// 78 courses indexed
The course directory
Every course we track across 10+ providers, in one searchable index. Want a shortlist picked for your goals? Run a free skill gap analysis.
Applied Generative AI & Prompt Engineering Mastery
Everyone talks about AI. Few actually understand it. Most people copy prompts from Google, get inconsistent results, and quietly wonder if they are falling behind. This course closes that gap for good. Here is what you will cover: Generative AI Foundations Understand how Generative AI differs from Machine Learning and Deep Learning so you can confidently follow modern AI conversations and trends. Explore ChatGPT features, the evolution of GenAI, and tools for text, image, audio, video, and code generation. Learn how newer AI systems are becoming more capable and interactive. LLMs, Use
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Evaluate, Analyze, and Model Performance
In real-world machine learning work, building a model is only half the job. Knowing how to evaluate it, explain its weaknesses, and defend improvements is what makes your work trustworthy. In this course, you will learn how to evaluate regression and classification models using the right metrics, diagnose where models systematically fail, and determine whether performance differences actually matter. You will practice selecting RMSE and MAE for reporting housing-price models, analyzing confusion matrices to uncover false-positive patterns in spam filters, and using bootstrapping to test wheth
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Interpretable Machine Learning
As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Interpretable Machine Learning, empowering you to develop AI solutions that are aligned with responsible AI principles. You will also gain an understanding of the emerging field of Mechanistic Interpretability and its use in understanding large lang
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Introduction to Applied Machine Learning
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning app
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Working with Notebooks in Vertex AI
This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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Validate and Explain Your ML Models
This short course helps you validate and explain machine learning models with confidence. You’ll learn practical strategies for using k-fold cross-validation and stratified sampling to estimate performance more accurately, especially when working with imbalanced data. You’ll also explore feature-importance techniques, including SHAP, to understand how your model behaves and how to explain its decisions clearly to technical and non-technical audiences. Through accessible videos, short readings, and hands-on activities, you’ll strengthen your ability to evaluate models beyond a single accuracy s
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Matrix Methods
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
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Employee Attrition Prediction Using Machine Learning
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features. Note: This course works best for learners who are based in the North America regio
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Data Science with NumPy, Sets, and Dictionaries
Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators. Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackl
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Mastering NLP: Foundations, ML & LLMs
Natural Language Processing (NLP) is transforming how businesses and technologies interact with human language. This course provides a comprehensive pathway from foundational concepts to advanced large language models, equipping learners with skills that are highly valued in today’s AI-driven world. Through a structured progression, you will build strong fundamentals in mathematics, machine learning, and text processing, then apply these concepts to real-world NLP tasks. By the end of the course, you will be able to design, implement, and optimize NLP systems, including modern LLM-based appli
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Deep Learning for Business
Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open sour
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Innovating with Google Cloud Artificial Intelligence
Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. Innovating with Google Cloud Artificial Intelligence explores how organizations can use AI and ML to transform their business processes. As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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AI for Construction Management & Design
Construction professionals today face rising demands for smarter, safer, and more efficient project delivery. The AI in Construction Certification is a beginner-friendly, hands-on course that allows you to apply artificial intelligence in real construction environments to improve accuracy, reduce costs, and enhance overall project performance without any coding. This certification is built around real-world construction workflows, including project planning, quality control, and site monitoring. You’ll learn how machine learning, predictive analytics, and AI-powered tools are transforming con
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Advanced Deployment Scenarios with TensorFlow
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work,
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Tools for Data Science
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
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Introduction to Embedded Machine Learning
Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine
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Advanced AI and Machine Learning Techniques and Capstone
This course explores advanced AI & ML techniques, ending with a comprehensive capstone project. You will learn about cutting-edge ML methods, ethical considerations in GenAI, and strategies for building scalable AI systems. The capstone project allows students to apply all their learned skills to solve a real-world problem. By the end of this course, you will be able to: 1. Implement advanced ML techniques such as ensemble methods and transfer learning. 2. Analyze ethical implications and develop strategies for responsible AI. 3. Design scalable AI & ML systems for high-performance scenarios
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Deep Neural Network for Beginners Using Python
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will: - Learn to train machines to predict like humans by masteri
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Owning the AI Lifecycle in Azure
Owning the AI Lifecycle in Azure focuses on managing AI system delivery from build through deployment and ongoing operations. AI initiatives introduce new complexities in data architecture, model development, performance evaluation, and production monitoring. This course equips you to coordinate those moving parts within enterprise environments. You’ll examine cloud-native AI architecture decisions, data readiness requirements, and model development workflows using Azure Machine Learning and Microsoft Foundry models. The course explores how AutoML, generative AI, AI agents, and Copilot deploy
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Python: Logistic Regression & Supervised ML
Build a strong foundation in supervised machine learning by learning how to develop, evaluate, and interpret classification models using Python. In this hands-on course, you will work with the real-world Titanic dataset to explore the complete machine learning workflow, from project setup and data preparation to model evaluation and deployment readiness. You will begin by understanding the lifecycle of a supervised machine learning project, defining problem objectives, and using essential Python libraries such as NumPy and pandas. You will also explore core supervised learning algorithms, inc
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Advancing Your Career in Production AI
This course prepares you to leverage your advanced skills in model development, optimization, and AI ethics to pursue senior career opportunities. You will learn how to strategically position yourself for roles such as Senior Machine Learning Engineer, MLOps Engineer, and AI Ethics Specialist by building a portfolio that showcases your end-to-end project experience and preparing for advanced technical interviews focused on system design and responsible AI.
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Advanced ROS 2: Aerial Robotics, AI & Deployment
Advanced ROS 2 development is a critical skill for building intelligent, autonomous robotic systems in modern industries such as aerospace, manufacturing, and AI-driven automation. This course focuses on integrating ROS 2 with advanced domains like aerial robotics, machine learning, and deployment pipelines to create scalable and efficient robotic solutions. Through hands-on projects and practical workflows, you will learn how to design, program, and deploy robotic systems using ROS 2. From building a DIY mobile robot to implementing CI/CD pipelines and integrating large language models, this
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GitHub: Codespaces, Actions, and Ecosystem Tools
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
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Tesla Stock Price Prediction using Facebook Prophet
In this 1.5-hour long project-based course, you will learn how to build a Facebook Prophet Machine learning model in order to forecast the price of Tesla 30 days into the future. We will also visualize the historical performance of Tesla through graphs and charts using Plotly express and evaluate the performance of the model against real data using Google Finance in Google Sheets. We will also dive into a brief stock analysis of Tesla and we will discuss PE ratio, EPS, Beta, Market cap, Volume and price range of Tesla. We will end the project by automating the forecasting process in such a wa
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