Python Machine Learning : Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow 2, 3rd Edition

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features • Third edition of the bestselling, widely acclaimed Python machine learning book • Clear and intuitive explanations take you deep into the theory and practice of Python machine learning • Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn • Master the frameworks, models, and techniques that enable machines to 'learn' from data • Use scikit-learn for machine learning and TensorFlow for deep learning • Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more • Build and train neural networks, GANs, and other models • Discover best practices for evaluating and tuning models • Predict continuous target outcomes using regression analysis • Dig deeper into textual and social media data using sentiment analysis 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 resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data. Cover Copyright Packt Page Contributors 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 clustering Dimensionality reduction for data compression Introduction to the basic terminology and notations Notation and conventions used in this book Machine learning terminology 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 Classification Artificial 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 descent Summary 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 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 machines Maximum 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 a high-dimensional space Decision tree learning Maximizing IG – 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 Datasets – Data Preprocessing Dealing with missing data Identifying missing values in tabular data Eliminating training examples or features with missing values Imputing missing values Understanding the scikit-learn estimator API Handling categorical data Categorical data encoding with pandas Mapping ordinal features Encoding class labels Performing one-hot encoding on nominal features Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features L1 and L2 regularization as penalties against model complexity A geometric interpretation of L2 regularization Sparse solutions with L1 regularization Sequential feature selection algorithms Assessing feature importance with random forests Summary Chapter 5: Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis The main steps behind principal component analysis Extracting the principal components step by step Total and explained variance Feature transformation Principal component analysis in scikit-learn Supervised data compression via linear discriminant analysis Principal component analysis versus linear discriminant analysis The inner workings of linear discriminant analysis Computing the scatter matrices Selecting linear discriminants for the new feature subspace Projecting examples onto the new feature space LDA via scikit-learn Using kernel principal component analysis for nonlinear mappings Kernel functions and the kernel trick Implementing a kernel principal component analysis in Python Example 1 – separating half-moon shapes Example 2 – separating concentric circles Projecting new data points Kernel principal component analysis in scikit-learn Summary Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Loading the Breast Cancer Wisconsin dataset Combining transformers and estimators in a pipeline Using k-fold cross-validation to assess model performance The holdout method K-fold cross-validation Debugging algorithms with learning and validation curves Diagnosing bias and variance problems with learning curves Addressing over- and underfitting with validation curves Fine-tuning machine learning models via grid search Tuning hyperparameters via grid search Algorithm selection with nested cross-validation Looking at different performance evaluation metrics Reading a confusion matrix Optimizing the precision and recall of a classification model Plotting a receiver operating characteristic Scoring metrics for multiclass classification Dealing with class imbalance Summary Chapter 7: Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Implementing a simple majority vote classifier Using the majority voting principle to make predictions Evaluating and tuning the ensemble classifier Bagging – building an ensemble of classifiers from bootstrap samples Bagging in a nutshell Applying bagging to classify examples in the Wine dataset Leveraging weak learners via adaptive boosting How boosting works Applying AdaBoost using scikit-learn Summary Chapter 8: Applying Machine Learning to Sentiment Analysis Preparing the IMDb movie review data for text processing Obtaining the movie review dataset Preprocessing the movie dataset into a more convenient format Introducing the bag-of-words model Transforming words into feature vectors Assessing word relevancy via term frequency-inverse document frequency Cleaning text data Processing documents into tokens Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Topic modeling with Latent Dirichlet Allocation Decomposing text documents with LDA LDA with scikit-learn Summary Chapter 9: Embedding a Machine Learning Model into a Web Application Serializing fitted scikit-learn estimators Setting up an SQLite database for data storage Developing a web application with Flask Our first Flask web application Form validation and rendering Setting up the directory structure Implementing a macro using the Jinja2 templating engine Adding style via CSS Creating the result page Turning the movie review classifier into a web application Files and folders – looking at the directory tree Implementing the main application as app.py Setting up the review form Creating a results page template Deploying the web application to a public server Creating a PythonAnywhere account Uploading the movie classifier application Updating the movie classifier Summary Chapter 10: Predicting Continuous Target Variables with Regression Analysis Introducing linear regression Simple linear regression Multiple linear regression Exploring the Housing dataset Loading the Housing dataset into a data frame Visualizing the important characteristics of a dataset Looking at relationships using a correlation matrix Implementing an ordinary least squares linear regression model Solving regression for regression parameters with gradient descent Estimating the coefficient of a regression model via scikit-learn Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Adding polynomial terms using scikit-learn Modeling nonlinear relationships in the Housing dataset Dealing with nonlinear relationships using random forests Decision tree regression Random forest regression Summary Chapter 11: Working with Unlabeled Data – Clustering Analysis Grouping objects by similarity using k-means K-means clustering using scikit-learn A smarter way of placing the initial cluster centroids using k-means++ Hard versus soft clustering Using the elbow method to find the optimal number of clusters Quantifying the quality of clustering via silhouette plots Organizing clusters as a hierarchical tree Grouping clusters in bottom-up fashion Performing hierarchical clustering on a distance matrix Attaching dendrograms to a heat map Applying agglomerative clustering via scikit-learn Locating regions of high density via DBSCAN Summary Chapter 12: Implementing a Multilayer Artificial Neural Network from Scratch Modeling complex functions with artificial neural networks Single-layer neural network recap Introducing the multilayer neural network architecture Activating a neural network via forward propagation Classifying handwritten digits Obtaining and preparing the MNIST dataset Implementing a multilayer perceptron Training an artificial neural network Computing the logistic cost function Developing your understanding of backpropagation Training neural networks via backpropagation About the convergence in neural networks A few last words about the neural network implementation Summary Chapter 13: Parallelizing Neural Network Training with TensorFlow TensorFlow and training performance Performance challenges What is TensorFlow? How we will learn TensorFlow First steps with TensorFlow Installing TensorFlow Creating tensors in TensorFlow Manipulating the data type and shape of a tensor Applying mathematical operations to tensors Split, stack, and concatenate tensors Building input pipelines using tf.data – the TensorFlow Dataset API Creating a TensorFlow Dataset from existing tensors Combining two tensors into a joint dataset Shuffle, batch, and repeat Creating a dataset from files on your local storage disk Fetching available datasets from the tensorflow_datasets library Building an NN model in TensorFlow The TensorFlow Keras API (tf.keras) Building a linear regression model Model training via the .compile() and .fit() methods Building a multilayer perceptron for classifying flowers in the Iris dataset Evaluating the trained model on the test dataset Saving and reloading the trained model Choosing activation functions for multilayer neural networks Logistic function recap Estimating class probabilities in multiclass classification via the softmax function Broadening the output spectrum using a hyperbolic tangent Rectified linear unit activation Summary Chapter 14: Going Deeper – The Mechanics of TensorFlow The key features of TensorFlow TensorFlow's computation graphs: migrating to TensorFlow v2 Understanding computation graphs Creating a graph in TensorFlow v1.x Migrating a graph to TensorFlow v2 Loading input data into a model: TensorFlow v1.x style Loading input data into a model: TensorFlow v2 style Improving computational performance with function decorators TensorFlow Variable objects for storing and updating model parameters Computing gradients via automatic differentiation and GradientTape Computing the gradients of the loss with respect to trainable variables Computing gradients with respect to non-trainable tensors Keeping resources for multiple gradient computations Simplifying implementations of common architectures via the Keras API Solving an XOR classification problem Making model building more flexible with Keras' functional API Implementing models based on Keras' Model class Writing custom Keras layers TensorFlow Estimators Working with feature columns Machine learning with pre-made Estimators Using Estimators for MNIST handwritten digit classification Creating a custom Estimator from an existing Keras model Summary Chapter 15: Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Understanding CNNs and feature hierarchies Performing discrete convolutions Discrete convolutions in one dimension Padding inputs to control the size of the output feature maps Determining the size of the convolution output Performing a discrete convolution in 2D Subsampling layers Putting everything together – implementing a CNN Working with multiple input or color channels Regularizing an NN with dropout Loss functions for classification Implementing a deep CNN using TensorFlow The multilayer CNN architecture Loading and preprocessing the data Implementing a CNN using the TensorFlow Keras API Configuring CNN layers in Keras Constructing a CNN in Keras Gender classification from face images using a CNN Loading the CelebA dataset Image transformation and data augmentation Training a CNN gender classifier Summary Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data Modeling sequential data—order matters Representing sequences The different categories of sequence modeling RNNs for modeling sequences Understanding the RNN looping mechanism Computing activations in an RNN Hidden-recurrence versus output-recurrence The challenges of learning long-range interactions Long short-term memory cells Implementing RNNs for sequence modeling in TensorFlow Project one: predicting the sentiment of IMDb movie reviews Preparing the movie review data Embedding layers for sentence encoding Building an RNN model Building an RNN model for the sentiment analysis task Project two: character-level language modeling in TensorFlow Preprocessing the dataset Building a character-level RNN model Evaluation phase: generating new text passages Understanding language with the Transformer model Understanding the self-attention mechanism A basic version of self-attention Parameterizing the self-attention mechanism with query, key, and value weights Multi-head attention and the Transformer block Summary Chapter 17: Generative Adversarial Networks for Synthesizing New Data Introducing generative adversarial networks Starting with autoencoders Generative models for synthesizing new data Generating new samples with GANs Understanding the loss functions of the generator and discriminator networks in a GAN model Implementing a GAN from scratch Training GAN models on Google Colab Implementing the generator and the discriminator networks Defining the training dataset Training the GAN model Improving the quality of synthesized images using a convolutional and Wasserstein GAN Transposed convolution Batch normalization Implementing the generator and discriminator Dissimilarity measures between two distributions Using EM distance in practice for GANs Gradient penalty Implementing WGAN-GP to train the DCGAN model Mode collapse Other GAN applications Summary Chapter 18: Reinforcement Learning for Decision Making in Complex Environments Introduction: learning from experience Understanding reinforcement learning Defining the agent-environment interface of a reinforcement learning system The theoretical foundations of RL Markov decision processes The mathematical formulation of Markov decision processes Visualization of a Markov process Episodic versus continuing tasks RL terminology: return, policy, and value function The return Policy Value function Dynamic programming using the Bellman equation Reinforcement learning algorithms Dynamic programming Policy evaluation – predicting the value function with dynamic programming Improving the policy using the estimated value function Policy iteration Value iteration Reinforcement learning with Monte Carlo State-value function estimation using MC Action-value function estimation using MC Finding an optimal policy using MC control Policy improvement – computing the greedy policy from the action-value function Temporal difference learning TD prediction On-policy TD control (SARSA) Off-policy TD control (Q-learning) Implementing our first RL algorithm Introducing the OpenAI Gym toolkit Working with the existing environments in OpenAI Gym A grid world example Implementing the grid world environment in OpenAI Gym Solving the grid world problem with Q-learning Implementing the Q-learning algorithm A glance at deep Q-learning Training a DQN model according to the Q-learning algorithm Implementing a deep Q-learning algorithm Chapter and book summary Other Books You May Enjoy Index

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Key Features

• Third edition of the bestselling, widely acclaimed Python machine learning book
• Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

• Master the frameworks, models, and techniques that enable machines to 'learn' from data
• Use scikit-learn for machine learning and TensorFlow for deep learning
• Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
• Build and train neural networks, GANs, and other models
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

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 resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Master the frameworks, models, and techniques that enable machines to learn from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis 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 resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data. ]]> Explore the principles and practicalities of quantum computing Key Features Discover how quantum computing works and delve into the math behind it with this quantum computing textbook Learn how it may become the most important new computer technology of the century Explore the inner workings of quantum computing technology to quickly process complex cloud data and solve problems Book Description Quantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you. Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples. What you will learn See how quantum computing works, delve into the math behind it, what makes it different, and why it is so powerful with this quantum computing textbook Discover the complex, mind-bending mechanics that underpin quantum systems Understand the necessary concepts behind classical and quantum computing Refresh and extend your grasp of essential mathematics, computing, and quantum theory Explore the main applications of quantum computing to the fields of scientific computing, AI, and elsewhere Examine a detailed overview of qubits, quantum circuits, and quantum algorithm Who this book is for Dancing with Qubits is a quantum computing textbook for those who want to deeply explore the inner workings of quantum computing. This entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and comput.. Explore the principles and practicalities of quantum computingKey FeaturesDiscover how quantum computing works and delve into the math behind it with this quantum computing textbookLearn how it may become the most important new computer technology of the centuryExplore the inner workings of quantum computing technology to quickly process complex cloud data and solve problemsBook DescriptionQuantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you. Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.What you will learnSee how quantum computing works, delve into the math behind it, what makes it different, and why it is so powerful with this quantum computing textbookDiscover the complex, mind-bending mechanics that underpin quantum systemsUnderstand the necessary concepts behind classical and quantum computingRefresh and extend your grasp of essential mathematics, computing, and quantum theoryExplore the main applications of quantum computing to the fields of scientific computing, AI, and elsewhereExamine a detailed overview of qubits, quantum circuits, and quantum algorithmWho this book is forDancing with Qubits is a quantum computing textbook for those who want to deeply explore the inner workings of quantum computing. This entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science. Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key Features Learn from friendly, plain English explanations and practical activities Put ideas into action with 5 hands-on projects that show step-by-step how to build intelligent software Use AI to win classic video games and construct a virtual self-driving car Book Description Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learn Master the basics of AI without any previous experience Build fun projects, including a virtual-self-driving car and a robot warehouse worker Use AI to solve real-world business problems Learn how to code in Python Discover the 5 principles of reinforcement learning Create your own AI toolkit Who this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level) Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi... Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible. Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively. This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results. You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. "Python Machine Learning, Third Edition" is a comprehensive guide to machine learning and deep learning with Python. It acts as both a clear step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. This new third edition is updated for TensorFlow 2 and the latest additions to scikit-learn. It's expanded to cover two cutting-edge machine learning techniques: reinforcement learning and generative adversarial networks (GANs). -- From the rear cover