Top 10 Data Science and AI Books
“Books are the soul of understanding intelligence.’
Machine Learning is a huge field, and its research is one of the most enlightening tasks that could ever be undertaken. Today, most of the business operations and technologies are carried out around ML and its innovative applications. Several professionals are developing advanced ML knowledge to thrive in their respective fields. They are more interested in learning about offerings, advancements, expert opinions, and different nuances in the context of machine learning or artificial intelligence (AI) as a whole.
If you are a tech devotee and are looking forward more to learning some ideas and innovations about machine learning, you can find a lot of comprehensive books that demonstrate and offer a variety of skills, advice, and learning opportunities. Here is a list of top 10 machine learning book techies to be read in 2020.
You want to learn ML, but don’t know how?
Well, before you embark on your epic journey to ML, you should first know some important theoretical and statistical values. And that’s where the book comes in! It’s a practical and high-level introduction to ML for complete beginners.
Machine Learning for Absolute Beginners:
- Author: Oliver Theobald.
Machine Learning for Total Beginners shows you everything you need, from learning how to download free datasets to software and ML collections. Topics such as data scrubbing techniques, regression analysis, clustering, neural networks, bias/variance, decision tree, etc. are also covered.
Machine Learning for Absolute Beginners:
Programming Collective Intelligence:
- Author: Toby Segaran
Programming Collective Wisdom is less of an introduction to ML and more of a guide for the implementation of ML. The book outlines the design of powerful ML algorithms for collecting data from applications, designing programs for accessing data from websites, and inferring collected data. Can chapter include exercises to extend the mentioned algorithms and further develop their efficiency and effectiveness?
The topics discussed in this book are Bayesian filtering, collaborative filtering strategies, emerging problem-solving intelligence, group or pattern detection methods, non-negative matrix factoring, search engine algorithms, vector support machines, and ways to make predictions.
Programming Collective Intelligence:
Artificial Intelligence and Machine Learning for Business.
- Author: Scott Chesterton.
Scott Chesterton’s book is not long-read or does not include advanced coding examples, but serves as a good technical guide on how to run AI and ML projects, how ML tools and techniques may best be used to process big data, and how to interpret the analytical results of a predictive model. The book is intended for intermediate-level users who are familiar with ML resources, frameworks, and techniques.
The book would be especially useful for ML engineers and analytics managers in companies who are looking to implement new AI and ML ventures to drive market growth or construct their business strategy.
Artificial Intelligence and Machine Learning for Business.
The Hundred-Page Machine Learning Book.
- Author: Andriy Burkov (2019).
Based on theoretical and practical applications, this book brings readers through machine learning in a condensed manner. The author provides readers with background information on basic machine learning topics through essential discussions needed to enhance their understanding.AI is a vast area, machine learning is key to being a professional, and this author takes care of all these considerations in Python.
From the first paragraph to the last, Burkov engages readers by systematically taking them into the world of machine learning. Around the same time, machine learning engineers are finding this book realistic because of the method used by the author to illustrate statistical and mathematical concepts. If you’re looking for a book that gives you an objective overview of the machine learning field and realistic use cases, this is your text. The Hundred-Page ML Book offers tools to allow readers to implement solutions in the real world.
The Hundred-Page Machine Learning Book.
Deep Learning with JavaScript: Neural networks in TensorFlow.js
- Authors: Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet (2020).
Are you interested in learning how to build a deep learning framework without using Python or R languages but JavaScript?
Then, this is your book given the comprehensive JavaScript programming knowledge provided in the book.
What about building a JavaScript deep learning program on your browser?
It is another significant field that makes Deep Learning special with JavaScript as readers learn new resources including Node-based Backends.
JavaScript is a popular programming language with data structures, APIs, and front-end systems. Authors include case studies for developers, including web transfer programs, user language processing, and image server processing. Readers may also predict generative deep learning that helps them to create text and generate images — all in JavaScript.
Deep Learning with JavaScript: Neural networks in TensorFlow.js
Mastering Large Datasets with Python:
- Author: John T. Wolohan (2020).
In the output, scaling ML requires extensive processing power such as GPUs and TPUs. Mastering Massive Datasets with Python teaches you practical tools for dealing with parallel and distributed systems including threading, processes, and concurrency. Wolohan teaches how to begin with quick, small projects that expand into Big Data pipelines.
According to Wolohan, it is important to use Python’s functional approaches to achieve optimal performance.
Distributed technology is being explored to train students for massive datasets on cloud-based systems. If you are interested in developing systems with Python, large data sets, and distributed data science models, this book will guide you through step-by-step processes.
Mastering Large Datasets with Python:
Data Science in Production.
- Author: Ben Weber (2020).
An understanding of data from the initial to the production phases is another case example illustrated in the book and offers meaningful insights to readers.
Areas such as cloud deployment, developing web endpoints and models of machine learning are additional examples covered in the book. Weber teaches from a top-down approach: build reproducible models that can scale well in production. Between PySpark, Pub/Sub techniques, and Kafka, Weber deeps dive into essential data science tools.
The comprehension of data from the initial phase to the production phase is another example of the case illustrated in the book and provides readers with meaningful insights.
Areas such as cloud deployment, web endpoint development, and machine learning models are additional examples covered in the book. Weber teaches from a top-down approach: to build reproducible models that can scale well in production. Alongside PySpark, Pub / Sub techniques, and Kafka, Weber deeps into essential data science tools.
Data Science in Production
Seven Databases in Seven Weeks 2nd Edition.
- Authors: Eric Redmond, Jim Wilson (2018).
Massive data structures call for large databases and database frameworks. Data Scientists must be comfortable working with multiple database systems, and Seven Seven Weeks Databases will dive deep into Redis, Neo4J, CouchDB, MongoDB, HBase, Postgres, and DynamoDB. Redmond and Wilson provide practical data modelling systems that mimic database systems for Fortune 500 companies.
Seven Databases in Seven Weeks 2nd Edition.
Artificial Intelligence with Python — Second Edition.
- Authors: Alberto Artasanchez, Prateek Joshi (2020).
Python’s Artificial Intelligence provides an overview of data science, machine learning, and AI applied across industries. The authors focus on students learning about the essentials of building ML pipelines. AI and cloud development tools are additional topics that you can learn from AI with Python.
Artasanchez and Joshi have updated their best-selling book TensorFlow 2.0 and the latest Python 3.9. Students are immersed in feature engineering and data pipelines, as well as advanced use cases such as speech recognition and chatbots. The authors also guide students to implement and deploy their machine learning systems through neural, deep learning, and cloud networks.
Artificial Intelligence with Python — Second Edition
The Elements of Statistical Learning.
- Author: Trevor Hastie, Robert Tibshirani, and Jerome Friedman
If you like statistics and want to learn machine learning from a statistic perspective, the Elements of Statistical Learning is a book you need to read. The ML book focuses on mathematical derivations for defining the underlying logic of the ML algorithm. Before you pick up this book, make sure you have at least a basic understanding of linear algebra.
The concepts explained in the Elements of Statistical Learning Book are not beginner-friendly. Therefore, you might find it difficult to digest. However, if you still want to learn them, you can check out the Introduction to Statistical Learning Book. It explains the same concepts but, in a novice, -friendly way.
The Elements of Statistical Learning.
Practical Data Science with R, (2nd edition).
- Authors: Nina Zumel, John Mount (2019).
Trained for R programming, Practical Data Science with R selects practical examples that students need to understand data science and apply their skills to R.Readers learn about the interpretation of statistical analysis, data science workflow, and presentation design.
Practical Data Science with R, (2nd edition).
“‘ Books take a few minutes for the sharing intelligence.’’
Conclusion:
The idea is not new. New Data Science and AI Books are airing all the time, and we can only expect the number of data science books to develop as the field continues to explode in popularity.
It doesn’t matter what you choose! If you’re really in love with Data Science a book that can add a lot of fun to your everyday life and lead your career as a Data Scientist.Choose the one that fits you best and learns, understands, knows!