The 10 best Git courses in 2026

This course delves into the transformative methodologies of DevOps and GitOps, equipping you with the skills to streamline software development and deployment processes. Through engaging lessons, you'll explore the origins and evolution of DevOps, understand the intricacies of Continuous Integration and Continuous Deployment (CI/CD), and gain hands-on experience with Git, the cornerstone of version control. Additionally, you'll learn to create efficient pipelines to automate workflows, enhancing productivity and collaboration within your team. By the end of this course, you'll be proficient in

Welcome to the Software Development Practices course! In this course, you will explore techniques for eliciting and documenting software requirements, including the creation of use cases and user stories. You will learn to design software systems using visual modeling methods such as UML diagrams and Data Flow Diagrams (DFDs). Throughout this learning journey, you will acquire the skills to produce high-quality code, leverage libraries and APIs, and efficiently manage code with version control tools like Git and GitHub. You will also delve into contemporary deployment and DevOps strategies, en

Learn to accelerate your software development workflow by combining GitHub Copilot with test-driven development, system-wide refactoring, and infrastructure-as-code generation. This course teaches you to use AI assistance at every stage of code quality — from writing your first test to deploying containerized applications. You will start with AI-assisted test-driven development, using GitHub Copilot to generate test cases, mock dependencies, and evaluate test coverage with pytest. You will then move to system-wide refactoring, leveraging @workspace references to analyze cross-file dependencie

This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.

Learn to evaluate, select, and integrate AI models using GitHub Models — a service that provides ready-to-use, off-the-shelf machine learning models directly within the GitHub platform. You will navigate the GitHub Models marketplace to compare models by provider, capability, and rate limits, then test them interactively using the built-in playground with system prompts and temperature controls. This course covers the practical skills needed to move from model evaluation to production integration. You will understand how rate limits work across different models, learn strategies for scaling b

This course builds a strong foundation in infrastructure automation using Ansible, equipping you with the skills to replace manual server provisioning with consistent, repeatable, and scalable automation workflows. You’ll begin by understanding why infrastructure automation is essential in modern IT environments. Instead of relying on manual configuration and repetitive setup processes, you’ll learn how Infrastructure as Code (IaC) brings structure, reliability, and version control to server management. You’ll explore Ansible’s agentless architecture, core components such as inventories, modu

This introductory course is designed for beginners with no prior knowledge of generative AI. You will start by gaining a high-level understanding of what generative AI is and how it works. Through interactive lessons and hands-on examples, you will learn fundamental skills like providing effective prompts and iteratively improving the generated outputs. As the course progresses, you will dive deeper into specific major generative AI models, including their unique capabilities and limitations. Finally,, you will get practical experience using leading systems like GitHub Copilot, DALL-E, and Ope

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what prog

A principle of DevOps is to replace manual processes with automation to improve efficiency, reduce human error, and accelerate software delivery. This requires automation that continuously integrates code changes and continuously delivers those changes to a production environment. This course introduces you to Continuous Integration and Continuous Delivery (CI/CD), an automated approach to software development. You’ll discover the benefits of CI/CD for creating a DevOps pipeline and explore popular CI/CD tools. You’ll examine the key features of CI, explore social coding, and the Git Feat

Learn to build cloud-based development environments with GitHub Codespaces, run GPU-accelerated AI workloads, use GitHub Copilot for AI-assisted coding, and automate CI/CD pipelines with GitHub Actions. This hands-on course walks you through launching Codespaces from repository templates, configuring dev containers for different machine types, and running NVIDIA GPU instances for machine learning tasks. You will use Whisper for speech-to-text transcription on GPU-enabled Codespaces and explore Hugging Face for model hosting, datasets, and fine-tuning pre-trained models. The course demonstrates