One of the biggest trends in recent years has been machine learning, and you can use it to automate your programming tasks to make them more efficient or improve your website analytics with AI algorithms. But if you’re new to the field, where do you start?
This list of the best books for machine learning engineers will get you up to speed quickly and easily on everything from neural networks to optical character recognition so that you can use this powerful technology in your software development projects from day one.
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What is Machine Learning?
The simplest definition of machine learning as provided by ComputerHope is as follows: “a set of algorithms that give computers the ability to learn without being explicitly programmed”. In practice, there are two main types of ML: supervised learning and unsupervised learning.
List of Top Rated Books for Machine Learning Engineers
Machine learning is a branch of Artificial Intelligence where computers develop the ability to learn without being explicitly programmed — just like humans do.
Machine learning engineers are highly sought after these days, and with good reason. The demand for machine learning experts is only growing as businesses realize more than ever how valuable artificial intelligence (AI) can be to their bottom line. Getting started in machine learning can be tough, but there are a number of great resources out there.
One of my favorite ways to learn is through books, so here are some excellent books you might want to check out if you’re interested in pursuing a career in machine learning.
by Ian Goodfellow, Yoshua Bengio, & Aaron Courville
Best Suited For : Undergraduate and Graduate Students
Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville — This book is a great starting point and has a nice set of exercises to get you thinking about basic issues. When you’re ready to move on, there are more topics to read and learn. Understanding Deep Learning is one of them; I personally find it useful, but you may want another perspective. It also has a lot of math in it, so if that’s not your cup of tea then maybe move on quickly as well or supplement with other resources like notes from Andrew Ng course. A new addition by Jeremy Howard – Deep learning Book: Modern AI made accessible! The book can be considered as Read before going to Deep Learning School.
- Linear Algebra Concepts
- Probability Theory
- Information Theory
- Numerical Computation
- Machine Learning
- Deep Feedworded Networks
- Convolutional Networks
- Sequence Modeling
by Andreas Mueller and Sarah Guido
Best Suited for : Beginners with familiarity with Python
Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido — Building on basics with hands-on examples, Andreas Mueller and Sarah Guido’s course book, Machine Learning with Python, teaches readers to how to apply machine learning to a wide range of real-world problems. From fundamental concepts such as regression analysis and classification to advanced topics like Deep Neural Networks and Reinforcement Learning.
The simple coding examples are accompanied by detailed instructions and illustrations that will help you implement them in your own projects. This is a great starting point for anyone looking to get their first experience with ML in Python or who want to gain a more complete understanding of some of the core concepts before moving onto more advanced texts.
The book you will discover how easy it could be to create machine learning systems by yourself, and learn how best to approach it.
With the help of this book, you will be able to develop your own method of discovering how people feel on Twitter, or make predictions regarding global warming. Machine learning applications are limitless and, given the sheer amount of information accessible today, largely only limited to your own imagination.
- Fundamental concepts
- Machine learning algorithms
- Data and Machine Learning
- Model evaluation and parameter tuning
- Pipelines for chaining models
by Christopher M. Bishop
Best Suited For: Beginners
Pattern Recognition and Machine Learning by Christopher M. Bishop — Chris Bishop is an Microsoft distinguished Scientist and Lab Director of Microsoft Research Cambridge. He is a professor of Computer Science at the University of Edinburgh, and an Associate of Darwin College. He was appointed Fellow of the Royal Academy of Engineering, and he was also appointed Fellow of the Royal Society of Edinburgh.
Pattern Recognition and Machine Learning: Data Mining, Inference, and Prediction by Christopher M. Bishop, is an elementary introduction to machine learning methods and their implementation.
This book is great as an introductory text to learning machine learning techniques from a statistical perspective. The book employs graphic models to illustrate the probability distributions, and it is the best book to employ graphical models for machine learning.
- Graphical Models to Machine Learning
- Probability Distributions
- Pattern recognition
- Signal Processing
- Data Mining
by Tom M. Mitchell
Best Suited For : Graduate Students and Advanced Undergraduate Students
Machine Learning by Tom M. Mitchell — This book is suitable for everyone interested in machine learning. The book will not only familiarize you with many different algorithms, but it will also give you a solid theoretical foundation on which to build your understanding of more complex methods. It starts by introducing decision trees and neural networks, then proceeds to cover support vector machines, Bayesian techniques, hidden Markov models, and clustering techniques. The discussions are intuitive and require no mathematical background beyond calculus. This is a great book if you want a quick overview of many important machine learning topics.
The book outlines the fundamental algorithms and theories that constitute the foundation for machine-learning. It discusses the theoretical aspects such as how learning performance depend on the amount of training instances presented? And which learning algorithms are best suited for various learning tasks?
The text focuses on the subject of machine learning that involves the research of algorithmic techniques that enable computers to learn through the use of experience. This book is designed to help undergraduates take upper-level and graduate level classes in machine learning.
- Basic concepts of statistics
- Artificial Intelligence
- Information Theory
- Online Data Sets
- Cognitive Science
- Computational Complexity
- Control Theory
by Charu C. Aggarwal
Best Suited For : Graduate Students, Researchers, and Practitioners
Neural Networks and Deep Learning by Charu C. Aggarwal — Charu C. Aggarwal is a Distinguished Research staff member (DRSM) in the IBM T. J. Watson Research Center in Yorktown Heights, New York. He received his undergraduate education with a major in Computer Science from the Indian Institute of Technology in Kanpur in 1993. He then earned the Ph.D. at The Massachusetts Institute of Technology in 1996. He has many patents in his name. Furthermore, he has thrice been designated as a master inventor at IBM.
Neural Networks and Deep Learning by Charu C. Aggarwal is a textbook which provides an introduction to deep learning in a logical manner. The book uses interactive programming assignments and projects with solutions to help teach concepts that are typically hard to grasp.
The book uses Python as its programming language, but does use other libraries like NumPy, SciPy, and Matplotlib, along with open-source frameworks such as TensorFlow. The book covers neural networks at multiple levels of abstraction before explaining deep learning architectures including Restricted Boltzmann Machines (RBM), Autoencoders, Convolutional Neural Networks (CNN), LSTM Recurrent Neural Networks (RNN), Deep Belief Nets (DBN) etc.
- Machine Learning and Neural Networks
- Support Vector Machines
- Linear/logistic regression
- Singular Value decomposition
- Matrix Factorization
- Neural Turing machines
- Adversarial Networks
- Kohonen self-organizing maps
by Aurélien Géron
Best Suited For : Beginners and Graduates
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron — Aurelien Geron works as a machine-learning instructor and consultant. An ex-Googler, part of YouTube’s Video Classification team between 2013 until 2016. He was also the founder as well as CTO at Wifirst, Kiwisoft and Polyconseil.
Learning to use machine learning models can be a daunting task. If you’re not already familiar with a particular model or technique, it’s hard to know where to start. This practical guide introduces you to multiple real-world problem domains and shows how powerful machine learning can be when applied correctly.
Each chapter features relevant background information followed by step-by-step instructions on building practical systems using tools such as scikit-learn, Keras, and TensorFlow.
As you work through each hands-on example, you’ll learn both general principles and concrete skills that will help you apply machine learning in your own projects. Topics include deep neural networks, natural language processing, image recognition, clustering data using KMeans clustering algorithm etc.
Prerequisites: The book assumes you have at least some Python programming experience and you have a some understanding of Python’s major scientific libraries – specifically, NumPy, pandas, and Matplotlib.
- Tensor Flow
- Simple Linear Regression
- Deep Neural Networks
- Support vector machines
- Decision Trees
- Random Forests
- Neural Net Architectures
by Oliver Theobald
Best Suited For : Absolute Beginners
Machine Learning for Absolute Beginners by Oliver Theobald — When it comes to machine learning, there’s a lot to know. And that can be pretty intimidating—especially if you’re brand new to data science. Luckily, there are a ton of great books that offer an introduction to concepts like supervised and unsupervised learning, regression analysis, decision trees, neural networks and clustering algorithms.
This book by Oliver Theobald is a great starting point for beginners. It walks you through basic concepts in data science before moving on to machine learning, with clear explanations and helpful illustrations in each chapter.
- Machine Learning Libraries
- Data Scrubbing Techniques
- k-fold Validation
- k-means Clustering
- Regression Analysis
- Decision Trees
- Building Machine Learning Models
by Andriy Burkov
Best Suited For : Absolute Beginners
The Hundred-Page Machine Learning Book by Andriy Burkov — Andriy Burkov is a machine learning expert based out of Quebec City, Canada. He has a Ph.D. in Artificial Intelligence. He specializes in natural language processing. Furthermore, he leads a team of machine learning developers at Gartner.
In The Hundred-Page Machine Learning Book, Andriy Burkov provides a comprehensive and self-contained machine learning resource, covering everything from basic concepts to advanced techniques.
This book is an absolute must-read for any practitioner who wants to get up to speed quickly on the field of machine learning. For anyone with a working knowledge of Python and some statistical background, you’ll find no shortage of valuable information here.
The text includes everything from linear and logistic regression to neural networks and support vector machines. Each concept is clearly explained without mathematical fluff or extraneous detail—making it one of our favorite introductory texts on machine learning available today.
- Machine Learning Concepts
- ML Library
- Linear Regression
- Logistic Regression
- Neural Networks
by Sebastian Raschka, Vahid Mirjalili
Best Suited For : Graduate and Advanced Users
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka & Vahid Mirjalili — This is again a must-have book for programmers trying to get started with machine learning. You will learn how to implement various algorithms from scratch in Python programming language, which makes it easier to understand the concepts and their application too. All the different data mining tasks are discussed here, so you can refer to them while implementing a machine learning algorithm.
- Machine Learning Algorithms
- How to use scikit-learn
- Data Processing
- Dimensionality Reduction
- Model Evaluation
- Hyperparameter Tuning
- Ensemble Learning
- Regression Analysis
- Clustering Analysis
- Multilayer Artificial Neural Network
by Valliappa Lakshmanan, Sara Robinson & Michael Munn
Best Suited For : Advanced Users, Data Scientists, ML Engineers
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson & Michael Munn — This book is about building AI-based applications and businesses. It covers a wide range of machine learning topics and clearly lays out design patterns that engineers need to deal with common problems in data preparation, model building, and MLOps (monitoring/operations).
For example, it talks about what kinds of monitoring are necessary at different stages of an application’s life cycle, along with guidelines for setting up monitoring in each case. As a bonus, it also describes how tools such as Amazon Web Services (AWS) and Google Cloud Platform can be used to scale up production applications.
- BigQuery ML
- Cloud AI Platform
- Explainable AI
- Keyed Predictions
- Neural Networks
by Kevin P. Murphy
Best Suited For : Undergraduate and Advanced Graduate Students
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy — This book by Kevin Murphy is considered one of the best books for ML engineers. In his review, Dave Williams writes that it has everything you need to know to understand and use most supervised machine learning algorithms to practice. If you only read one book on machine learning, make it this one! This book covers things like dimensionality reduction and kernel methods.
This book provides a detailed introduction to machine learning. The book covers examples drawn from application domains such as text processing, molecular biology, computer vIsion, and robotics.
The methods described in this book have been implemented in a MATLAB software package named probabilistic modeling toolkit (PMTK).
- Linear regression
- Logistic regression
- Bayes’ rule
- The Pisson distribution
- Bayesian concept learning
- Gaussian Models
- Bayesian statistics
- Markov models
- Monte Carlo inference
by Laurence Moroney
AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney – This book is all about how machine learning works, why we use it and how to build it. AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney takes you on a journey through deep learning algorithms and their implementation in TensorFlow and Theano. You will also be taught strategies to build your own machine learning applications using both Python and Java.
The goal of this book is to prepare you as a developer on AI and ML technologies.
- Hyperparameter tuning
- relu – rectified linear unit
- Neural network
- Convolutional Neural Networks
by Marc Peter Deisenroth
Best Suited For : Beginners to Advanced Users
Mathematics for Machine Learning by Marc Peter Deisenroth — This book presents techniques needed to put data to work. This book approaches mathematics from a problem-solving perspective rather than an abstract one, which means that it is easier to grasp and understand.
Deisenroth begins with linear algebra and then continues with concepts such as logic, probability, statistical methods and measure theory.
After reading each chapter, readers are encouraged to solve examples that reinforce what they have learned in order to cement their understanding of a concept before moving on. This textbook is best suited for people who have had at least some experience with statistics or other mathematical subjects, but want a review of those topics while learning how they are applied in machine learning settings.
- Linear algebra
- Analytic Geometry
- Matrix decompositions
- Vector calculus
- Probability & Statistics
- Linear Regression
- Principal component analysis
- Gaussian mixture models
14. Machine Learning For Dummies
by John Paul Mueller
Best Suited For: Undergraduate and Advanced Graduate Students
Machine Learning For Dummies by John Paul Mueller — While there are many books that take a focused look at machine learning, John Paul Mueller’s book offers an overview of its concepts in a friendly and entertaining way. If you’re new to data science but still looking to dip your toes into it, or if you’re already immersed in coding but want to get up to speed on machine learning techniques, then you should pick up a copy of Machine Learning For Dummies.
The book is full of practical examples and gives detailed explanations without being overly technical. It’s also particularly valuable because it doesn’t neglect more conceptual ideas about how algorithms work – instead, it shows how they can be applied.
- AI and Machine Learning
- Usage of datasets
- Application of machine learning
by Shai Shalev-Shwartz, Shai Ben-David
Best Suited For : Beginners and Advanced Graduate Students
Understanding Machine Learning: From Theory to Algorithms
by Shai Shalev-Shwartz, Shai Ben-David — Understanding Machine Learning explores what machine learning is, what it can and cannot do, how it works and where it came from. Readers are introduced to basic concepts in probability and statistics as well as advanced topics such as neural networks.
The book describes algorithms that make use of various structures from linear regression models to deep convolutional neural networks (CNNs). You will learn about approaches such as decision trees, support vector machines (SVMs), k-nearest neighbors (KNNs) and ensemble methods.
The authors also describe distributed computing frameworks including MapReduce and cloud data services such as Amazon Web Services (AWS) Elastic MapReduce. Understanding Machine Learning discusses key implementation issues like cost function optimization, parameter tuning and hyperparameter optimization.
- Machine Learning
- Valiant’s Probably Approximately Correct (PAC) learning model
- Empirical Risk Minimization (ERM)
- Structural Risk Minimization (SRM)
- Minimum Description Length (MDL)
- Stochastic gradient descent
- Neural networks
- Structured output learning
- Emerging theoretical concepts
- PAC-Bayes approach
by Stefan Jansen
Best Suited For : Intermediate and Advanced Users
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python by Stefan Jansen — If you’re looking to develop algorithmic trading strategies with Python, and you want to know how deep learning can be applied in financial markets, then Machine Learning for Algorithmic Trading is a must-read.
In Machine Learning for Algorithmic Trading, author Stefan Jansen provides data scientists and machine learning engineers with a comprehensive guide to extracting signals from market and alternative data sources. If you’re looking to gain insight into current ML techniques used in finance, or implement your own trading strategy using Python code without taking out a bank loan just yet, then look no further.
- Automated trading strategies
- pandas, TA-Lib
- TensorFlow 2
- Machine learning algorithms
- Deep learning
by Emmanuel Ameisen
Best Suited For : Beginners and Advanced Users
Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen — Emmanuel Ameisen currently serves as Engineering Director at Facebook and previously served as Engineering Manager at Google. He is a Ph.D. in Computer Science from Ecole Normale Supérieure de Lyon and holds an Habilitation à Diriger des Recherches degree (HDR) from Université Paris-Sud XI and Université Paris Diderot Paris 7 (France).
His research interests are machine learning algorithms and parallelization strategies, signal processing systems design and automatic target recognition; he has published numerous scientific papers in these fields.
The author provides an in-depth look at all major steps of developing a machine learning powered application, from building models to deploying them on production platforms. Full of practical examples and helpful diagrams, it helps you turn your knowledge into real world applications.
- Identifying the right ML approach
- Building an initial prototype
- Iterating on models
- Deployment and monitoring
Machine Learning Engineer: What Does a Machine Learning Engineer Do?
As the ‘sexiest job of the 21st century’ and all its other accolades suggest, machine learning positions are highly coveted and hence difficult to get your hands on. The good news is that even though competition is stiff, you don’t necessarily need to be a machine learning expert with a Ph.D. in the field to land such roles.
What is a Machine Learning Engineer?
If you happen to love coding and solving problems related to machine learning, then a machine learning engineer position could be the choice for you. Recruiters typically look for people who have good knowledge of both programming languages and various algorithms as well as experience with training models from scratch. Among the most popular programming languages are Python, Java and Scala.
How does Machine Learning work?
Machine Learning is a branch of artificial intelligence wherein computers are able to learn and solve problems on their own. It does so by analyzing large amounts of data, processing it and making predictions or classifications about it. Machine Learning has many practical applications in modern society, including spam filtering, self-driving cars and facial recognition software. One downside of Machine Learning is that these processes can often be slow and require extensive data, both of which increase costs.
How does Machine Learning affect businesses?
Machine Learning is disrupting industries in new and exciting ways. Online retailers use machine learning to better predict consumer behavior and anticipate their needs. Insurance companies are using it to assess risk, which improves policy pricing and underwriting. You can even pay with your face or fingerprint instead of a credit card at some stores! These are just a few examples of how ML has changed business as we know it.
What’s the difference between machine learning and artificial intelligence?
Machine learning is a subset of artificial intelligence. But, like technology, AI has grown far beyond its humble origins. Artificial intelligence today could mean anything from programs that analyze large sets of data to build recommendation engines that know you better than you know yourself, to computer systems that can beat humans at Go and are capable of driving cars. Machine learning is part of it all—but so much more as well.
Is there a good introductory book on machine learning?
There are several good introductory books on machine learning: Elements of Statistical Learning by Hastie, Tibshirani and Friedman and An Introduction to Statistical Learning by James, Witten and Hastie.
Which programming language should I learn first?
This is a question asked by many people interested in learning machine learning, since many of today’s most popular ML packages are written in Python. However, if you already know C++ or Java, then these languages might be a better choice for you since they’re very similar to R and Matlab respectively. If you want to learn Python from scratch though, I recommend starting with Learn Python The Hard Way by Zed Shaw or Data Science from Scratch: First Principles with Python by Jose Portilla.
Where can I get data for Machine Learning?
In order to create meaningful machine learning models, you need to access data. Sometimes that means you will have to build a dataset from scratch by collecting it yourself, but if you don’t have that option or simply don’t want to spend time collecting a new dataset, then you can purchase datasets from third-party vendors. The drawback of using a third-party dataset is that, sometimes, there isn’t enough information about how your company would use it.
What tools do you need for Machine Learning?
You’ll need programming and statistics skills. Most coding languages, including Python, R, C++, C# and Java have some form of machine learning library you can use to apply machine learning to your problem. If you don’t know how to code (or are a beginner), here are some Python libraries that make it easy for beginners: Weka, Orange and EASI. The MLlib in Apache Spark is also good for beginners with Spark.
How do you get started with Machine Learning?
Getting started with Machine Learning isn’t an easy thing to do. There are several frameworks and packages out there that can be confusing if you’re new to it.
What type of jobs are available in Machine Learning?
There are currently a number of Machine Learning jobs available on big companies such as Google, Facebook, Microsoft, etc. and they pay very well (in average). Moreover these jobs seem to always be available as you can check on their official websites that you can find below. However in my opinion, it is better to focus more on smaller companies because in these places people are more friendly and will help you out if you want to learn or grow with them.
How much money should I make when I start learning Machine Learning?
In general, Machine Learning engineers make between $100k and $150k in their first year on average. Obviously, where you live can have a big impact on your salary, with locations like New York City (NYC) and San Francisco Bay Area (SFBA) having some of highest salaries for ML engineers.
Can I use Machine Learning at my current job?
If you’re employed by a large company, there is likely a data science team somewhere in your organization. This can be a good way to get an introduction to machine learning. Depending on your position within that team, you may have access to some of their tools and resources (which can make it easier for you to learn). However, before you ask if you can use their machine learning resources, understand that ML is so hot right now that companies are heavily investing in hiring and training employees just for this expertise.
Machine Learning Engineer Job Description
Machine learning engineers have a slightly different job description. Engineers work on existing software that processes data and creates models. Data scientists mainly engage in machine learning and other distributed computing tasks such as finding similarities between large sets of data. Machine learning engineers, on the other hand, use software tools to integrate machine learning into existing applications.
As part of the company’s data science team, machine learning engineers are involved in all stages of data analysis. They may do some simple tasks like downloading and processing raw data for further study or troubleshooting problems with the deployment of machine learning models.
Machine Learning Engineer Salary and Career Requirements
Machine learning engineers require a wide range of skills, which is why it’s hard to provide a general idea of the salary they expect to receive. Among other things, their level of experience plays an important role in defining their pay; entry-level positions command an average salary of $100,000.
To qualify as a machine learning engineer, you will need at least a bachelor’s degree in computer science or mathematics. You should also be proficient with Java and C++ programming languages. As far as software engineering is concerned, Python and Scala are valued more than R Language (the latter is more of a data science language).
Machine Learning Engineer Education and Training
Employers are looking for professionals with a strong background in mathematics, statistics and computer programming. Expect the interview process to be challenging if you lack real world experience. It is common for companies to conduct interviews using practical tests that require candidates to solve problems using machine learning algorithms. As all successful machine learning engineers have a solid foundation in data science, print sources are not enough to prepare for the job.
Machine Learning Engineer Careers and Job Outlook
With an increased interest in machine learning among tech giants, demand for machine learning engineers is expected to rise over the next years. As more companies become involved with data science it will continue to become a more popular field of work.
The Machine Learning Engineer job market is growing much faster than many other occupations. While the American economy in total will grow 13 percent from 2016 to 2026, jobs for machine learning engineers are expected to increase 31 percent during the same time period, according to the US Bureau of Labor Statistics.
Free Resources to learn Machine Learning
There are plenty of resources to help you get started with Machine Learning. You can learn Machine Learning from basic to advanced topics by taking online courses, reading books and articles, or by attending meetups and hackathons. One of my favorite machine learning platforms is Kaggle. On Kaggle, you’ll be able to find data sets, participate in competitions and master new algorithms for free. Plus, if you have a specific problem that needs solving — such as improving ad clickthrough rates — a Kaggle competition could be your answer!
You can check these free YouTube resources on Machine Learning:-
All those terms such as artificial intelligence (AI), machine learning (ML) and deep learning (DL) seem to be everywhere now – not just in the media but also in the job market. If you are a recent graduate or even planning your next career move, you might naturally wonder whether AI-related roles could make for a suitable choice and what exactly do they entail.
You can visit GeeksforGeeks to learn more about Machine Learning.
If you want to learn more about machine learning and how to use it in your own projects, these books are a great place to start!
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