Machine Learning Engineer Career Path & Resources
Machine Learning is a top-growing skill. Good engineers are in-demand in a wide variety of industries and as a fast moving field, recent experience is valued and needed.
Why should you become a
Machine Learning Engineer?
Machine Learning Engineers combine the skills of AI Researchers with the ones of Software Engineers. The result of that is a data-inspired engineering role that's crucial to businesses today.
Even better – Machine Learning is a fast-moving field. Newcomers can pick up the basics quickly and position themselves with cutting-edge knowledge.
Machine Learning is set for another high-growth year. It's not too late to get into it!
Best books to build Machine Learning understanding.
A well-written and thorough book can be an amazing path to build deeper understanding 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
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
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
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.
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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 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.
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.
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The Machine Learning must-reads you shouldn't miss.
Key articles and posts of industry experts can help you get a better picture of what you are getting
In our opinion, these are some must-reads you really shouldn't miss.
Karpathy on "Software 2.0"
Andrej Karpathy is the Director of AI at Tesla. Before that, though, he authored this blog post in 2017 talking about Deep Learning as "Software 2.0" of some sort. A must-read if you ever want to have another way of thinking about ML.
Simple Reinforcement Learning with Tensorflow
This 8-part series by Arthur Juliani (Deep RL researcher at Unity) is an amazing entry point to the new and mysterious advancements of Reinforcement Learning, perfectly suited for folks coming from other topics in Machine Learning.
Building Safe A.I. (Trask)
Andrew Trask is a specialist in Federated Learning and Safe AI. In this blogpost, he writes about training a neural network that is fully encrypted during training (trained on unencrypted data).
Opportunities and projects in the Machine Learning space.
In the end, advancing your career is all about getting the right opportunities at the right time and a
good portion of luck.
These are some interesting things going on in the Machine Learning space and you
probably don't want to miss them.
Specialize with Kaggle
It wouldn't be the first time I've seen someone get hired over good Kaggle results! Kaggle competitions are data science and ML projects that are graded through a public leaderboard. A good place on the leaderboard shows that you know your craft and can apply your knowledge to real-life problems!
Find early stage positions
While Google & co. are saturated with academic talent, there are incredible opportunities to get your foot in the door quicker with an early-stage positions.
Today, startups and early-stage businesses are looking for ML engineers more and more for a wider variety of jobs than what's possible in the more established industry.
Platforms like AngelList can help you find those positions!
Get into open-source
The world thrives on open-source software and this is no exception. Core contributors to core libraries and fast-growing tech like React, scikit-learn, Bitcoin and TensorFlow prove their abilities by going into the inner workings of a framework to improve it. For many companies, that's a desirable skill!
These projects are always looking for fresh faces. Grab an issue from the issue board or review a PR to get started!