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How to learn Machine Learning

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Read the best books to build Machine Learning understanding.

A well-written and thorough book can be an amazing path to build deeper understanding of Machine Learning and also act as a handbook as you discover the internet's vast resources.

These are our and our experts top picks to get started building career-relevant skills.

Introduction to Statistical learning

Introduction to Statistical learning

ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Bishop's book on pattern recognition is a classic textbook and staple in Machine Learning. Beimg aimed at grad students, but also at researchers and practitioners, it's no easy lecture, but a truly fundamental course book.

Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow

Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow

The hands-on Machine Learning book is an amazing piece by Aurélien Géron, taking you from the basics of Machine Learning to applying them to real-word scenarios all in one book.

Pragmatic Programmer

Pragmatic Programmer

At least today, code is our door to building algorithms and complex Machine Learning systems. If you want to invest in becoming a more proficient Machine Learning professional faster, investing in code skills is the way to do so.

Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction

Richard Sutton's book on Machine Learning is universally regarded as one of the most fundamental and important pieces on the matter. Reinforcement Learning is quickly becoming a major part of AI innovation, and a good read for any engineer and scientists to go through.

Generative Deep Learning

Generative Deep Learning

It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples o

Federated Learning (Synthesis Lectures)

Federated Learning (Synthesis Lectures)

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security.

Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.

Advanced Machine Learning with Python

Advanced Machine Learning with Python

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.

Probabilistic Machine Learning: Advanced Topics

Probabilistic Machine Learning: Advanced Topics

An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference.

Work with one of our 194 Machine Learning mentors available

Self-teaching is a great tool, but working with an experienced mentor can accelerate your learnings by 10x.

Our mentors are hand-picked Machine Learning experts who will help you learn Machine Learning and guide you towards your goals.


Hi there! I'm Oluwole, a Certified TensorFlow Developer and an ML Engineer, currently serving as the VP of Engineering and Environmental Data at ChangeBlock. My work lets me combine my passions for technology and environmental conservation, as we use machine learning to tackle climate change, I am excited to share …

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I've worked at Google, Facebook. and Databricks building teams and products in the AI/ML, Search and Recommendation, leading/managing anywhere from 2 to 200 person teams. At the same time, I have a very active life outside work, so I'm a strong advocate for work-life harmony. I love mentoring and guiding …

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Hi there! I am a Data Science professional with 8+ years of experience in the field. I've worked at Data consultancy firm Mu Sigma for 3 years and completed 5 years at LinkedIn. During my time at the consultancy, I've worked with fortune 100 clients on various problems in Technology …

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Chris Busch is a seasoned product and technology executive with over 20 years of experience driving digital transformation and innovation across healthcare, consumer goods, and enterprise technology. With a track record of building and scaling SaaS, AI/ML, and IoT platforms, Chris has consistently translated complex customer needs into market-leading digital …

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Lead Machine Learning Engineer @ Institut Polytechnique de Paris with the Hi! PARIS Research center https://www.hi-paris.fr/ Ex-Amazon Data Science & DHL Consulting I lead a team of 8 engineers (ml ops engineers, data scientists, data engineers) creating state-of-the-art open-source python packages. Before becoming a manager, my role involved being an …

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When I began my professional journey, I faced numerous uncertainties about the right path to take and the skills I needed to excel. Fortunately, I discovered a mentor who provided me with guidance, a broader perspective, and milestones to strive for. Inspired by this experience, I am eager to pay …

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Data scientist experienced (5+ years) in the prototyping and development of data processing and machine/deep learning algorithms with a focus on remote sensing data. Passionate about use of remote sensing data to gain a better understanding of the environment and to improve the management of natural resources and human activities. …

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Gourav is a seasoned Data Science Manager with over 10+ years of experience, distinguished for his expertise in LLM (Large Language Model), prompt engineering, NLP, and classical ML solutions. He has consistently demonstrated a knack for unraveling complex, open-ended challenges and seamlessly integrating solutions into both new and existing workflows. …

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Courses to deepen your Machine Learning skills.

These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a learning community, keeping you accountable and on the path.

Our experts recommend these courses, from free selections to paid programs.

Udacity's Machine Learning Nanodegree

Udacity's Machine Learning Nanodegree

Udacity has been a pioneer of Machine Learning courses since launching their wide range of ML, Data Science and Robotics courses a few years back. These Nanodegrees are pricey but often come with career support and human project grading.

Fast.ai

Fast.ai

With the motto "making neural nets uncool again", fast.ai is a straight-to-the-point practical (and free!) course that is valued by Machine Learning enthusiasts and engineers worldwide. Fast.ai comes with a community, many practical projects and great content.

Machine Learning A-Z™

Machine Learning A-Z™

Kirill Eremenko's course on Udemy is a classic with almost a million (!) students worldwide. A-Z takes you from a bit of coding knowledge to making your own predictions and building ML models pretty swiftly. At prices between $10 - $20 it's also cheaper than many alternatives.

Deep Learning Specialization

Deep 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. 38% of students started a new career after completing this specialization.

MIT Open: Linear Algebra

MIT Open: Linear Algebra

Math is the foundation of Machine Learning and much needed if you need to work on the inner logic of its systems. Senior engineers are encouraged to propose and submit their own papers – and getting your LinAlg back in order is a must for that.

Lex Fridman's MIT Deep Learning

Lex Fridman's MIT Deep Learning

Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.

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