Best Machine Learning Books to Go One Step Ahead
Machine Learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is one of the most exciting technologies that one would have ever come across. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Here you will get some of the best Machine Learning books to learn and apply fundamental Machine Learning concepts from the best in this field, get real-world experience and suggestions and lots of training resources.
Deep learning has taken the world of technology by storm since the beginning of the decade. There was a need for a textbook for students, and instructors that include basic concepts, practical aspects, and advanced research topics. This is the first comprehensive textbook on the subject, written by some of the most innovative and prolific researchers in the field. This will be a reference for years to come.
This book covers:
- Machine Learning Basics.
- Applied Math.
- Linear Algebra.
- Probability and Information Theory.
- Numerical Computation.
- Deep Feedforward Networks.
- Regularization for Deep Learning.
- Optimization for Training Deep Models.
- Convolutional Networks.
- Sequence Modeling: Recurrent and Recursive Nets.
This book is a great introduction to machine learning and it provides a great practical guide to get started and execute on Machine Learning. A wonderful book for engineers who want to incorporate Machine Learning in their day-to-day work without necessarily spending an enormous amount of time going through a formal degree program. This book will benefit newcomers to the field as a thorough introduction to the fundamentals of machine learning being one of the best Machine Learning books.
This book includes:
- What is Machine Learning?
- Types of Machine Learning.
- Notations and Definitions.
- Fundamental Algorithms.
- Anatomy of a Learning Algorithm.
- Basic Practice.
- Neural Networks and Deep Learning.
- Problems and Solutions.
- Advanced Practice.
Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along even at home. This is a book to read first and then digging into specialization. It will definitely help you to grasp the essentials and to have a base for the next level.
This book features:
- Introduction of Machine Learning.
- From Data Science to AI, to Machine Learning.
- Needed Tools.
- Machine Learning Categories.
- Machine Learning in Action.
- Regression Analysis.
- Clustering Analysis.
- Dimentionality Reduction.
- Support Vector Machines.
- Artificial Neural Networks.
- Bias & Variance.
- Decision Trees.
- Algorithm Selection.
- Development Environment.
- Building a model in Python.
- Career & Study Options.
- Further Resources.
This book covers a ton of Machine Learning algorithms and applications with example code but the code is so succinct that it's not really a programming book as much as a crash course in some Machine Learning math libraries available for Python, what the algorithms do and when to use them. It does give clear examples and explanations of when to use each algorithm and how. It's all terribly practical and understandable. This book has an intuitive structure that elaborates at length on core Machine Learning concepts and doesn't overburden with complex programming. Very well written and understandable introduction, covering a broad range of topics. Excellent reference book.
This book will help you to learn:
- Fundamental concepts and applications of machine learning.
- Advantages and shortcomings of widely used machine learning algorithms.
- How to represent data processed by machine learning, including which data aspects to focus on.
- Advanced methods for model evaluation and parameter tuning.
- The concept of pipelines for chaining models and encapsulating your workflow.
- Methods for working with text data, including text-specific processing techniques.
- Suggestions for improving your machine learning and data science skills.
This book provides excellent descriptions of the key methods used in predictive analytics. It is a wonderful self-contained book that touches upon the essential aspects of Machine Learning and presents them in a clear and intuitive light. With its incremental discussions ranging from anecdotal accounts underlying the 'big idea' to more complex information-theoretic, probabilistic, statistic, and optimization theoretic concepts, its emphasis on how to turn a business problem into an analytics solution, and its pertinent case studies and illustrations. This book makes for an easy and compelling read, which can motivate greatly to anyone interested in finding out more about machine learning and its applications to predictive analytics.
This book delivers:
- Machine Learning for Predictive Data Analytics.
- Data to Insights to Decisions.
- Data Exploration.
- Information based Learning.
- Similarity-based Learning.
- Probability-based Learning.
- Error based Learning.
- Case Study: Customer Churn.
- Case Study: Galaxy Classification.
- The Art of Machine Learning for Predictive Data Analytics.
- Descriptive Statistics and Data Visualization for Machine Learning.
- Introduction to Probability for Machine Learning.
- Differentiation Techniques for Machine Learning.
This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. This book will help you to understand Machine Learning algorithms such as Bayesian and ensemble methods and manifold learning and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will learn to apply transfer learning to satellite images to predict the economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.
This book contents:
- Machine Learning for Trading.
- Market and Fundamental Data.
- Alternative Data for Finance.
- Alpha Factor Research.
- Strategy Evaluation.
- The Machine Learning Process.
- Linear Models.
- Time Series Models.
- Bayesian Machine Learning.
- Decision Trees and Random Forests.
- Gradient Boosting Machines.
- Unsupervised Learning.
- Working with Text Data.
- Topic Modeling.
- Word Embeddings.
This book is one of the best introduction to Machine Learning. It is very well organized and well written. It doesn't go into very advanced topics, but it is a great resource for understanding the basic concepts and also for your interviews in Machine Learning. It provides a unique approach to machine learning, contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. This book provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.
This book provides:
- Machine Learning Introduction.
- Fundamentals of Numerical Optimization.
- Automatic feature design for regression.
- Automatic feature design for classification.
- Kernels, backpropagation, and regularized cross-validation.
- Advanced gradient schemes.
- Dimension reduction techniques.
This is your quick guide to implementing TensorFlow in your day-to-day machine learning activities. You will learn advanced techniques that bring more accuracy and speed to machine learning. You will upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
This book offers:
- The basics of the TensorFlow machine learning library.
- Linear Regression techniques with TensorFlow.
- SVMs with hands-on recipes.
- Implementation of Neural Networks and Improve Predictions.
- Apply NLP and sentiment analysis to your data.
- Master CNN and RNN through practical recipes.
- Take TensorFlow into production.
This book offers a survey of machine learning techniques along with descriptions of current Big Data solutions. The author covers quite a lot in the book as the discussion ranges from machine learning algorithms, statistical math, Hadoop, and data science programming languages. In the later chapters, the book gives code samples in 5 different frameworks which are Mahout, R, Spark, Python, and Julia. This is a really outstanding book that gives you an explanation of what machine learning is in the most direct way.
This book features:
- Introduction to Machine Learning.
- Machine Learning and Large scale Datasets.
- Introduction to Hadoop Architecture and Ecosystem.
- Machine Learning Tools, Libraries and Frameworks.
- Decision Tree-based Learning.
- Instance and Kernel method based Learning.
- Association Rules-based Learning.
- Clustering-based Learning.
- Bayesian Learning.
- Regression-based Learning.
- Deep Learning.
- Reinforcement Learning.
- Ensemble Learning.
- New-generation data architectures for machine learning.
Deep learning has boosted the entire field of machine learning. By using concrete examples, minimal theory, and two production-ready Python frameworks, Scikit-Learn and TensorFlow, this book helps you to gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter, this best Machine Learning books will help you apply what you’ve learned, all you need is programming experience to get started.
This book delivers:
- Machine learning landscape, particularly neural nets.
- Using Scikit-Learn to track an example machine-learning project end-to-end.
- Several training models, including support vector machines, decision trees, random forests, and ensemble methods.
- Using the TensorFlow library to build and train neural nets.
- Diving into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learning techniques for training and scaling deep neural nets.