## Best Books On Artificial Intelligence And Machine Learning

Here is a list of some of our favourite and best artificial intelligence books.

No matter how much you understand the concept, each of these books will help further your knowledge.

## 1. Artificial Intelligence: Guide for Absolute Beginner

This AI book is a must for anyone looking to learning the basics.

The overall aim is to explore and examine key concepts, methods and techniques used in Artificial Intelligence. It provides readers with the information and tools necessary to start understanding smart machines, deep learning, machine learning, big data, speech recognition, cognitive computing and weak and strong artificial intelligence.

The book presents the following points:

- An Introduction To Descriptive Statistics
- An Introduction To Artificial Intelligence
- The Artificial Intelligence Ecosystem
- Big Data And Artificial Intelligence
- Embracing Emerging Technology
- Exploring Data Types
- Associated Techniques
- Data Mining

## 2. Mining of Massive Datasets

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets.

It begins with a discussion of the map-reduce framework, an important tool for parallelising algorithms automatically.

Some of the preceding chapters include:

- The tricks of locality-sensitive hashing
- Stream processing algorithms for mining data that arrives too fast for exhaustive processing
- The PageRank idea and related tricks for organising the Web
- The problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

## 3. Deep Learning

This artificial intelligence book gives an introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

*Deep Learning* is perfect for university students, people looking for a career in AI in either industry or research or engineers developing a new product or platform.

This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

It describes deep learning techniques used by practitioners in industry, such as:

- Deep feedforward networks
- Regularisation
- Optimisation algorithms
- Convolutional networks
- Sequence modeling
- Practical methodology

Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

## 4. Understanding Machine Learning: From Theory to Algorithms

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this artificial intelligence textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks.

These include:

- Discussing the computational complexity of learning and the concepts of convexity and stability
- Important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning
- Emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

## 5. Python Machine Learning By Example

This AI book is for anyone interested in entering the data science stream with machine learning. This book starts with an introduction to machine learning and Python and shows you how to complete the setup.

Moving ahead, you will learn all the important concepts such as:

- Exploratory data analysis
- Data preprocessing
- Feature extraction
- Data visualisation and clustering
- Classification
- Regression and model performance evaluation

An interesting feature of this book is that it gives you a step by step process to build your own models from scratch. Towards the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.

## 6. Probabilistic Programming and Bayesian Methods for Hackers

This book illustrates the Bayesian inference through probabilistic programming with PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib.

It starts by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, it introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback.

You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing.

Some of the topics this book covers include:

- Learning the Bayesian “state of mind” and its practical implications
- Understanding how computers perform Bayesian inference
- Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
- Using Bayesian inference to improve A/B testing

## 7. Think Stats: Probability and Statistics for Programmers

The final book on this list covers how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses.

You’ll explore distributions, rules of probability, visualisation, and many other tools and concepts.

By the end of the book you will be able to:

- Develop an understanding of probability and statistics by writing and testing code
- Run experiments to test statistical behavior, such as generating samples from several distributions
- Use simulations to understand concepts that are hard to grasp mathematically
- Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
- Use statistical inference to answer questions about real-world data