To master the knowledge of a field requires systematic learning, and the knowledge learned from one book is far from enough. In addition, in addition to the technology related to the field, the corresponding industry development is also particularly important. This article will take machine learning as an example, from the shallow to the deep, combined with other related fields of technology, industry, etc., to recommend rich bibliography resources for readers.
A “mini map” to teach you how to attack machine learning!
As you can see from the chart, if you want to become an expert in machine learning, you need to start from the beginning, deep learning, data science, R language, Python, finance, expert level books, and so on.
Without further ado, let’s take a look at the Top10 most popular books related to machine learning.
Scikit-learn and TensorFlow Machine Learning Practical Guide
Hands-on Machine Learning with Scikit-Learn and TensorFlow
Aurelien Geron
Publisher: O’Reilly Media
With concrete examples, little theory, and two mature Python frameworks: SciKit-Learn and TensorFlow, this book helps you master the concepts and tools needed to build intelligent systems. You’ll learn a variety of techniques, from simple linear regression to deep neural networks. The exercises in each chapter will help you apply what you’ve learned, all you need is some programming experience.
From this book you will learn:
- Explore machine learning environments, especially neural networks
- Use Scikit-learn to track end-to-end sample machine learning projects
- Explore several training models, including support vector machines, decision trees, random forests, and set methods
- Build and train neural networks using the TensorFlow library
- Neural network architecture, including convolutional networks, cyclic networks and deep reinforcement learning
- Learn techniques for training and scaling deep neural networks
- Apply real-world code examples without learning too much machine learning theory or algorithm detail
2. Practical Statistics for Data Scientists
Practical Statistics for Data Scientists: 50 Essential Concepts
By Peter Bruce & Andrew Bruce
Publisher: O’Reilly Media
Many data science resources include statistical methods, but lack a deep statistical perspective. If you are familiar with R programming and have some knowledge of statistics, this quick reference will help you build Bridges of knowledge that are easy to learn and accessible.
From this book you will learn:
- Why is exploratory data analysis a key step in data science
- How can random sampling reduce bias and produce higher quality data sets, even for big data
- How do experimental design principles contribute to the final answer to the question
- How can regression be used to estimate results and detect anomalies
- Key classification techniques used to predict which category a record falls into
- Statistical machine learning approaches to “learning” from data
- Unsupervised learning methods for extracting meaning from unlabeled data
3. Python deep learning
Deep Learning with Python
By Francois Chollet
Publisher: Manning Publications
This book is an introduction to deep learning using the Python language and the powerful Keras library. This book, written by Keras author and Google AI researcher Francois Chollet, provides intuitive explanations and practical examples to help readers understand. You will apply challenging concepts and practices in computer vision, natural language processing and generative modeling. By the end of this book, you will have the knowledge and practical skills to apply deep learning to your own projects.
From this book you will learn:
- Basic principles of deep learning
- Build your own deep learning environment
- Image classification model
- Deep learning of text and sequences
- Neural style transfer, text generation and image generation
4. Deep Learning
English title: Deep Learning
By Ian Goodfellow, Yoshua Bengio & Aaron Courville
Publisher: The MIT Press
The “Flower Book” is considered a veritable AI bible. Deep Learning, co-authored by Ian Goodfellow, Yoshua Bengio and Aaron Courville, three leading and authoritative experts in the field of deep learning, has long topped the list of Artificial intelligence books on Amazon in the United States, and the Chinese version was sold out after it was published last year.
The book introduces basic mathematical knowledge, machine learning experience and the current theory and development of deep learning from the superficial to the deep, which can help ai technology enthusiasts and practitioners have a comprehensive understanding of deep learning under the guidance of the thinking of three experts and scholars.
This is a textbook, not just a textbook, and anyone interested in deep learning will benefit from reading this book.
Python: Pandas, NumPy and IPython for Data Analysis
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
By Wes McKinney
Publisher: O’Reilly Media
Written by The creator of the Python Pandas project, Wes McKinney, this book is a practical introduction to data science tools in Python. The book is ideal for analysts new to Python as well as Python programmers unfamiliar with data science and scientific computing. GitHub offers data files and related materials.
Reading this book you will learn:
- Use IPython shell and Jupyter Notebook for exploratory calculations
- Learn basic and advanced features in NumPy
- Learn to use the data analysis tools in pandas
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create information visualizations using Matplotlib
- Apply the Pandas GroupBY tool to slice, DICE blocks, and aggregate datasets
- Analyze and process regular and irregular time series data
- Detailed examples of how to solve real-world data analysis problems
6. R Data Science
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
By Hadley Wickham and Garrett Grolemund
Publisher: O’Reilly Media
The goal of this book is to provide a solid foundation for implementing data science by teaching readers to use important data science tools. By the end of this book, you will have mastered the essence of R and will be able to use a variety of tools to solve various data science problems. Each chapter is organized in such an order: give some fascinating examples first so that you can understand the chapter as a whole, and then dive into the details. Each section of this book is accompanied by exercises to help you put what you have learned into practice.
This book is for R data scientists.
7. Python Data Science Manual
Python Data Science Handbook: Essential Tools for Working with Data
By Jake VanderPlas
Publisher: O’Reilly Media
This book is a reference for science, research, and computational and statistical methods centered on the need for depth of data. Each of the five chapters introduces one or two key toolkits in Python data science. Start with IPython and Jupyter, which provide the computing environment data scientists need; Chapter 2 looks at NumPy, which provides ndarray objects that can store and manipulate large arrays efficiently in Python. Chapter 3 focuses on Pandas, which provides DataFrame objects that efficiently store and manipulate labeled/column data in Python. Chapter 4 focuses on Matplotlib, which provides Python with many data visualization capabilities; Chapter 5 focuses on SciKit-learn, a library that provides an efficient and clean Python implementation of important machine learning algorithms.
The book is for data science researchers with a programming background who intend to use open source Python tools for analyzing, manipulating, visualizing, and learning about data.
8. Python Machine Learning
Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow
Sebastian Raschka and Vahid Mirjalili
Publisher: Packt Publishing – EBooks Account
Machine learning is eating the software world, and deep learning is expanding machine learning. Learn about and implement the cutting edge of machine learning, neural networks, and deep learning with the second edition of Sebastian Raschka’s bestselling Python Machine Learning. Thoroughly updated with the latest Python open source libraries, this book provides the practical knowledge and techniques needed to create machine learning, deep learning, and modern data analysis.
From this book, you will learn:
- Understand key frameworks for data science, machine learning, and deep learning
- Use Python’s latest open source library for machine learning
- Explore machine learning techniques with challenging real data
- Use the TensorFlow library to master deep neural networks
- Understand the mechanism of classification algorithms to achieve optimal work
- Use regression analysis to predict continuous goal outcomes
- Discover hidden patterns and structures in the data through clustering
- Use sentiment analysis to dig deep into text and social media data
9. Python Pocket Guide
Python Pocket Reference: Python In Your Pocket (Pocket Reference (O’Reilly))
By Mark Lutz
Publisher: O’Reilly Media
This guide is a perfect hands-on quick reference for Python 3.4 and 2.7. You’ll learn about Python types and statements, special method names, built-in functions and exceptions, common standard library modules, and other Python tools.
The Python Pocket Guide (Fifth Edition), written by Mark Lutz, a recognized leader in Python, is a classic Python tutorial (Learning Python and Programming Python, O ‘Reilly) is the ideal assistant.
This book covers:
- Built-in object types, including numbers, lists, dictionaries, and more
- Statements and syntax for creating and processing objects
- Structuring and reusing functions and modules that code uses
- Python is an object-oriented programming tool
- Built-in functions, exceptions, and properties
- Proprietary operator overload methods
- Widely used standard library modules and extensions
- Command-line options and development tools
- Python idioms and hints
- Python’s SQL database API
10. Fundamentals of Statistical Learning
The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
By Trevor Hastie and Robert Tibshirani
Publisher: Springer
The rapid development of computing and information technology has brought about huge amounts of data in fields as diverse as medicine, biology, finance and marketing. Making sense of this data is a challenge that has led to the development of new tools in statistics and extended to new fields such as data mining, machine learning and bioinformatics. Many tools have common ground, but are often expressed in different terms. Fundamentals of Statistical Learning (2nd Ed.)(English) introduces some important concepts in these fields. Although statistical methods are used, the emphasis is on concepts rather than mathematics. Many examples are accompanied by colour drawings. Fundamentals of Statistical Learning (2nd Edition) covers a wide range of topics from guided learning (forecasting) to unguided learning. Topics including neural networks, support vector machines, classification trees, and ascension are the most comprehensive of their kind.
Fundamentals of Statistical Learning (2nd edition) can be used as a teaching material for undergraduate and graduate students in the relevant fields of higher education. For those who are interested in data mining, the fundamentals of Statistical Learning (2nd edition) is worth reading.
According to the mini-map, it can be divided into seven “areas”, each with its own recommended books.
Recommended advanced books for beginners
1, Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
By Oliver Theobald
2, “Make Your Own Neural Network: An In-depth Visual Introduction For Beginners”
By Michael Taylor
3. The Math of Neural Networks
By Michael Taylor
Deep learning related bibliography recommendations
1. Deep Learning with Python
By Francois Chollet
2. Deep Learning: A Practitioner’s Approach
By Josh Patterson and Adam Gibson
Neural Networks with R: Smart Models Using CNN, RNN, Deep Learning, and Artificial Intelligence Principles
By Giuseppe Ciaburro and Balaji Venkateswaran
Recommended books on data science
Data Science from Scratch: First Principles with Python
By Joel Grus
2. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
Provost and Tom Fawcett
3. Think Bayes: Bayesian Statistics in Python
By Allen B. Downey
R language related bibliography recommended
1. Ggplot2: Elegant Graphics for Data Analysis (Use R!)
By Hadley Wickham
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O ‘Reilly Cookbooks)
By Paul Teetor
3. R Graphics Cookbook: Practical Recipes for Visualizing Data
By Winston Chang
Recommended Python books
Introducing Python: Modern Computing in Simple Packages
By Bill Lubanovic
2. Learning Python, 5th Edition
By Mark Lutz
3. Fluent Python: Clear, Concise, and Effective Programming
By Luciano Ramalho
Recommended books related to finance
1. Advances in Financial Machine Learning
By Marcos Lopez de Prado
2, Building Winning Algorithmic Trading Systems, + Website: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)”
By Kevin J. Davey
Algorithmic Trading: Winning Strategies and Their Rationale
By Ernie Chan
Expert book recommendation
1. Pattern Recognition and Machine Learning (Information Science and Statistics)
By Christopher M. Bishop
Machine Learning (McGraw-hill International Editions Computer Science Series)
By Tom M. Mitchell
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
By Richard S. Sutton and Andrew G. Barto
For more book recommendations, see the link to the original:
Hands-On Machine Learning and its related books
anvaka.github.io
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