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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hands-on machine learning with scikit-learn, keras & tensorflow - Books for Machine Learning engineers
introduction to machine learning with python - books for machine learning engineers
neural networks and deep learning - top rated books for machine learning
Books for Machine Learning engineers

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.

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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.

1. Deep Learning

by Ian Goodfellow, Yoshua Bengio, & Aaron Courville

Best Suited For : Undergraduate and Graduate Students

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Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

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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.

Topics Covered:

  • Linear Algebra Concepts
  • Probability Theory
  • Information Theory
  • Numerical Computation
  • Machine Learning
  • Deep Feedworded Networks
  • Convolutional Networks
  • Sequence Modeling

Chapter 1 begins with Introduction
Chapter 2 is on Linear Algebra
Chapter 3 is on Probability and Information Theory
Chapter 4 is on Numerical Computation
Chapter 5 covers Machine learning Basics
Chapter 6 is on Deep Feedforward Networks
Chapter 7 is on Regularization for Deep Learning
Chapter 8 is on Optimization of Training Deep Models
Chapter 9 is on Convolutional Networks
Chapter 10 is on Sequence Modeling
Chapter 11 covers Practical Methodology
Chapter 12 is about Applications
Chapter 13 is on Linear Factor Models
Chapter 14 is on Autoencoders
Chapter 15 is about Representation Learning
Chapter 16 is on Structured Probabilistic Models for Deep Learning
Chapter 18 is on Confronting the Partition Function
Chapter 19 is on Approximate Inference
Chapter 20 is on Deep Generative Models

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2. Introduction to Machine Learning with Python

by Andreas Mueller and Sarah Guido

Best Suited for : Beginners with familiarity with Python

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Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido

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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.

Topics Covered:

  • Fundamental concepts
  • Machine learning algorithms
  • Data and Machine Learning
  • Model evaluation and parameter tuning
  • Pipelines for chaining models

Chapter 1 covers fundamental concepts of machine learning
Chapter 2 and 3 is on machine learning algorithms
Chapter 4 covers different aspects of data
Chapter 5 is on model evaluation and parameter tuning
Chapter 6 covers concepts of pipelines
Chapter 7 covers different applications of data
Chapter 8 covers advanced topics

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3. Pattern Recognition and Machine Learning

by Christopher M. Bishop

Best Suited For: Beginners

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Pattern Recognition and Machine Learning by Christopher M. Bishop

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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.

Topics Covered:

  • Graphical Models to Machine Learning
  • Probability Distributions
  • Pattern recognition
  • Signal Processing
  • Data Mining
  • Bioinformatics

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4. Machine Learning

by Tom M. Mitchell

Best Suited For : Graduate Students and Advanced Undergraduate Students

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Machine Learning by Tom M. Mitchell

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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.

Topics Covered:

  • Basic concepts of statistics
  • Artificial Intelligence
  • Information Theory
  • Online Data Sets
  • Cognitive Science
  • Computational Complexity
  • Control Theory

Chapter 1 starts with Introduction
Chapter 2 is on Concept Learning and the General-to-Specific Ordering
Chapter 3 is on Decision Tree Learning
Chapter 4 is on Artificial Neural Networks
Chapter 5 is on Evaluating Hypothesis
Chapter 6 is on Bayesian Learning
Chapter 7 is on Computational Learning Theory
Chapter 8 is on Instance-Based Learning
Chapter 9 is on Genetic Algorithms
Chapter 10 is on Learning Sets of Rules
Chapter 11 is on Analytical Learning
Chapter 12 covers Combining Inductive and Analytical Learning
Chapter 13 is on Reinforcement Learning

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5. Neural Networks and Deep Learning

by Charu C. Aggarwal

Best Suited For : Graduate Students, Researchers, and Practitioners

Neural Networks and Deep Learning by Charu C. Aggarwal

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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.

Topics Covered:

  • Machine Learning and Neural Networks
  • Support Vector Machines
  • Linear/logistic regression
  • Singular Value decomposition
  • Matrix Factorization
  • Word2ved
  • Neural Turing machines
  • Adversarial Networks
  • Kohonen self-organizing maps

Chapter 1 starts with Introduction
Chapter 2 is on Machine Learning with Shallow Neural Networks
Chapter 3 is on Training Deep Neural Networks
Chapter 4 is on Teaching Deep Learners to Generalize
Chapter 5 is on Radial Basis Function Networks
Chapter 6 is on Restricted Boltzmann Machines
Chapter 7 is on Recurrent Neural Networks
Chapter 8 is on Convolutional Neural Networks
Chapter 9 is on Deep Reinforcement Learning
Chapter 10 covers Advanced Topics in Deep Learning

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6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

by Aurélien Géron

Best Suited For : Beginners and Graduates

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

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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.

Topics Covered:

  • Scikit-Learn
  • Tensor Flow
  • Keras
  • Simple Linear Regression
  • Deep Neural Networks
  • Support vector machines
  • Decision Trees
  • Random Forests
  • Neural Net Architectures

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7. Machine Learning for Absolute Beginners

by Oliver Theobald

Best Suited For : Absolute Beginners

41rrTir 2qL. SX333 BO1,204,203,200
Machine Learning for Absolute Beginners by Oliver Theobald

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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.

Topics Covered:

  • Machine Learning Libraries
  • Data Scrubbing Techniques
  • k-fold Validation
  • k-means Clustering
  • Regression Analysis
  • Decision Trees
  • Building Machine Learning Models

What is machine learning?
Machine learning categories
The machine learning toolbox
Data scrubbing
Setting up your data
Linear regression
Logistic regression
K-nearest neighbors
K-means clustering
Bias & variance
Support vector machines
Artificial neural networks
Decision trees
Ensemble modeling
Development environment
Building a model in python
Model optimization
Bug bounty
Appendix: Introduction to Python

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8. The Hundred-Page Machine Learning Book

by Andriy Burkov

Best Suited For : Absolute Beginners

41G61B4ygXL. SX383 BO1,204,203,200
The Hundred-Page Machine Learning Book by Andriy Burkov

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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.

Topics Covered:

  • Machine Learning Concepts
  • ML Library
  • Statistics
  • Linear Regression
  • Logistic Regression
  • Neural Networks

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10. Python Machine Learning

by Sebastian Raschka, Vahid Mirjalili

Best Suited For : Graduate and Advanced Users

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Python Machine Learning by Sebastian Raschka & Vahid Mirjalili

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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.

Topics Covered:

  • Machine Learning Algorithms
  • How to use scikit-learn
  • Data Processing
  • Dimensionality Reduction
  • Model Evaluation
  • Hyperparameter Tuning
  • Ensemble Learning
  • Regression Analysis
  • Clustering Analysis
  • TensorFlow
  • Multilayer Artificial Neural Network

Chapter 1 starts with Giving Computers the Ability to Learn from Data
Chapter 2 is on Training Simple Machine Learning Algorithms for Classification
Chapter 3 is a Tour of Machine Learning Classifiers Using scikit-learn
Chapter 4 is on Building Good Training Datasets – Data Reprocessing
Chapter 5 is on Compressing Data via Dimensionality Reduction
Chapter 6 is on Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Chapter 7 is on Combining Different Models for Ensemble Learning
Chapter 8 is on Applying Machine learning to Sentiment Analysis
Chapter 9 is on Embedding a Machine Learning Model into a Web Application
Chapter 10 is on Predicting Continuous Target Variables with Regression Analysis
Chapter 11 is on Working with Unlabeled Data – Clustering Analysis
Chapter 12 is on Implementing a Multilayer Artificial Neural Network from Scratch
Chapter 13 is on Parallelizing Neural Network Training with TensorFlow
Chapter 14 is on Going Deeper – The Mechanics of TensorFlow
Chapter 15 is on Classifying Images with Deep Convolutional Neural Networks
Chapter 16 is on Modeling Sequential Data Using Recurrent Neural Networks
Chapter 17 is on Generative Adversarial Networks for Synthesizing New Data
Chapter 18 is on Reinforcement Learning for Decision-Making in Complex Environments

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10. Machine Learning Design Patterns

by Valliappa Lakshmanan, Sara Robinson & Michael Munn

Best Suited For : Advanced Users, Data Scientists, ML Engineers

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Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson & Michael Munn

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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.

Machine Learning Design Patterns - machine learning books
Machine Learning Chart (Source: Machine Learning Design Patterns)

Topics Covered:

  • TensorFlow
  • Keras
  • BigQuery ML
  • TPU
  • Cloud AI Platform
  • Explainable AI
  • Keyed Predictions
  • Neural Networks
  • scikit-learn
  • XGBoost
  • PyTorch

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Machine Learning Design Patterns - job roles
(Source: Machine Learning Design Patterns)

11. Machine Learning: A Probabilistic Perspective

by Kevin P. Murphy

Best Suited For : Undergraduate and Advanced Graduate Students

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Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

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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).

Topics Covered:

  • Linear regression
  • Logistic regression
  • Bayes’ rule
  • The Pisson distribution
  • Bayesian concept learning
  • Gaussian Models
  • Bayesian statistics
  • Markov models
  • Monte Carlo inference

Chapter 1 starts with Introduction
Chapter 2 is on Probability
Chapter 3 is on Generative models for discrete data
Chapter 4 is on Gaussian models
Chapter 5 is on Bayesian statistics
Chapter 6 covers Frequentist statistics
Chapter 7 covers Linear regression
Chapter 8 is on Logistic regression
Chapter 9 is on Generalized linear models and the exponential family
Chapter 10 is on Directed graphical models (Bayes nets)
Chapter 11 is on Mixture models and the EM algorithm
Chapter 12 is on Latent linear models
Chapter 13 is on Sparse linear models
Chapter 14 is on Kernels
Chapter 15 is on Gaussian processes
Chapter 16 is on Adaptive basis function models
Chapter 17 is on Markov and hidden Markov models
Chapter 18 is on State space models
Chapter 19 is on Undirected graphical models (Markov random fields)
Chapter 20 is on Exact inference for graphical models
Chapter 21 is on Variational inference
Chapter 22 is on More variational inference
Chapter 23 is on Monte Carlo inference
Chapter 24 is on Markov chain Monte Carlo inference
Chapter 25 covers Clustering
Chapter 26 is on Graphical model structure learning
Chapter 27 is on Latent variable models of discrete data
Chapter 28 covers Deep Learning

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12. AI and Machine Learning for Coders

by Laurence Moroney

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AI and Machine Learning for Coders by Laurence Moroney

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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.

Topics Covered:

  • Hyperparameter tuning
  • TensorFlow
  • relu – rectified linear unit
  • Neural network
  • Keras
  • Convolutional Neural Networks

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13. Mathematics for Machine Learning

by Marc Peter Deisenroth

Best Suited For : Beginners to Advanced Users

51hb+vJN CL. SX348 BO1,204,203,200
Mathematics for Machine Learning by Marc Peter Deisenroth

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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.

Topics Covered:

  • Linear algebra
  • Analytic Geometry
  • Matrix decompositions
  • Vector calculus
  • Probability & Statistics
  • Linear Regression
  • Principal component analysis
  • Gaussian mixture models

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14. Machine Learning For Dummies

by John Paul Mueller

Best Suited For: Undergraduate and Advanced Graduate Students

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Machine Learning For Dummies by John Paul Mueller

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.

Topics Covered:

  • AI and Machine Learning
  • Python
  • TensorFlow
  • Usage of datasets
  • Application of machine learning

Part I: Introducing How Machines Learn
Chapter 1 covers Getting the Real Story about AI
Chapter 2 is on Learning in the Age of Big Data
Chapter 3 is on Having a Glance at the Future

Part II: Preparing Your Learning Tools
Chapter 4 is on Installing a Python Distribution
Chapter 5 is on Beyond Basic Coding in Python
Chapter 6 is on Working with Google Colab

Part III: Getting Started with the Math Basics
Chapter 7 is on Demystifying the Math Behind Machine Learning
Chapter 8 is on Descending the Gradient
Chapter 9 is on Validating Machine Learning
Chapter 10 is on Starting with Simple Learners

Part IV: Learning from Smart and Big Data
Chapter 11 is on Preprocessing Data
Chapter 12 is on Leveraging similarity
Chapter 13 is on Working with Linear Models the Easy Way
Chapter 14 is on Hitting Complexity with Neural Networks
Chapter 15 is on Going a Step Beyond Using Support Vector Machines
Chapter 16 is on Resorting to Ensembles of Learners

Part V: Applying Learning to Real Problems
Chapter 17 is on Classifying Images
Chapter 18 is on Scoring Opinions and Sentiments
Chapter 19 is on Recommending Products and Movies

Part VI: The Part of Tens
Chapter 20 is on Ten Ways to Improve Your Machine Learning
Chapter 21 is on Ten Guidelines for Ethical Data Usage
Chapter 22 is on Ten Machine learning Packages to Master

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15. Understanding Machine Learning

by Shai Shalev-Shwartz, Shai Ben-David

Best Suited For : Beginners and Advanced Graduate Students

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Understanding Machine Learning
by Shai Shalev-Shwartz, Shai Ben-David

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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.

Topics Covered:

  • 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

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16. Machine Learning for Algorithmic Trading

by Stefan Jansen

Best Suited For : Intermediate and Advanced Users

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Machine Learning for Algorithmic Trading by Stefan Jansen

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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.

Topics Covered:

  • Automated trading strategies
  • pandas, TA-Lib
  • scikit-learn
  • LightGBM
  • SpaCy
  • Gensim
  • TensorFlow 2
  • Zipline
  • backtrader
  • Alphalens
  • pyfolio
  • Machine learning algorithms
  • NLP
  • Deep learning

Part I: Data, alpha factors and portfolios
Chapter 1 starts with Machine Learning for Trading – From Idea to Execution
Chapter 2 is on Market and Fundamental Data – Sources and Techniques
Chapter 3 is on Alternative Data for Finance – Categories and use cases
Chapter 4 is on Financial Feature Engineering – How to Research Alpha Factors
Chapter 5 is on Portfolio Optimization and Performance Evaluation

Part II: ML for trading – Fundamentals
Chapter 6 is on The Machine Learning Process
Chapter 7 is on Linear Models – From Risk Factors to Return Forecasts
Chapter 8 is on The ML4T Workflow – From Model to Strategy Backtesting
Chapter 9 is on Time-Series Models for Volatility Forecasts and Statistical Arbitrage
Chapter 10 is on Bayesian ML – Dynamic Sharpe Ratios and pairs Trading
Chapter 11 is on Random Forests – A Long-Short Strategy for Japanese Stocks
Chapter 12 is about Boosting Your Trading Strategy
Chapter 13 is on Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning

Part III: Natural Language Processing
Chapter 14 is on Text Data for Trading – Sentiment Analysis
Chapter 15 is on Topic Modeling
Chapter 16 is on Word Embeddings for Earnings Calls and SEC Filings

Part IV: Deep and Reinforcement Learning
Chapter 17 is on Deep Learning for Trading
Chapter 18 is on CNNs for Financial Time Series and Satellite Images
Chapter 19 is on RNNs for Multivariate Time Series and Sentiment Analysis
Chapter 20 is on Autoencoders for Conditional Risk Factors and Asset Pricing
Chapter 21 is on Generative Adversarial Networks for Synthetic Time-Series Data
Chapter 22 covers Deep Reinforcement Learning
Chapter 23 is on Conclusions and Next Steps

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17. Building Machine Learning Powered Applications

by Emmanuel Ameisen

Best Suited For : Beginners and Advanced Users

Building Machine Learning Powered Applications by Emmanuel Ameisen

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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.

Topics Covered:

  • Identifying the right ML approach
  • Building an initial prototype
  • Iterating on models
  • Deployment and monitoring

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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:-

Machine Learning Tutorial Python -1: What is Machine Learning?
Machine Learning Course for Beginners


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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