Best Machine Learning Books - Download all PDF for Free

 

Best Machine Learning Books - LunaticAI
Hi Everyone! Thanks for your so much love and support on our first post of Top 21 Series i.e. 21 Best Programming Books Of All-time - Download all PDF For FreeThis is the second post inside this series. Today, I'm gonna share some great, newly released and best machine learning books of 2021. You can check the detailed reviews as well as download all the books in pdf version for free. Stay tuned till the last and if you find this list helpful then please share it with others.

1. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Deep Learning for Coders with Fastai and PyTorch - LunaticAI
Author: Jeremy Howard and Sylvain Gugger

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In this book, Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala.
2. Grokking Deep Reinforcement Learning
Grokking Deep Reinforcement Learning - LunaticAI
Author: Miguel Morales

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Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.

3. Machine Learning Engineering
Machine Learning Engineering - LunaticAI
Author: Andriy Burkov

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The most comprehensive book on the engineering aspects of building reliable AI systems.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." - Cassie Kozyrkov, Chief Decision Scientist at Google

"Foundational work about the reality of building machine learning models in production." - Karolis Urbonas, Head of Machine Learning and Science at Amazon

4. GANs in Action: Deep learning with Generative Adversarial Networks
GANs in Action: Deep learning with Generative Adversarial Networks - LunaticAI
Author: Jakub Langr and Vladimir Bok

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This book teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you’ll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.
5. AI and Machine Learning for Coders
AI and Machine Learning for Coders - LunaticAI
Author: Laurence Moroney

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If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

You’ll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.
6. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition - LunaticAI
Author: Andrew Bruce, Peter C. Bruce, and Peter Gedeck

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This book is for aspiring Data Scientists with no proper training in Statistics. This book eliminates the overdose of statistics and gives just the ideas important to Data Scientists. Besides, this book is for individuals who have contemplated the essential information on Statistics. Besides, this book gives instances of statistical utilizing R. This will permit you to practice the necessary concepts and sharpen your R skills. The structure of this book is as per real-world uses of Data Science.
7. Regression and Other Stories
Regression and Other Stories - LunaticAI
Author: Aki Vehtari, Andrew Gelman, and Jennifer Hill

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This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
8. Approaching (Almost) Any Machine Learning Problem
Approaching (Almost) Any Machine Learning Problem - LunaticAI
Author: Abhisek Thakur

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This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
9. Deep Learning from Scratch: Building with Python from First Principles
Deep Learning from Scratch: Building with Python from First Principles - LunaticAI
Author: Seth Weidman

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This eBook Provides:

- Extremely clear and thorough mental models - accompanied by working code examples and mathematical explanations - for understanding neural networks. 

- Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework.

- Working implementations and clear-cut explanations of convolutional and recurrent neural networks.

- Implementation of these neural network concepts using the popular PyTorch framework.


10. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers - LunaticAI
Author: Daniel Situnayake and Pete Warden

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With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step.
11. Machine Learning Design Patterns
Machine Learning Design Patterns - LunaticAI
Author: Michael Munn, Sara Robinson, and Valliappa Lakshmanan

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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
12. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow - LunaticAI
Author:  Aurelien Geron

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This machine Learning book is the best bet if you want to dive as soon as possible into the application of machine learning and artificial intelligence in real-world projects. According to author, before you start with this book, you should have some Python programming experience and you should be familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib. 

Each chapter in the machine learning book features numerous exercises that will help you apply what you’ve learned till that time.
13. Hands-On Gradient Boosting with XGBoost and scikit-learn
Hands-On Gradient Boosting with XGBoost and scikit-learn - LunaticAI
Author: Corey Wade and Kevin Glynn

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The Hands-On Gradient Boosting with XGBoost and scikit-learn eBook introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. 
14. Kubeflow for Machine Learning
Kubeflow for Machine Learning - LunaticAI
Author: Ilan Filonenko, Holden Karau, Boris Lublinsky, Richard Liu, Trevor Grant

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If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.
15. Machine Learning and Data Science Blueprints for Finance
Machine Learning and Data Science Blueprints for Finance - LunaticAI
Author: Brad Lookabaugh, Hariom Tatsat, and Sahil Puri

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- With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. 

- You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and robo-advisor and chatbot development. 

- You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. 
16. Machine Learning with R, the Tidyverse, and Mlr
Machine Learning with R, the Tidyverse, and Mlr - LunaticAI
Author: Hefin Rhys

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Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation.
17. Feature Engineering and Selection
Feature Engineering and Selection - LunaticAI
Author: Kjell Johnson and Max Kuhn

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This book describes techniques for finding the best representations of predictors for modeling and for finding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
18. Machine Learning in Finance: From Theory to Practice
Machine Learning in Finance: From Theory to Practice - LunaticAI
Author: Matthew F. Dixon, Igor Halperin and Paul Bilokon

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. 

This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.
19. Building Machine Learning Pipelines
Building Machine Learning Pipelines - LunaticAI
Author: Catherine Nelson and Hannes Hapke

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In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.

Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.
20. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
Practical Deep Learning for Cloud, Mobile, and Edge - LunaticAI
Author: Hannes Hapke and Catherine Nelson 

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This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly.
21. Building Machine Learning Powered Applications: Going from Idea to Product
Building Machine Learning Powered Applications: Going from Idea to Product - LunaticAI
Author: Emmanuel Ameisen

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Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including experienced practitioners and novices alike-will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. 


So, That is all! We hope this list helps you. Please if you like this then share it with your networks and needed ones. This books are for personal use only. If you are capable of buying then purchase a hardcopy from Amazon.

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August 21, 2021
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