Master your path
To become an expert in machine learning, you first need a strong foundation in four learning areas: coding, math, ML theory, and how to build your own ML project from start to finish.
The four areas of machine learning education
When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects. Start with our guided curriculums designed to increase your knowledge, or choose your own path by exploring our resource library.
Start learning with one of our guided curriculums containing recommended courses, books, and videos.
Learn the basics of ML with this collection of books and online courses. You will be introduced to ML with scikit-learn, guided through deep learning using TensorFlow 2.0, and then you will have the opportunity to practice what you learn with beginner tutorials.
Once you understand the basics of machine learning, take your abilities to the next level by diving into theoretical understanding of neural networks, deep learning, and improving your knowledge of the underlying math concepts.
Reading is one of the best ways to understand the foundations of ML and deep learning. Books can give you the theoretical understanding necessary to help you learn new concepts more quickly in the future.
This introductory book provides a code-first approach to learn how to implement the most common ML scenarios, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes.
Using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—this book helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.
Taking a multi-part online course is a good way to learn the basic concepts of ML. Many courses provide great visual explainers, and the tools needed to start applying machine learning directly at work, or with your personal projects.
DeepLearning.AIDeep Learning Specialization
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects and build a career in AI. You will master not only the theory, but also see how it is applied in industry.
To go deeper with your ML knowledge, these resources can help you understand the underlying math concepts necessary for higher level advancement.
Imperial College LondonMathematics for Machine Learning Specialization
This online specialization from Coursera aims to bridge the gap of mathematics and machine learning, getting you up to speed in the underlying mathematics to build an intuitive understanding, and relating it to Machine Learning and Data Science.
This introductory course from MIT covers matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.
This book provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex world of datasets needed to train models in machine learning.
We've gathered our favorite resources to help you get started with TensorFlow libraries and frameworks specific to your needs. Jump to our sections for TensorFlow.js, TensorFlow Lite, and TensorFlow Extended.
You can also browse the official TensorFlow guide and tutorials for the latest examples and colabs.
Learn to spot the most common ML use cases including analyzing multimedia, building smart search, transforming data, and how to quickly build them into your app with user-friendly tools.
Get a hands-on look at how to put together a production pipeline system with TFX. We'll quickly cover everything from data acquisition, model building, through to deployment and management.
This book walks you through the steps of automating an ML pipeline using the TensorFlow ecosystem. The machine learning examples in this book are based on TensorFlow and Keras, but the core concepts can be applied to any framework.
DeepLearning.AIMachine Learning Engineering for Production (MLOps) Specialization
Expand your production engineering capabilities in this four-course specialization. Learn how to conceptualize, build, and maintain integrated systems that continuously operate in production.
When designing an ML model, or building AI-driven applications, it's important to consider the people interacting with the product, and the best way to build fairness, interpretability, privacy, and security into these AI systems.