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