Coding in Python: A Comprehensive Beginners Guide to Learn the Realms of Coding in Python (Free PDF)

 

Python Coding is not a book you can read while relaxing on the couch. This book is for those that are ready to start working right away to write your own codes. I do not recommend this book if you are the type of person who reads a book once and never opens it again because you think you have mastered the book's technical contents. Tech books are different from novels and other non-fiction books. They demand more than merely one reading. Buy this book if you have made up your mind to read it and practice it again and again.

This book will compel you to step into the practical world. What makes this book different from the other books is its specific features and contents. Let's take a look at both.

Features of the book:

• Practicality
• Easy-to-digest
• Clarity of concepts
• Simplicity of communication
• Clear examples

You'll discover...

• Python installation process
• Python data types
• Python lists
• Python tuples
• Python if, if-else, if-elif statements
• Python dictionaries and dictionary constructors
• Python functions
• Python for and while loops
• Python input function
• Python classes, child classes, import functions
• And more!

If you're interested in the practical application of learning to code with Python, then this book is for you.

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Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance (Free PDF)

 

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. 

Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. 

What You Will Learn

Discover applied machine learning processes and principles

Implement machine learning in areas of healthcare, finance, and retail

Avoid the pitfalls of implementing applied machine learning

Build Python machine learning examples in the three subject areas

Who This Book Is For

Data scientists and machine learning professionals.  

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Python Data Structures and Algorithms (Free PDF)

 

A knowledge of data structures and the algorithms that bring them to life is the key to

building successful data applications. With this knowledge, we have a powerful way to

unlock the secrets buried in large amounts of data. This skill is becoming more important in

a data-saturated world, where the amount of data being produced dwarfs our ability to

analyze it. In this book, you will learn the essential Python data structures and the most

common algorithms. This book will provide basic knowledge of Python and an insight into

the exciting world of data algorithms. We will look at algorithms that provide solutions to

the most common problems in data analysis, including sorting and searching data, as well

as being able to extract important statistics from data. With this easy-to-read book, you will

learn how to create complex data structures such as linked lists, stacks, and queues, as well

as sorting algorithms such as bubble sort and insertion sort. You will learn the common

techniques and structures used in tasks such as preprocessing, modeling, and transforming

data. We will also discuss how to organize your code in a manageable, consistent, and

extendable way. You will learn how to build components that are easy to understand,

debug, and use in different applications.

A good understanding of data structures and algorithms cannot be overemphasized. It is an

important arsenal to have in being able to understand new problems and find elegant

solutions to them. By gaining a deeper understanding of algorithms and data structures,

you may find uses for them in many more ways than originally intended. You will develop

a consideration for the code you write and how it affects the amount of memory and

CPU cycles to say the least. Code will not be written for the sake of it, but rather with a

mindset to do more using minimal resources. When programs that have been thoroughly

analyzed and scrutinized are used in a real-life setting, the performance is a delight to

experience. Sloppy code is always a recipe for poor performance. Whether you like

algorithms purely from the standpoint of them being an intellectual exercise or them

serving as a source of inspiration in solving a problem, it is an engagement worthy of

pursuit.

The Python language has further opened the door for many professionals and students to

come to appreciate programming. The language is fun to work with and concise in its

description of problems. We leverage the language's mass appeal to examine a number of

widely studied and standardized data structures and algorithms.

The book begins with a concise tour of the Python programming language. As such, it is not

required that you know Python before picking up this book.

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Python 101: 2nd Edition

 

The second edition of Python 101 (50,000+ eBook readers alone) uses Python 3 to teach you how to program successfully. This book can be read by people with no programming experience, although understanding basic computer terminology is recommended.

While Python 101 teaches you all the basics that you need to know, it also dives into many intermediate level topics too. This book is split up into four sections:

The Python Language

Intermediate Topics

Creating Sample Applications

Distributing Your Code

This book has been completely rewritten from the ground up. There are many new chapters and coverage of new features in Python 3.

Another new feature is that Python 101 has Review Questions for every chapter. The book also includes an answer key in the back.

Unlike most beginner Python books, Python 101 explains how to create simple applications. It also goes on to teach you how to distribute your code as a Windows executable.

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Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib (Free PDF)

 

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.

Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.

After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.

What You'll Learn

Work with vectors and matrices using NumPy

Plot and visualize data with Matplotlib

Perform data analysis tasks with Pandas and SciPy

Review statistical modeling and machine learning with statsmodels and scikit-learn

Optimize Python code using Numba and Cython

Who This Book Is For

Developers who want to understand how to use Python and its related ecosystem for numerical computing.

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The Little Book of Deep Learning François Fleuret (Free PDF)

 

Unlock the Power of Deep Learning Embark on an extraordinary journey into the realm of cutting-edge technology with The Little Book of Deep Learning . Discover the secrets behind one of the most revolutionary advancements of our time, and witness how it is transforming industries across the globe. Explore the intricate world of neural networks and artificial intelligence as you delve into the minds of experts and pioneers. Gain unparalleled insights into the principles, algorithms, and applications of deep learning, unraveling complex concepts with ease. From image recognition to natural language processing, uncover the limitless possibilities that await you within these pages. Witness how deep learning is reshaping medicine, finance, entertainment, and more, igniting a new era of innovation. Written by a leading authority in the field, this captivating book distills the essence of deep learning, providing a comprehensive yet accessible guide for both beginners and seasoned professionals. Its engaging narrative and practical examples will empower you to harness the true potential of this transformative technology. Don't miss your chance to join the ranks of those who have unlocked the power of deep learning. Whether you're a student, researcher, or industry enthusiast, The Little Book of Deep Learning is your gateway to a world of unlimited possibilities.

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Its hard to believe, but the best 6 machine learning books are completely free:

 

- Deep Learning - https://lnkd.in/gxpnZ6Sa

- Dive into Deep Learning - d2l.ai

- Machine Learning Engineering - https://lnkd.in/eVCAYh4

- Python Data Science Handbook - https://lnkd.in/ehfZ-Tx

- Probabilistic Machine Learning - https://lnkd.in/gcSBFgk

- Machine Learning Yearning - https://lnkd.in/d3bC2d2R

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Approaching (Almost) Any Machine Learning Problem (PDF Book)

 

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.

Table of contents:

- Setting up your working environment

- Supervised vs unsupervised learning

- Cross-validation

- Evaluation metrics

- Arranging machine learning projects

- Approaching categorical variables

- Feature engineering

- Feature selection

- Hyperparameter optimization

- Approaching image classification & segmentation

- Approaching text classification/regression

- Approaching ensembling and stacking

- Approaching reproducible code & model serving

There are no sub-headings. Important terms are written in bold.

I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, please create an issue on github repo: https://github.com/abhishekkrthakur/approachingalmost

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The Principles of Deep Learning Theory (Free PDF)

 

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus. informal probability theory. it can easily fill a semester long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning. 

Book Buy : The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks

Book Main page : https://arxiv.org/abs/2106.10165

PDF Link : https://arxiv.org/pdf/2106.10165.pdf

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10 BOOKS THAT WILL BOOST YOUR PRODUCTIVITY!

 

10 BOOKS THAT WILL BOOST YOUR PRODUCTIVITY!

1. Focus on What Matters: A Collection of Stoic Letters on Living Well https://amzn.to/3RgjAJe

2. How to Finish Everything You Start https://amzn.to/3N1KtOr

3. Do It Today: Overcome Procrastination, Improve Productivity, and Achieve More Meaningful Things https://amzn.to/3uDL9mK

4. Atomic Habits: An Easy and Proven Way to Build Good Habits and Break Bad Ones https://amzn.to/49UM3vy

5. Deep Work: Rules for Focused Success in a Distracted World https://amzn.to/3T0lPSo

6. Attention Span: Finding Focus for a Fulfilling Life https://amzn.to/3uv1Lgx

7. Time Wise https://amzn.to/3QUmphJ

8. Do Epic Shit https://amzn.to/3QZfaFc

9. Do the Hard Things First: How to Win Over Procrastination and Master the Habit of Doing Difficult Work (Bulletproof Mindset Mastery Series https://amzn.to/47Nry1Z

10. Do the Impossible: How to Become Extraordinary and Impact the World at Scale (Becoming Extraordinary, Book 1) https://amzn.to/3Ghgrm7

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Python Programming for Data Analysis (Free PDF)

 

This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules.  The section on object-oriented programming explains features of the language that facilitate common programming patterns.

After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.

The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. 

To get the most out of this book, open a Python interpreter and type along with the many code samples.

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Dive into Deep Learning (Free PDF)

 

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required―every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.

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Mathematics for Machine Learning (Free PDF)

 

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) (Free PDF)

 

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.

This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.

Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Buy : Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

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Foundations of Data Science (Free PDF)

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

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Book Description

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

About the Author

Avrim Blum is Chief Academic Officer at Toyota Technical Institute at Chicago and formerly Professor at Carnegie Mellon University, Pennsylvania. He has over 25,000 citations for his work in algorithms and machine learning. He has received the AI Journal Classic Paper Award, ICML/COLT 10-Year Best Paper Award, Sloan Fellowship, NSF NYI award, and Herb Simon Teaching Award, and is a Fellow of the Association for Computing Machinery (ACM).

John Hopcroft is a member of the National Academy of Sciences and National Academy of Engineering, and a foreign member of the Chinese Academy of Sciences. He received the Turing Award in 1986, was appointed to the National Science Board in 1992 by President George H. W. Bush, and was presented with the Friendship Award by Premier Li Keqiang for his work in China.

Ravi Kannan is Principal Researcher for Microsoft Research, India. He was the recipient of the Fulkerson Prize in Discrete Mathematics (1991) and the Knuth Prize (ACM) in 2011. He is a distinguished alumnus of the Indian Institute of Technology, Bombay, and his past faculty appointments include Massachusetts Institute of Technology, Carnegie Mellon University, Pennsylvania, Yale University, Connecticut, and the Indian Institute of Science.

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Hands-On Data Analysis with NumPy and pandas (Free PDF)

 

Key Features

• Explore the tools you need to become a data analyst
• Discover practical examples to help you grasp data processing concepts
• Walk through hierarchical indexing and grouping for data analysis

Book Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning.

Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python's NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python's pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them.

By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.

What you will learn

• Understand how to install and manage Anaconda
• Read, sort, and map data using NumPy and pandas
• Find out how to create and slice data arrays using NumPy
• Discover how to subset your DataFrames using pandas
• Handle missing data in a pandas DataFrame
• Explore hierarchical indexing and plotting with pandas

Who This Book Is For

Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

Table of Contents

1. Setting Up a Python Data Analysis Environment
2. Diving into NumPY
3. Operations on NumPy Arrays
4. Pandas Are Fun! What Is pandas?
5. Arithmetic, Function Application and Mapping with pandas
6. Managing, Indexing, and Plotting

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Successful Algorithmic Trading halls-moore (PDF)

 

Introduction to the Book

1.1 Introduction to QuantStart

QuantStart was founded by Michael Halls-Moore, in 2010, to help junior quantitative analysts

(QAs) find jobs in the tough economic climate. Since then the site has evolved to become a

substantial resource for quantitative finance. The site now concentrates on algorithmic trading,

but also discusses quantitative development, in both Python and C++.

Since March 2010, QuantStart has helped over 200,000 visitors improve their quantitative

finance skills. You can always contact QuantStart by sending an email to mike@quantstart.com.

1.2 What is this Book?

Successful Algorithmic Trading has been written to teach retail discretionary traders and trading

professionals, with basic programming skills, how to create fully automated profitable and robust

algorithmic trading systems using the Python programming language. The book describes the

nature of an algorithmic trading system, how to obtain and organise financial data, the concept of backtesting and how to implement an execution system. The book is designed to be

extremely practical, with liberal examples of Python code throughout the book to demonstrate

the principles and practice of algorithmic trading.

1.3 Who is this Book For?

This book has been written for both retail traders and professional quants who have some basic

exposure to programming and wish to learn how to apply modern languages and libraries to

algorithmic trading. It is designed for those who enjoy self-study and can learn by example. The

book is aimed at individuals interested in actual programming and implementation, as I believe

that real success in algorithmic trading comes from fully understanding the implementation

details.

Professional quantitative traders will also find the content useful. Exposure to new libraries

and implementation methods may lead to more optimal execution or more accurate backtesting.

1.4 What are the Prerequisites?

The book is relatively self-contained, but does assume a familiarity with the basics of trading in

a discretionary setting. The book does not require an extensive programming background, but

basic familiarity with a programming language is assumed. You should be aware of elementary

programming concepts such as variable declaration, flow-control (if-else) and looping (for/while).

Some of the trading strategies make use of statistical machine learning techniques. In addition, the portfolio/strategy optimisation sections make extensive use of search and optimization.

PDF Link - successful algorithmic trading halls-moore

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Understanding Deep Learning (PDF Book)

 

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models

Short, focused chapters progress in complexity, easing students into difficult concepts

Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models

Streamlined presentation separates critical ideas from background context and extraneous detail

Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible

Programming exercises offered in accompanying Python Notebooks

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Understanding Machine Learning: From Theory to Algorithms (PDF Book)

 

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of 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; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Free PDF)

 

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

Use Scikit-learn to track an example ML project end to end

Explore several models, including support vector machines, decision trees, random forests, and ensemble methods

Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection

Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers

Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning 

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