The 2026 refresh of the best practices course, brought up to C# 14. Clean code principles, the coding conventions and guidelines that keep a codebase maintainable, when and how to refactor, and how to write code that is genuinely testable.
Everything below was built on the same conviction: an adult learning something hard does not need to be entertained, they need to be respected. Explain the why, show the real thing, and get out of the way. These have been watched by nearly half a million people — and this is the Pluralsight catalogue alone, which leaves out two decades of training built for Microsoft, Intel, HP, Cloudera and others.
Sorted so the newest work sits first. Every course links straight through to Pluralsight — filter by the part of the stack you care about.
The 2026 refresh of the best practices course, brought up to C# 14. Clean code principles, the coding conventions and guidelines that keep a codebase maintainable, when and how to refactor, and how to write code that is genuinely testable.
Feature engineering is where most machine learning projects are quietly won or lost. This course covers how generative AI changes that work — using LLMs to derive, transform, and enrich features, and knowing when the model should do the engineering and when you still should.
The output of an LLM is only ever as good as the prompt it receives. A short, practical pass at designing prompts that produce the result you actually wanted — prompt structure, best practices, advanced features, and the limits you will run into.
Agents are where generative AI stops producing output and starts producing outcomes. This course builds working agents with OpenAI AgentKit — wiring up tools, handling multi-step workflows, and structuring an agent so it holds together outside a demo.
There are many ways to solve a problem using Machine Learning. Picking the right algorithm can make the difference between success or “burning down in flames”. In this course, Machine Learning with Python - Practical Application, you’ll learn how to pick the right ML model to solve your real-world problem. First, you’ll explore the characteristics of many real-world problems that can be solved using ML. Next, you’ll discover how each one of the types of algorithms can solve a particular problem and how. Finally, you’ll learn how to pick the right algorithm for your problem. When you’re finished with this course, you’ll have the skills and knowledge of ML needed to get started working on your problem and make the world a better place.
Generative AI will not replace you, but someone using it will. In this course, Core Concepts of Generative AI for Developers, you’ll learn about the breadth, opportunity, and recent developments in the current Generative AI market. First, you’ll learn how Gen AI is changing the way business is done and revolutionizing the enterprise. Then, you'll explore the core concepts of Generative AI that you need to know to create the applications of the future. Finally, you’ll learn the different types of categories available in the Gen AI space, namely based on text, images, audio, and video.. When you’re finished with this course, you’ll have the skills and knowledge of the core concepts of Generative AI needed to understand what you can do to create epic applications.
There’s too much noise in the AI landscape, and choosing the right generative AI tools and technologies is more complex than ever. In this course, Choosing Generative AI Tools and Technologies, you’ll gain the ability to evaluate and select the tools that best fit your organization’s needs using the most current information available. First, you’ll explore the key factors that should guide your decisions, including cost, privacy, scalability, compatibility, and ease of use. Next, you’ll discover how to assess AI tools for personalization, multimodal capabilities, and how AI agents can support business operations. Finally, you’ll learn how to compare the strengths and limitations of different GenAI platforms across industries—and how to decide when to build in-house versus using third-party solutions. When you’re finished with this course, you’ll have the skills and knowledge of generative AI platforms needed to confidently align technology decisions with your business strategy.
Combining data from multiple tables is essential in relational databases. In this course, Join and Combine Data with T-SQL, you’ll learn how to write and optimize INNER, OUTER, and CROSS JOINs as well as UNION and UNION ALL operations to build powerful multi-table queries. First, you’ll explore the fundamentals of joins and how to apply them effectively based on table relationships. Next, you’ll combine data across multiple tables using INNER, LEFT, and FULL joins—while learning to handle unmatched rows, aliases, and filters. Finally, you’ll advance into complex techniques like self-joins, CROSS joins, and merging result sets with UNION and UNION ALL, including performance considerations . By the end of this course, you’ll be confident in querying and combining relational data using SQL Server.
Too many professionals end their week feeling drained but unsure what they actually accomplished. In this course, Track Deep Work Time with RescueTime, you’ll learn to measure and improve your deep work habits using real-time productivity data. First, you’ll explore how to set clear focus time goals using RescueTime. Next, you’ll discover how to review your weekly analytics and identify where time is truly going. Finally, you’ll learn how to adjust your schedule and time blocks based on that insight. When you’re finished with this course, you’ll have the skills and confidence to align your time with what matters most—and reclaim control of your focus.
AI-generated images are revolutionizing creativity and content creation. In this course, Image Generation with Midjourney, you’ll learn how to generate stunning images using only your imagination and the Midjourney platform. First, you’ll craft effective prompts, apply parameters like aspect ratio and chaos, and explore the newest Midjourney features with hands-on demos. Next, you’ll also understand how to use AI responsibly. Finally, you’ll learn how to create responsibly by evaluating ethical considerations and applying best practices for inclusive, transparent AI-generated content. By the end of this single module, you’ll be ready to create and iterate on visuals like a pro.
OpenAI's GPT-4o model and DALL-E image generation are now deeply integrated into ChatGPT, opening up powerful new creative workflows. In this course, Image Generation with OpenAI ChatGPT, you'll learn to design, iterate, and refine high-quality images and even generate short videos. First, you'll explore how GPT-4o enhances prompt fidelity and multimodal interaction. Next, you'll walk through live image editing, inpainting, and multi-turn refinement directly in the chat. Then, you'll try image-to-image generation and see how to use your own images as inputs. Finally, you'll review best practices for ethical publishing and responsible attribution. When you're finished, you’ll be ready to create polished AI visuals and integrate image generation into your real-world projects.
Generative AI has immense capabilities and awe-inspiring potential, but it can also introduce significant costs that can erode ROI if left unchecked. In this course, Effective Cost Management for Generative AI, you’ll learn how to evaluate and optimize the total cost of ownership of generative AI implementations. First, you’ll explore the major factors that impact generative AI expenses. Next, you’ll discover techniques to reduce these costs, developing strategies for efficient model use and cutting unnecessary overhead. Finally, you’ll learn how to create long-term budgeting and forecasting models for AI initiatives. When you’re finished with this course, you’ll have the skills and knowledge to manage generative AI costs effectively, ensuring your AI-driven projects remain innovative yet financially sustainable.
GPT-4.5 takes writing assistance to the next level, unlocking new and advanced writing capabilities. In this course, Improved Writing with GPT-4.5, you’ll learn how to write better content, faster. First, you’ll explore the key improvements that make GPT-4.5 feel more natural, intelligent, and reliable. Next, you’ll discover practical applications of these advancements to create content that requires less revisions and feels genuinely human. Finally, you’ll understand the model’s limitations. When you’re finished with this course, you’ll have the skills and knowledge of GPT-4.5 needed to produce high-quality content efficiently and effectively.
Integrating Gen AI into your application unlocks a whole new set of functionalities, but you have to make sure you integrate in the right way. In this course, Integrating Generative AI for Developers, you’ll learn what you need to know to add Gen AI into your application. First, you’ll explore the key technical requirements and dependencies for Gen AI deployments. Next, you’ll learn how to architect Gen AI powered applications that scale. Third, you will learn how to create a phased rollout strategy. Finally, you’ll learn how to establish performance metrics and monitoring processes for Gen AI systems. When you’re finished with this course, you’ll have the skills and knowledge of integrating Gen AI needed to enhance your applications with Gen AI functionality.
Let's build secure, high-performance APIs for AI applications using Python! This course covers REST API basics, integrating generative AI models, optimizing model inference, implementing security measures, and monitoring API performance for real-time AI capabilities. You'll explore how to integrate Python-based models with API backends and enhance performance through model inference techniques, batching, and optimization. In this course, AI-powered Python Applications: Building APIs for Generative AI Models, you'll harness the power of AI in production environments. First, you'll dive into creating REST APIs that effectively serve generative AI models, such as text generation, summarization, and data enrichment. Next, you'll discover how to optimize model inference through techniques like batching and caching to achieve high performance. Finally, you'll implement critical security measures, including user authentication, authorization, and rate-limiting, to ensure API reliability in real-time applications. By the end of this course, you’ll gain insights into creating scalable, secure APIs that bring generative AI functionalities into real-world application, providing seamless, AI-driven experiences in applications that require real-time responses.
Generative AI is a game changer, but enterprises need more. They need Gen AI on their data. In this course, Introduction to Amazon Q Business, you’ll learn how to Create, configure, and use a custom Amazon Q Business assistant. First, you’ll learn how to create a Q Business assistant. Next, you’ll discover the ways in which you can connect it to enterprise data sources. Finally, you’ll learn how to utilize the assistant for trend analysis and business insights. When you’re finished with this course, you’ll have the skills and knowledge of Amazon Q Business needed to unleash the power of generative AI in the workplace with Amazon Q Business.
Having a great idea is just the start, implementation and execution are key. Improve your chances of success by using Agile development methodology and support your efforts with the right tools: Jira and Jira Agile (formerly GreenHopper). This course covers the following PMBOK® Process Groups: Planning, Executing, Monitoring and Controlling. This course covers the following PMBOK® Knowledge Areas: Project Time Management, Project Quality Management, Project Human Resource Management, Project Communications Management.
Creating detailed and accurate product documentation can be time-consuming and challenging. In this course, Create AI-assisted Product Documentation, you’ll learn to leverage generative AI to streamline this process. First, you’ll explore how to set up and configure generative AI tools for documentation. Next, you’ll discover how to generate comprehensive and accurate product content using AI. Finally, you’ll learn how to refine and customize AI-generated documentation to fit specific product needs. When you’re finished with this course, you’ll have the skills and knowledge of AI-assisted product documentation needed to enhance your workflow and productivity.
Developing any type of application usually requires a common set of functionality, for example reading and writing files, parsing XML/JSON, calling a web API, or other similar methods. In this course, .NET BCL Fundamentals, you’ll learn to use the built-in .NET libraries, also known as base class libraries (BCL). First, you’ll explore the common type system. Next, you’ll discover how to work with input and output including how to parse files from various data interchange formats. Then, you’ll learn how to work with and query data. Next, you’ll explore the security related namespaces. You will then move forward into network programming. Additionally, you’ll learn about how to globalize and localize applications. Then, you will explore how to debug an application as well as how to create multithreaded applications. Finally, you’ll explore some lesser known System and Microsoft namespaces that may come in handy from time to time. When you’re finished with this course, you’ll have the skills and knowledge of how to avoid reinventing the wheel by using the built-in .NET base class libraries needed to create an amazing .NET application.
Computer vision applications can automate and enhance the analysis and interpretation of visual data (images/videos) beyond human capabilities. In this course, Object Detection Recognition and Tracking, you’ll learn to create an image classifier using Tensorflow. First, you’ll explore how neural networks are used for image classification. Next, you’ll learn how to create an image classifier with different levels of accuracy. Finally, you’ll learn about three different types of neural networks. When you’re finished with this course, you’ll have the skills and knowledge of computer vision for object detection recognition and tracking needed to create an image classifier.
JSON is one of the most widely used data interchange formats. It is used for serializing and transmitting structured data. In this course, Working with JSON in .NET, you’ll gain the ability to serialize and deserialize JSON in .NET. First, you’ll explore the basics of serialization and deserialization. Next, you’ll discover how to control serialization behavior in mostly any scenario available. Finally, you’ll learn how to use advanced techniques to optimize performance. When you’re finished with this course, you’ll have the skills and knowledge of JSON in .NET needed to work with JSON using the System.Text.Json base class library.
Generative AI is a turning point in human history. Those who leverage LLMs will be more productive, creative, efficient, and will be able to achieve more with less. In this course, Developing Generative AI Applications with Python and OpenAI (ChatGPT), you’ll gain the ability to create generative AI applications. First, you’ll learn about the fundamentals of generative AI models, including their architecture, training processes, and applications. At this point you’ll learn how to write good prompts, which is an extremely valuable skill. Next, you’ll familiarize yourself with the OpenAI API and the available models, third you’ll use the API to generate human-like responses to questions or generate content based on your prompts. Moving forward, you will learn how to create a basic chatbot. Finally, you’ll learn how to train a model using your own data. When you’re finished with this course, you’ll have the skills and knowledge of how to create a generative AI application using the OpenAI API and Python.
Maintaining code is not easy, especially when it is poorly written and hard to understand. In this course, C# Best Practices, you’ll learn how to create clean code. First, you’ll learn the clean code principles. Next, you’ll discover which are the coding conventions and guidelines that you need to follow when writing code, including how to and when to refactor. Finally, you’ll learn how to create testable code. When you’re finished with this course, you’ll have the skills and knowledge of clean code needed to write code that is easy to maintain and extend, by following the C# best practices and coding conventions.
Source code management is an absolute must and Git is the best source control system out. However, learning Git is usually a lot harder than it should be, especially from the command line. In this course, Using Git with a GUI, you’ll learn how to use Git with a very gentle learning curve. First, you’ll explore why Git is the way to go when using a source code management system. Next, you’ll discover the mechanics of using Git, followed by learning the advanced actions available that can save you from a headache or two. Finally, you’ll learn about the available branching strategies at your disposal. When you’re finished with this course, you’ll have the skills and knowledge of Git and SourceTree needed to work with Git using a GUI.
Loading data is one of the most important skills you can have. Do you know which is one of the most powerful and widely used languages to work with data? If you guessed Python, you are right. In this course, Importing Text Files in Python, you’ll gain the ability to load in the most efficient way text and tabular data. First, you’ll explore how to import text and flat files. Next, you’ll discover how to load numerical data using Numpy. Finally, you’ll learn how to load and import tabular data using Pandas. When you’re finished with this course, you’ll have the skills and knowledge of importing text and flat files needed to load numerical and tabular data in Python.
Understanding how to work with files is an absolute must when developing applications with Python. In this course, Working with Files in Python, you’ll gain the ability to create files, append text, read from files, find files, create and locate directories, and more. First, you’ll learn the differences between working with files in different operating systems. Next, you’ll explore how to create and locate directories. Finally, you’ll discover how to find, create, delete, and rename files as well as how to read and write to them. When you’re finished with this course, you’ll have the skills and knowledge of file handling needed to create Python applications that require working with files.
The rising popularity of the web, mainly around JavaScript related technologies, has given JSON a great deal of importance over other data interchange formats, like XML. In this course, Getting Started with JSON in C# Using Json.NET 12, you will learn foundational knowledge that will allow you to work with JSON in .NET. First, you will learn the principles behind serialization fundamentals. Next, you will discover how to control and customize serialization using settings and attributes. Then, you will explore advanced serialization and deserialization techniques, including LINQ to JSON. Finally, you will learn about the future of Json.NET with .NET Core and .NET 5. When you're finished with this course, you will have the skills and knowledge needed to work with JSON in .NET.
At the core of working with large-scale datasets is a thorough knowledge of Big Data platforms like Apache Spark and Hadoop. In this course, Developing Spark Applications Using Scala & Cloudera, you’ll learn how to process data at scales you previously thought were out of your reach. First, you’ll learn all the technical details of how Spark works. Next, you’ll explore the RDD API, the original core abstraction of Spark. Then, you’ll discover how to become more proficient using Spark SQL and DataFrames. Finally, you'll learn to work with Spark's typed API: Datasets. When you’re finished with this course, you’ll have a foundational knowledge of Apache Spark with Scala and Cloudera that will help you as you move forward to develop large-scale data applications that enable you to work with Big Data in an efficient and performant way.
At the core of working with large-scale datasets is a thorough knowledge of Big Data platforms like Apache Spark and Hadoop. In this course, Developing Spark Applications with Python & Cloudera, you’ll learn how to process data at scales you previously thought were out of your reach. First, you’ll learn all the technical details of how Spark works. Next, you’ll explore the RDD API, the original core abstraction of Spark. Finally, you’ll discover how to become more proficient using Spark SQL and DataFrames. When you’re finished with this course, you’ll have a foundational knowledge of Apache Spark with Python and Cloudera that will help you as you move forward to develop large-scale data applications that enable you to work with Big Data in an efficient and performant way.
Technology moves and courses get retired — Hadoop clusters, Solr, DALL·E 3, prompt engineering for a model two generations back. I am leaving the list here because it is an honest map of where this industry has been since 2014, and because a few of them were the best work I had done at the time.
Sixty-four courses taught me exactly where video stops working on its own — which is the reason Lupo.ai exists. If that problem is yours too, start with the three pillars.