Becoming a Machine Learning Engineer is an in-demand career
path. It
requires deep expertise in Machine Learning and a
strong network to carry you along. Here are some resources to help you on your journey.
Why should you become a Machine Learning Engineer?
Demand for experts in Machine Learning is growing rapidly. Companies are
looking for people with deep expertise in the field of Machine Learning to help
them
build their products and services.
As a result, Machine Learning Engineers are in high demand and command high salaries. According to leading
sources,
the median salary for a Machine Learning Engineer is $150,000 and a
senior Machine Learning Engineer can earn up to $200,000. Even entry-level
positions can command great salaries.
No wonder that interest in a career in Machine Learning is growing rapidly.
Explore the
resources below to learn more about how to become a Machine Learning Engineer.
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.
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.
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.
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
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.
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.
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.
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.
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.
Get the guidance you need to become a Machine Learning Engineer
There is no better source of accountability and motivation than having a
personal mentor.
What used to be impossible to find is now just two clicks away! All mentors are vetted & hands-on!
Lead Machine Learning Engineer
at Institut Polytechnique de Paris
Lead ML Research Engineer @ Institut Polytechnique de Paris / Ex-Amazon
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Lead Machine Learning Engineer @ Institut Polytechnique de Paris with the Hi! PARIS Research center https://www.hi-paris.fr/ Professor of Data Science @ NYU 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 …
Machine learning engineer
at Raft
5 years of experience in Machine learning and Data science
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Jatin Ahuja is working as Machine Learning Engineer at Raft to solve real world problems using machine learning. Former to this, he was part of RedHat as a Data Engineer working with Finance data pipeline and automation tasks. He has also worked with a couple of AI based startups to …
PhD Machine Learning Engineer
PhD/Postdoc Machine Learning Engineer and Former UC Davis ML Course Instructor
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Hi! My name is Robin. I have PhD and MS degrees in Computer Science and Electrical Engineering from UC Davis, where I was employed as the full instructor for the undergraduate Machine Learning Course. Afterwards, I completed a Machine Learning PostDoc at LLNL working on NIF during the recent world …
Current Machine Learning Engineer at
at Notsohuman.ai, StabilityAI,
Our top 1.3% mentor with 5+ years of mentoring/transforming at Mentorcruise. Top Mentor.
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Hey there, if you're looking for a mentor with a unique blend of humor and professionalism, then look no further than Nilesh Jain! To know more about me and the mentorship, play the intro video and/or book a session. With over 5 years of experience as a mentor on the …
Senior Machine Learning Engineer
at Planet Labs
Experienced technical startup founder, published O'Reilly author, astrophysics PhD
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Scientist, entrepreneur, writer, and technologist. I am currently a Senior Machine Learning Engineer at Planet Labs PBC, where I am part of the Applied Machine Learning team. I also have a PhD in computational astrophysics, and a BSc in applied physics & applied mathematics. After my PhD, I cofounded an …
Data Scientist || Machine Learning Engineer
at Google
5+ years of experience in Data Science and Machine Learning
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Amin is a machine learning engineer / full stack data scientist, currently working as a Machine Learning Engineer at Google. Amin's method of mentorship is to empower the mentee. Helping mentees with totally different backgrounds (electrical engineering, biology, civil engineering), Amin understands the struggles many students have in fitting into …
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 into.
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.
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.
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!
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!
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!
Still not
convinced? Don’t just take our word for it
We’ve already delivered
1-on-1 mentorship to thousands of students, professionals, managers and executives. Even
better, they’ve left an average rating of 4.9 out of 5 for our mentors.
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