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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 Aurรฉlien Gรฉron, taking you from the basics of Machine Learning to applying them to real-word scenarios all in one book.

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.

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

Itโ€™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

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.

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.

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.

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Having many varied roles on my own path to product management (engineering, sales, marketing, co-founder) I was lucky enough to have great mentors and coaches that guided me along my journey. From the initial CTOs/GMs at Intel that advised me to understand marketing and sales (beyond engineering), to really learning โ€ฆ

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ABOUT ME I currently work on cyber threat intelligence applications of ML/AI - my team delivers real ML/AI/DS work spanning both research and specific applications. In my spare time I advise companies on DS/ML, strategy, and hiring. I am a believer that everyone needs a mentor, regardless of their current โ€ฆ

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I manage a small data science team for a Series B startup (<30 people) and can help you learn about data science products, what makes good analysis/analytics great, applying machine learning beyond pure model building, managing realtime models, moving to and thriving in a startup, and lastly how to get โ€ฆ

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โš ๏ธ Permanent resident of Canada ๐Ÿ‡จ๐Ÿ‡ฆ from Japan ๐Ÿ‡ฏ๐Ÿ‡ต, currently volunteering in Malawi ๐Ÿ‡ฒ๐Ÿ‡ผ. Apologies in advance for any inconvenience we might face during the communications due to bad internet connectivity. I am a freelance software developer, previously working at a Big Tech & Silicon Valley-based start-up company while wearing โ€ฆ

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Expertise in enabling, developing and deploying robust end-to-end data pipelines and machine learning models that have real world impact on a regular basis. Over the years, I have had the opportunity to work with and learn from some of the best minds at prestigious organizations like Mercedes-Benz and General Motors. โ€ฆ

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**Why can I help you? ** I had been a software engineer at Microsoft for 6+ years, a research intern at NASA, and completed my PhD in Computer Science at Virginia Tech in 2023. I am currently a clinical assistant professor at Questrom School of Business at Boston University, where โ€ฆ

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Govind has been working in data science consulting industry for 8+ years and has solved industry problems through building scalable machine learning solutions across multiple domains :- Insurance, banking, telecom and healthcare. He has helped mentees landing their first data science jobs or transition into new roles.

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Key points - Mastering Data Science, Bioinformatics, Machine Learning, and Statistics with my expert guidance. - Balancing academia and real-world expertise, I bring a strong technical background to the mentorship. - Navigate the field with confidence as I guide you from the ground up, tailored for beginners. - Transition seamlessly โ€ฆ

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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-Zโ„ข

Machine Learning A-Zโ„ข

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 โ€“ 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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