits, mixins, and inheritance Companion objects and factories Encapsulation and polymorphism Functional Programming Basics Pure functions and referential transparency Higher-order functions and currying Immutability and persistent data structures Pattern matching and recursion Having Fun with Functional Data Structures Lists, sets, and maps in Scala Folding and reducing operations Stream processing and lazy evaluation For-comprehensions Building Web Applications in Functional Style Introduction to Play Framework Functional web routing and request handling JSON handling with Play-JSON Middleware and functional composition Connecting to a Database Introduction to Slick library Database configuration and setup Querying and updating with Slick Transactions and error handling Building Scalable and Extensible Components Modular architecture and design patterns Dependency injection with MacWire Type classes and type-level programming Implicit parameters and conversions Concurrency Programming & Akka Introduction to Akka framework and Actor model Actor systems and message passing Futures and Promises Supervision and fault tolerance Building Confidence with Testing Introduction to ScalaTest and ScalaCheck Unit testing and property-based testing Test-driven development in Scala Mocking and integration testing Interoperability between Scala and Java Calling Java code from Scala Using Java libraries in Scala projects Converting Java collections to Scala collections Writing Scala code that can be called from Java Using Generative AI and GPT Technologies in Scala Programming Overview of GPT and generative AI Integrating GPT with Scala applications Use cases and practical examples

Course Duration: Half-day or full-day (can also be delivered as a 3-part virtual workshop series) Target Audience: Professionals in non-technical roles (e.g. executive assistants, HR, marketing, project managers, operations staff, trainers, and admin support) who want to use generative AI to enhance their work—without needing coding skills. Course Objectives By the end of this course, participants will be able to: Understand what generative AI is and how it works in plain language. Identify use cases relevant to their role or industry. Use popular generative AI tools (like ChatGPT, Gemini, and Copilot) confidently. Write effective prompts to get better, more relevant results. Apply AI ethically and responsibly in the workplace. Course Outline Module 1: Demystifying Generative AI What is generative AI? (Plain language explanation) How AI models like ChatGPT, Copilot, and Gemini work Examples of what generative AI can (and can’t) do Myths, risks, and benefits of AI in non-tech roles Module 2: Everyday Use Cases for Professionals Time-saving applications: Drafting emails, reports, meeting summaries Creating checklists, plans, or SOPs Enhancing creativity: Brainstorming ideas for events, campaigns, or training Writing social media posts, newsletters, or job ads Organising information: Summarising documents Structuring spreadsheets or creating templates Supporting communication: Improving tone and clarity Translating or simplifying content Module 3: Prompting Essentials What is a “prompt” and why does it matter? How to write clear, specific, and effective prompts Prompting frameworks (e.g., “Role–Task–Goal” method) Live practice: transforming a vague prompt into a powerful one Troubleshooting: when AI gives poor answers and how to improve them Module 4: Hands-On Exploration Try-it-yourself exercises using ChatGPT or Gemini (guided) Real workplace examples and team challenges Create an AI-generated email, checklist, or idea list Optional: use industry-specific examples (e.g., HR, admin, events, comms) Module 5: Responsible AI Use at Work Understanding AI limitations and biases Protecting privacy and sensitive data When not to use AI Workplace policies and guidelines (customisable for your organisation) Ethical use: attribution, transparency, and human review Module 6: Getting Started in Your Role Tools overview: free vs paid options (ChatGPT, Microsoft Copilot, Gemini) Building your own AI toolkit Tips for staying up to date as tools evolve 30-day challenge: how to build AI into your daily workflow Delivery Style Highly interactive, practical, and low-jargon No coding or tech background required Hands-on demos, guided practice, and scenario-based activities Group discussion and peer learning Course Materials Provided Quick-start guide: Top 10 prompts for non-tech roles AI Prompt Library for your job type Do’s and Don’ts for ethical use of AI at work “AI in Action” workbook with examples and checklists Personal AI Action Plan Optional Add-ons Team-based AI hackathon (mini workplace challenge) Tailored follow-up webinar for Q&A and deeper use cases Co-branded playbook for organisational AI use Integration with digital transformation or innovation initiatives

Course Duration: Half-day or full-day (can also be delivered as a 3-part virtual workshop series) Target Audience: Professionals in non-technical roles (e.g. executive assistants, HR, marketing, project managers, operations staff, trainers, and admin support) who want to use generative AI to enhance their work—without needing coding skills. Course Objectives By the end of this course, participants will be able to: Understand what generative AI is and how it works in plain language. Identify use cases relevant to their role or industry. Use popular generative AI tools (like ChatGPT, Gemini, and Copilot) confidently. Write effective prompts to get better, more relevant results. Apply AI ethically and responsibly in the workplace. Course Outline Module 1: Demystifying Generative AI What is generative AI? (Plain language explanation) How AI models like ChatGPT, Copilot, and Gemini work Examples of what generative AI can (and can’t) do Myths, risks, and benefits of AI in non-tech roles Module 2: Everyday Use Cases for Professionals Time-saving applications: Drafting emails, reports, meeting summaries Creating checklists, plans, or SOPs Enhancing creativity: Brainstorming ideas for events, campaigns, or training Writing social media posts, newsletters, or job ads Organising information: Summarising documents Structuring spreadsheets or creating templates Supporting communication: Improving tone and clarity Translating or simplifying content Module 3: Prompting Essentials What is a “prompt” and why does it matter? How to write clear, specific, and effective prompts Prompting frameworks (e.g., “Role–Task–Goal” method) Live practice: transforming a vague prompt into a powerful one Troubleshooting: when AI gives poor answers and how to improve them Module 4: Hands-On Exploration Try-it-yourself exercises using ChatGPT or Gemini (guided) Real workplace examples and team challenges Create an AI-generated email, checklist, or idea list Optional: use industry-specific examples (e.g., HR, admin, events, comms) Module 5: Responsible AI Use at Work Understanding AI limitations and biases Protecting privacy and sensitive data When not to use AI Workplace policies and guidelines (customisable for your organisation) Ethical use: attribution, transparency, and human review Module 6: Getting Started in Your Role Tools overview: free vs paid options (ChatGPT, Microsoft Copilot, Gemini) Building your own AI toolkit Tips for staying up to date as tools evolve 30-day challenge: how to build AI into your daily workflow Delivery Style Highly interactive, practical, and low-jargon No coding or tech background required Hands-on demos, guided practice, and scenario-based activities Group discussion and peer learning Course Materials Provided Quick-start guide: Top 10 prompts for non-tech roles AI Prompt Library for your job type Do’s and Don’ts for ethical use of AI at work “AI in Action” workbook with examples and checklists Personal AI Action Plan Optional Add-ons Team-based AI hackathon (mini workplace challenge) Tailored follow-up webinar for Q&A and deeper use cases Co-branded playbook for organisational AI use Integration with digital transformation or innovation initiatives

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

Data modelling and visualisation skills are in demand! Create Interactive Reports and Dashboards.

Who is this Course for? Unity 3D Game Design Masterclass. Unity serves as top development engine for both 2D and 3D games and experiences. With its support for VR, AR, and AI, it extends its level beyond gaming to pre-visualization for films and architectural visualization. This training provides you with the fundamental skills to craft immersive virtual worlds. Click here for more info: Website Duration: 10 hours Approach: 1on1 Sessions. Create your own schedule by booking a convenient hour between 9 am and 7 pm, Mon to Sat 10-Hour Unity 3D Game Design Training Course: Module 1: Introduction to Unity 3D (2 hours) Explore the fundamentals of Unity 3D and its relevance in game design. Familiarize yourself with the Unity 3D interface, project creation, and asset management. Understand game objects, components, and their management within Unity 3D. Module 2: Game Mechanics (2 hours) Grasp basic game concepts, player movement, and camera control. Master the creation, control, and interaction of game objects. Learn about physics, collision detection, and interactive environment design. Module 3: Scripting with C# (2 hours) Dive into the C# scripting language, covering variables, data types, and functions. Control game objects using C# and understand game loops and events. Module 4: User Interfaces and Audio (2 hours) Design intuitive user interfaces and incorporate elements like buttons and sliders. Gain expertise in audio import, editing, and managing game sound effects and background music. Module 5: Optimization and Deployment (2 hours) Master game optimization techniques, including performance enhancement through scripting. Build, test, and deploy games for various platforms, including desktop, mobile, and web. Course Highlights: Introduction to Unity: Understand the gaming landscape and Unity's essential components. Game Development Basics: Learn project creation, geometry and texture importing, terrain generation, and primitive object creation. Materials and Lighting: Explore material manipulation, lighting creation, and animation adjustments. Audio Integration: Add, modify, and interact with game sounds. Programming Tools: Gain familiarity with programming using JavaScript, including variables, functions, and simple programming. Testing and Debugging: Rigorous game testing, bug fixing, and iterative development processes. Game Building: Configure game authoring settings, build games for Windows, and test the final product. Unity Pro Integration: For this training, utilize Unity Pro, a cutting-edge development platform enabling the creation of 3D, 2D, VR, and AR experiences across diverse industries, including gaming, automotive, AEC, and film. Start your Unity Pro journey at Unity's official website. With Unity Pro, accelerate development, enhance iteration speed, and expand your business, creating remarkable games and immersive experiences supported across multiple platforms. Learning Outcomes: Unity Proficiency: Master Unity 3D essentials, including game mechanics, C# scripting, UI design, and audio integration. Optimization and Deployment: Learn to optimize games for high performance and successfully deploy them across various platforms. Effective Testing: Develop strong testing and debugging skills for efficient game development. Useful Websites: Unity Learn: Access tutorials, courses, and projects for in-depth learning. Unity Asset Store: Explore a vast library of assets, scripts, and tools for game enhancement. Unity Forums: Engage with the community, ask questions, and share knowledge with fellow developers. GitHub: Collaborate on Unity projects and access shared resources for collaborative development. Gamasutra: Stay updated with industry trends, news, and valuable insights into game development. Download Unity Software Key Details for This Course: 1. Ideal for Beginners: Learn game development with Unity, perfect for beginners. Its user-friendly interface and intuitive design make it accessible for newcomers. Dive into state logic and C# programming, building a strong foundation for 3D game development. 2. Coding Flexibility: Unity offers flexibility with coding. Explore visual scripting using Bolt, enabling idea implementation through flow graphs and state graphs, suitable for both low-level and high-level logic. Advanced users can delve into tools like PlayMaker and Adventure Creator for intricate gameplay and AI behaviors. 3. Understanding Unity's Engine: Gain insights into Unity's engine workings. Explore its role as a dynamic scene player, initializing graphics, audio, and input settings at game start. Understand scene loading, asset integration, and the seamless execution of physics simulations and events, ensuring a smooth gaming experience. Course Benefits: Comprehensive Training: Master Unity 3D for creating engaging games across platforms. Interactive Learning: Engage in live online sessions for real-time feedback and collaboration. Hands-On Experience: Apply skills through practical projects, building a strong portfolio. Flexibility: Choose between live online or in-person sessions, with recorded materials for review. Ongoing Support: Enjoy lifetime email assistance for any challenges you face even after the course. Certification: Receive an industry-recognized certification, enhancing your game development career prospects.

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