Python Machine Learning - Second Edition : Unlock Modern Machine Learning and Deep Learning Techniques with Python by Using the Latest Cutting-edge Open Source Python Libraries.

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 DescriptionMachine 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. 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 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 Table of Contents1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using Scikit-Learn 4. Building Good Training Sets - Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data - Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper - The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data using Recurrent Neural Networks Key Features A practical approach to the frameworks of data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningLearn 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. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka's Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. What you will learn Use the key frameworks of data science, machine learning, and deep learningAsk new questions of your data through machine learning models and neural networksWork with the most powerful Python open-source libraries in machine learningBuild deep learning applications using Keras and TensorFlowEmbed your machine learning model in accessible web applicationsPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringAnalyze images using deep learning techniquesUse sentiment analysis to delve deeper into textual and social media data About the Author Sebastian Raschka, author of the best selling Python Machine Learning, has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python. Sebastian loves to write and talk about data science, machine learning, and Python, and he's motivated to help people developing 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. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on nove BUnlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries./bh2About This Book/h2ulliSecond edition of the bestselling book on Machine Learning/liliA practical approach to key frameworks in data science, machine learning, and deep learning/liliUse the most powerful Python libraries to implement machine learning and deep learning/liliGet to know the best practices to improve and optimize your machine learning systems and algorithms/li/ulh2Who This Book Is For/h2If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.h2What You Will Learn/h2ulliUnderstand the key frameworks in data science, machine learning, and deep learning/liliHarness the power of the latest Python open source libraries in machine learning/liliExplore machine learning techniques using challenging real-world data/liliMaster deep neural network implementation using the TensorFlow library/liliLearn the mechanics of classification algorithms to implement the best tool for the job/liliPredict continuous target outcomes using regression analysis/liliUncover hidden patterns and structures in data with clustering/liliDelve deeper into textual and social media data using sentiment analysis/li/ulh2In Detail/h2Machine 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world.If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.h2Style and Approach/h2Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book • 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 Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. 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 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 In Detail 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python. Key Features A practical approach to the frameworks of data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Learn 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. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka’s Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world . Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. What you will learn Use the key frameworks of data science, machine learning, and deep learning Ask new questions of your data through machine learning models and neural networks Work with the most powerful Python open-source libraries in machine learning Build deep learning applications using Keras and TensorFlow Embed your machine learning model in accessible web applications Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Analyze images using deep learning techniques Use sentiment analysis to delve deeper into textual and social media data Unlock Modern Machine Learning And Deep Learning Techniques With Python By Using The Latest Cutting-edge Open Source Python Libraries. About This Book * 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 Who This Book Is For If You Know Some Python And You Want To Use Machine Learning And Deep Learning, Pick Up This Book. Whether You Want To Start From Scratch Or Extend Your Machine Learning Knowledge, This Is An Essential And Unmissable Resource. Written For Developers And Data Scientists Who Want To Create Practical Machine Learning And Deep Learning Code, This Book Is Ideal For Developers And Data Scientists Who Want To Teach Computers How To Learn From Data. 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 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 In Detail 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. --publisher's Description. 1. Giving Computers The Ability To Learn From Data -- 2. Training Simple Machine Learning Algorithms For Classification -- 3. A Tour Of Machine Learning Classifiers Using Scikit-learn -- 4. Building Good Training Sets-data Preprocessing -- 5. Compressing Data Via Dimensionality Reduction -- 6. Learning Best Practices For Model Evaluation And Hyperpaarmeter Tuning -- 7.combining Different Models For Ensemble Learning -- 8. Applying Machine Learning To Sentiment Analysis -- 9. Embedding A Machine Learning Model Into A Web Application -- 10. Predicting Continuous Target Variables With Regression Analysis -- 11. Working With Unlabeled Data-clustering Analysis -- 12. Implementing A Multilayer Artificial Neural Network From Scratch -- 13. Parallelizing Neural Network Training With Tensorflow -- 14. Going Deeper -- The Mechanics Of Tensorflow -- 15. Classifying Images With Deep Convolutional Neural Networks -- 16. Modeling Sequential Data Using Recurrent Neural Networks. Sebastian Raschka, Vahid Mirajalili. Includes Index. Cover Copyright Credits About the Authors About the Reviewers www.PacktPub.com Customer Feedback Table of Contents Preface Chapter 1: Giving Computers the Ability to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Making predictions about the future with supervised learning Classification for predicting class labels Regression for predicting continuous outcomes Solving interactive problems with reinforcement learning Discovering hidden structures with unsupervised learning. Finding subgroups with clusteringDimensionality reduction for data compression Introduction to the basic terminology and notations A roadmap for building machine learning systems Preprocessing -- getting data into shape Training and selecting a predictive model Evaluating models and predicting unseen data instances Using Python for machine learning Installing Python and packages from the Python Package Index Using the Anaconda Python distribution and package manager Packages for scientific computing, data science, and machine learning Summary. Chapter 2: Training Simple Machine Learning Algorithms for ClassificationArtificial neurons -- a brief glimpse into the early history of machine learning The formal definition of an artificial neuron The perceptron learning rule Implementing a perceptron learning algorithm in Python An object-oriented perceptron API Training a perceptron model on the Iris dataset Adaptive linear neurons and the convergence of learning Minimizing cost functions with gradient descent Implementing Adaline in Python Improving gradient descent through feature scaling. Large-scale machine learning and stochastic gradient descentSummary Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn Choosing a classification algorithm First steps with scikit-learn -- training a perceptron Modeling class probabilities via logistic regression Logistic regression intuition and conditional probabilities Learning the weights of the logistic cost function Converting an Adaline implementation into an algorithm for logistic regression Training a logistic regression model with scikit-learn Tackling overfitting via regularization. Maximum margin classification with support vector machinesMaximum margin intuition Dealing with a nonlinearly separable case using slack variables Alternative implementations in scikit-learn Solving nonlinear problems using a kernel SVM Kernel methods for linearly inseparable data Using the kernel trick to find separating hyperplanes in high-dimensional space Decision tree learning Maximizing information gain -- getting the most bang for your buck Building a decision tree Combining multiple decision trees via random forests K-nearest neighbors -- a lazy learning algorithm Summary. Chapter 4: Building Good Training Sets -- Data Preprocessing. "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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn."-- Résumé de l'éditeur