Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2nd Edition by Sebastian Raschka, Vahid Mirjalili is a PDF book for free download.
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.
Key Features
Second edition of the bestselling book on Machine Learning
A practical approach to key frameworks in data science, machine learning, and deep learning
Use the most powerful Python libraries to implement machine learning and deep learning
Get to know the best practices to improve and optimize your machine learning systems and algorithms
Book Description
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities.
If you've read the first edition of this book, you'll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
What you will learn
Understand the key frameworks in data science, machine learning, and deep learning
Harness the power of the latest Python open source libraries in machine learning
Explore machine learning techniques using challenging real-world data
Master deep neural network implementation using the TensorFlow 1.x library
Learn the mechanics of classification algorithms to implement the best tool for the job
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Delve deeper into textual and social media data using sentiment analysis
What's the key takeaway from your book?
That machine learning can be useful in almost every problem domain. I cover a lot of different subfields of machine learning in my book; by providing hands-on examples for each one of those topics, my hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications.
Also, using well-developed and maintained open source software makes machine learning very accessible to a broad audience of experienced programmers, as well as people who are new to programming.
And by introducing the basic mathematics behind machine learning, we can appreciate machine learning being more than just black box algorithms, giving readers an intuition of the capabilities but also limitations of machine learning, and how to apply those algorithms wisely.
What’s new & updated in this second edition of Python Machine Learning?
Oh, where should I start. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano.
Don’t worry - we didn't remove it - but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its release by Google in November 2015.
Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focusing on deep learning applications.
In a similar vein to the rest of the book, these new chapters not only provide readers with practical instructions and examples, but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works.
What makes this book stand out from other machine learning titles?
I certainly can't speak about all books on the market. However, since the first edition was released, I engaged in countless discussions with my readers, to help them with particular questions and to get their opinion on the parts they found unclear or topics they wish I had covered.
The connection between theory and praxis in particular was what readers found most helpful and somewhat lacking from other introductory texts (which, I heard, were either too theoretical or too practical). This constructive feedback has been invaluable for the second edition, helping me to focus on those parts that were still left unclear.
In a nutshell, the second edition of Python Machine Learning provides a healthy mix of theory and practical examples that most people found so helpful in the first edition, and the second edition adds on top of it with many refinements and additional topics based on the large corpus of invaluable reader feedback.
About the Author
Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python.
While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.
His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award.
In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.
Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University.
Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.
While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.
CONTENTS:
Chapter 1: Giving Computers the Ability to Learn from Data
Chapter 2: Training Simple Machine Learning Algorithms
Chapter 3: A Tour of Machine Learning Classifiers
Chapter 4: Building Good Training Sets – Data Preprocessing
Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Chapter 7: Combining Different Models for Ensemble Learning
Chapter 8: Applying Machine Learning to Sentiment Analysis
Chapter 9: Embedding a Machine Learning Model into a Web Application
Chapter 10: Predicting Continuous Target Variables with Regression Analysis
Chapter 11: Working with Unlabeled Data – Clustering Analysis
Chapter 12: Implementing a Multilayer Artificial Neural Network from Scratch
Chapter 13: Parallelizing Neural Network Training with TensorFlow
Chapter 14: Going Deeper – The Mechanics of TensorFlow
Chapter 15: Classifying Images with Deep Convolutional Neural Networks
Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
About The Book:
Publisher : Packt Publishing; 2nd edition (September 20, 2017)
Language : English
Pages : 622
File: PDF, 9MB
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