# Machine learning education
# Master your path
To become an expert in machine learning, you first need a strong foundation in [four learning areas](https://www.tensorflow.org/resources/learn-ml#four-areas): coding, math, ML theory, and how to build your own ML project from start to finish.
Begin with TensorFlow's [curated curriculums](https://www.tensorflow.org/resources/learn-ml#curriculums) to improve these four skills, or choose your own learning path by exploring our [resource library](https://www.tensorflow.org/resources/learn-ml/tensorflow.org/resources/learn-ml#educational-resources) below.
### 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.
- **Coding skills:** Building ML models involves much more than just knowing ML concepts---it requires coding in order to do the data management, parameter tuning, and parsing results needed to test and optimize your model.
- **Math and stats:** ML is a math heavy discipline, so if you plan to modify ML models or build new ones from scratch, familiarity with the underlying math concepts is crucial to the process.
- **ML theory:** Knowing the basics of ML theory will give you a foundation to build on, and help you troubleshoot when something goes wrong.
- **Build your own projects:** Getting hands on experience with ML is the best way to put your knowledge to the test, so don't be afraid to dive in early with a simple [colab](https://colab.sandbox.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb) or [tutorial](https://www.tensorflow.org/tutorials/keras/classification) to get some practice.
## TensorFlow curriculums
Start learning with one of our guided curriculums containing recommended courses, books, and videos.
 For beginners [Basics of machine learning with TensorFlow](https://www.tensorflow.org/resources/learn-ml/basics-of-machine-learning) Learn the basics of ML with this collection of books and online courses. You will be introduced to ML and guided through deep learning using TensorFlow 2.0. Then you will have the opportunity to practice what you learn with beginner tutorials.
 For intermediate level \& experts [Theoretical and advanced machine learning with TensorFlow](https://www.tensorflow.org/resources/learn-ml/theoretical-and-advanced-machine-learning) 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.
 For beginners [TensorFlow for JavaScript development](https://www.tensorflow.org/resources/learn-ml/basics-of-tensorflow-for-js-development) Learn the basics of developing machine learning models in JavaScript, and how to deploy directly in the browser. You will get a high-level introduction on deep learning and on how to get started with TensorFlow.js through hands-on exercises.
## Educational resources
Choose your own learning path, and explore books, courses, videos, and exercises recommended by the TensorFlow team to teach you the foundations of ML.
[:books:
Books](https://www.tensorflow.org/resources/learn-ml#books) [
Online courses](https://www.tensorflow.org/resources/learn-ml#courses) [
Math concepts](https://www.tensorflow.org/resources/learn-ml#math-concepts) [
TF resources](https://www.tensorflow.org/resources/learn-ml#other-resources) [
Human-centered AI](https://www.tensorflow.org/resources/learn-ml#human-centered-ai)
## Books
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.
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources)  [AI and Machine Learning for Coders](https://www.oreilly.com/library/view/ai-and-machine/9781492078180/) by Laurence Moroney 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.
[View book](https://www.oreilly.com/library/view/ai-and-machine/9781492078180/) Code Theory Build  [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) by Francois Chollet This book is a practical, hands-on introduction to Deep Learning with Keras.
[View book](https://www.manning.com/books/deep-learning-with-python-second-edition) Code Theory Build  [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) by Aurélien Géron 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.
[View book](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) Code Theory Build  [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville This Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general, and deep learning in particular.
Free [View book](https://www.deeplearningbook.org/) Math Theory Build  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen This book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more.
Free [View book](http://neuralnetworksanddeeplearning.com/) Code Math Theory Build  [Learning TensorFlow.js](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) by Gant Laborde A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.
[View book](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) Code Theory Build  [Deep Learning with JavaScript](https://www.manning.com/books/deep-learning-with-javascript) by Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet Written by the main authors of the TensorFlow library, this book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.
[View book](https://www.manning.com/books/deep-learning-with-javascript) Code Theory Build
## Online courses
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.
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources) 
DeepLearning.AI
[Intro to TensorFlow for AI, ML, and Deep Learning](https://www.coursera.org/learn/introduction-tensorflow) Developed in collaboration with the TensorFlow team, this course is part of the TensorFlow Developer Specialization and will teach you best practices for using TensorFlow.
[View course](https://www.coursera.org/learn/introduction-tensorflow) Code Build  [Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187) In this online course developed by the TensorFlow team and Udacity, you'll learn how to build deep learning applications with TensorFlow.
Free [View course](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187) Code Math Theory Build 
DeepLearning.AI
[TensorFlow Developer Specialization](https://www.coursera.org/specializations/tensorflow-in-practice) In this four-course Specialization taught by a TensorFlow developer, you'll explore the tools and software developers use to build scalable AI-powered algorithms in TensorFlow.
[View course](https://www.coursera.org/specializations/tensorflow-in-practice) Code Build 
Google Developers
[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. It features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Free [View course](https://developers.google.com/machine-learning/crash-course) Math Theory  [MIT 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/) In this course from MIT, you will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.
Free [View course](http://introtodeeplearning.com/) Code Math Theory Build 
DeepLearning.AI
[Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) 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.
[View course](https://www.coursera.org/specializations/deep-learning) Code Math Theory Build 
DeepLearning.AI
[TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment) You've learned how to build and train models. Now learn to navigate various deployment scenarios and use data more effectively to train your model in this four-course Specialization.
[View course](https://www.coursera.org/specializations/tensorflow-data-and-deployment) Build Code 
DeepLearning.AI
[TensorFlow: Advanced Techniques Specialization](https://www.coursera.org/specializations/tensorflow-advanced-techniques) This specialization is for software and ML engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
[View course](https://www.coursera.org/specializations/tensorflow-advanced-techniques) Build Code  [Fundamentals of Google AI for Web Based Machine Learning](https://www.edx.org/professional-certificate/google-fundamentals-of-google-ai-for-web-based-machine-learning) Learn how you can get more eyes on your cutting edge research, or deliver super powers in your web apps in future work for your clients or the company you work for with web-based machine learning.
[View course](https://www.edx.org/professional-certificate/google-fundamentals-of-google-ai-for-web-based-machine-learning) Build Code Theory
## Math concepts
To go deeper with your ML knowledge, these resources can help you understand the underlying math concepts necessary for higher level advancement.
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources) 
A friendly introduction to linear algebra for ML
A bird's-eye view of linear algebra for machine learning. Never taken linear algebra or know a little about the basics, and want to get a feel for how it's used in ML? Then this video is for you.
Free
Watch the video
Math [Video](https://www.youtube.com/watch?v=LlKAna21fLE) 
Imperial College London
[Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning?source=deprecated_spark_cdp) 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.
[View course](https://www.coursera.org/specializations/mathematics-machine-learning?source=deprecated_spark_cdp) Math Theory  [Deep learning](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) by 3Blue1Brown 3blue1brown centers around presenting math with a visuals-first approach. In this video series, you will learn the basics of a neural network and how it works through math concepts.
Free [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) Math Theory  [Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) by 3Blue1Brown A series of short, visual videos from 3blue1brown that explain the geometric understanding of matrices, determinants, eigen-stuffs and more.
Free [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) Math  [Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) by 3Blue1Brown A series of short, visual videos from 3blue1brown that explain the fundamentals of calculus in a way that give you a strong understanding of the fundamental theorems, and not just how the equations work.
Free [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) Math  [MIT 18.06: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) 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.
Free [View course](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) Math  [MIT 18.01: Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) This introductory calculus course from MIT covers differentiation and integration of functions of one variable, with applications.
Free [View course](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) Math  [Seeing Theory](https://seeing-theory.brown.edu/) by Daniel Kunin, Jingru Guo, Tyler Dae Devlin, Daniel Xiang A visual introduction to probability and statistics.
Free [Learn more](https://seeing-theory.brown.edu/) Math  [An Introduction to Statistical Learning](https://statlearning.com/) by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani 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.
Free [View book](https://statlearning.com/) Math
## TensorFlow resources
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](https://www.tensorflow.org/resources/learn-ml#js), [LiteRT](https://www.tensorflow.org/resources/learn-ml#lite), and [TFX](https://www.tensorflow.org/resources/learn-ml#tfx).
You can also browse the official TensorFlow [guide](https://www.tensorflow.org/guide) and [tutorials](https://www.tensorflow.org/tutorials) for the latest examples and colabs.
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources)  [Machine Learning Foundations](https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV) Machine Learning Foundations is a free training course where you'll learn the fundamentals of building machine learned models using TensorFlow.
Free [View series](https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV) Code Theory Build 
TensorFlow from the Ground Up
This ML Tech Talk is designed for those that know the basics of Machine Learning but need an overview on the fundamentals of TensorFlow (tensors, variables, and gradients without using high level APIs).
Free
Watch the video
Code Theory [Video](https://www.youtube.com/watch?v=3LLZzi48iB8) 
Intro to Deep Learning
This ML Tech Talk includes representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow.
Free
Watch the video
Code Theory [Video](https://www.youtube.com/watch?v=AhE8RhPGH1A)  [Coding TensorFlow](https://www.youtube.com/playlist?list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx) In this series, the TensorFlow Team looks at various parts of TensorFlow from a coding perspective, with videos for use of TensorFlow's high-level APIs, natural language processing, neural structured learning, and more.
Free [View series](https://www.youtube.com/playlist?list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx) Code Build 
Spotting and solving everyday problems with machine learning
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.
Free
Watch the video
Theory [Video](https://www.youtube.com/watch?v=_1QtMPuYIVw)
## For Javascript
Explore the latest resources at [TensorFlow.js](https://www.tensorflow.org/js).
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources)  [Machine Learning for Web Developers (Web ML)](https://youtube.com/playlist?list=PLOU2XLYxmsILr3HQpqjLAUkIPa5EaZiui) Get a practical working knowledge of using ML in the browser with JavaScript. Learn how to write custom models from a blank canvas, retrain models via transfer learning, and convert models from Python.
[View series](https://youtube.com/playlist?list=PLOU2XLYxmsILr3HQpqjLAUkIPa5EaZiui) Code Math Theory Build  [Learning TensorFlow.js](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) by Gant Laborde A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.
[View book](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) Code Theory Build  [Getting started with TensorFlow.js by TensorFlow](https://www.youtube.com/playlist?list=PLs6AluHXaQnjeI6jzDkpKXvbPj31i4GgF) A 3-part series that explores both training and executing machine learned models with TensorFlow.js, and shows you how to create a machine learning model in JavaScript that executes directly in the browser.
Free [View series](https://www.youtube.com/playlist?list=PLs6AluHXaQnjeI6jzDkpKXvbPj31i4GgF) Code Theory  [Google AI for JavaScript developers with TensorFlow.js](https://www.edx.org/course/google-ai-for-javascript-developers-with-tensorflowjs) Go from zero to hero with web ML using TensorFlow.js. Learn how to create next generation web apps that can run client side and be used on almost any device.
Free [View course](https://www.edx.org/course/google-ai-for-javascript-developers-with-tensorflowjs) Code Theory Build  [TensorFlow.js: Intelligence and Learning Series](https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YIeVA3dNxbR9PYj4wV31oQ) by The Coding Train Part of a larger series on machine learning and building neural networks, this video playlist focuses on TensorFlow.js, the core API, and how to use the JavaScript library to train and deploy ML models.
Free [View series](https://www.youtube.com/playlist?list=PLRqwX-V7Uu6YIeVA3dNxbR9PYj4wV31oQ) Code Math Theory
## For Mobile \& Edge
Explore the latest resources at [Google AI Edge](https://ai.google.dev/edge/).
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources) 
Google Developers
[On-Device Machine Learning](https://developers.google.com/learn/topics/on-device-ml#build-your-first-on-device-ml-app) Learn how to build your first on-device ML app through learning pathways that provide step-by-step guides for common use cases including audio classification, visual product search, and more.
[Learn more](https://developers.google.com/learn/topics/on-device-ml#build-your-first-on-device-ml-app) Code Build  [Introduction to LiteRT](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190) Learn how to deploy deep learning models on mobile and embedded devices with LiteRT in this course, developed by the TensorFlow team and Udacity as a practical approach to model deployment for software developers.
Free [View course](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190) Code Theory Build
## For Production
Explore the latest resources at [TFX](https://www.tensorflow.org/tfx).
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources) 
ML engineering for production ML deployments with TFX
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.
Free
Watch the video
Code Build [Video](https://www.youtube.com/watch?v=YeuvR6m6ACQ)  [Building Machine Learning Pipelines](https://www.oreilly.com/library/view/building-machine-learning/9781492053187/) by Hannes Hapke, Catherine Nelson 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.
[View book](https://www.oreilly.com/library/view/building-machine-learning/9781492053187/) Code Theory Build 
DeepLearning.AI
[Machine Learning Engineering for Production (MLOps) Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops/) Expand your production engineering capabilities in this four-course specialization. Learn how to conceptualize, build, and maintain integrated systems that continuously operate in production.
[View course](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops/) Code Theory Build  [ML Pipelines on Google Cloud](https://www.coursera.org/learn/ml-pipelines-google-cloud) This advanced course covers TFX components, pipeline orchestration and automation, and how to manage ML metadata with Google Cloud.
[View course](https://www.coursera.org/learn/ml-pipelines-google-cloud) Code Theory Build
## Human-centered AI
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.
[Back to Resource Library](https://www.tensorflow.org/resources/learn-ml#educational-resources)  [Responsible AI practices](https://www.tensorflow.org/responsible_ai) Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow.
Free [Learn more](https://www.tensorflow.org/responsible_ai)  [People + AI guidebook](https://pair.withgoogle.com/) This guidebook from Google will help you build human-centered AI products. It'll enable you to avoid common mistakes, design excellent experiences, and focus on people as you build AI-driven applications.
Free [Learn more](https://pair.withgoogle.com/)  [Intro to Fairness in Machine Learning module](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture) This one-hour module within Google's MLCC introduces learners to different types of human biases that can manifest in training data, as well as strategies for identifying, and evaluating their effects.
Free [Learn more](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)
## Join TensorFlow's global community
[Learn more](https://www.tensorflow.org/community)