A practical approach to key frameworks in data science, machine learning, and deep learning. If we are satisfied with its performance, we can now use this model to predict new, future data. The word 'Packt' and the Packt logo are registered trademarks belonging to Often we are working with data of high dimensionality—each observation comes with a high number of measurements—that can present a challenge for limited storage space and the computational performance of machine learning algorithms. In the following chapters, we will use a matrix and vector notation to refer to our data. A popular example of reinforcement learning is a chess engine. Another milestone was recently achieved by researchers at DeepMind, who used deep learning to predict 3D protein structures, outperforming physics-based approaches for the first time (https://deepmind.com/blog/alphafold/). Raw data rarely comes in the form and shape that is necessary for the optimal performance of a learning algorithm. We briefly went over the typical roadmap for applying machine learning to problem tasks, which we will use as a foundation for deeper discussions and hands-on examples in the following chapters. Reinforcement learning is concerned with learning to choose a series of actions that maximizes the total reward, which could be earned either immediately after taking an action or via delayed feedback. discounts and great free content. Using unsupervised learning techniques, we are able to explore the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function. All rights reserved, Access this book, plus 7,500 other titles for, Get all the quality content you’ll ever need to stay ahead with a Packt subscription – access over 7,500 online books and videos on everything in tech, Giving Computers the Ability to Learn from Data, Building intelligent machines to transform data into knowledge, The three different types of machine learning, Introduction to the basic terminology and notations, A roadmap for building machine learning systems, Training Simple Machine Learning Algorithms for Classification, Artificial neurons – a brief glimpse into the early history of machine learning, Implementing a perceptron learning algorithm in Python, Adaptive linear neurons and the convergence of learning, A Tour of Machine Learning Classifiers Using scikit-learn, First steps with scikit-learn – training a perceptron, Modeling class probabilities via logistic regression, Maximum margin classification with support vector machines, Solving nonlinear problems using a kernel SVM, K-nearest neighbors – a lazy learning algorithm, Building Good Training Sets – Data Preprocessing, Partitioning a dataset into separate training and test sets, Assessing feature importance with random forests, Compressing Data via Dimensionality Reduction, Unsupervised dimensionality reduction via principal component analysis, Supervised data compression via linear discriminant analysis, Using kernel principal component analysis for nonlinear mappings, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, Using k-fold cross-validation to assess model performance, Debugging algorithms with learning and validation curves, Fine-tuning machine learning models via grid search, Looking at different performance evaluation metrics, Combining Different Models for Ensemble Learning, Bagging – building an ensemble of classifiers from bootstrap samples, Leveraging weak learners via adaptive boosting, Applying Machine Learning to Sentiment Analysis, Preparing the IMDb movie review data for text processing, 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, Embedding a Machine Learning Model into a Web Application, Serializing fitted scikit-learn estimators, Setting up an SQLite database for data storage, Turning the movie review classifier into a web application, Deploying the web application to a public server, Predicting Continuous Target Variables with Regression Analysis, Implementing an ordinary least squares linear regression model, Fitting a robust regression model using RANSAC, Evaluating the performance of linear regression models, Turning a linear regression model into a curve – polynomial regression, Dealing with nonlinear relationships using random forests, Working with Unlabeled Data – Clustering Analysis, Grouping objects by similarity using k-means, Organizing clusters as a hierarchical tree, Locating regions of high density via DBSCAN, Implementing a Multilayer Artificial Neural Network from Scratch, Modeling complex functions with artificial neural networks, A few last words about the neural network implementation, Parallelizing Neural Network Training with TensorFlow, Training neural networks efficiently with high-level TensorFlow APIs, Choosing activation functions for multilayer networks, Going Deeper – The Mechanics of TensorFlow, Understanding TensorFlow's computation graphs, Executing objects in a TensorFlow graph using their names, Saving and restoring a model in TensorFlow, Transforming Tensors as multidimensional data arrays, Utilizing control flow mechanics in building graphs, Classifying Images with Deep Convolutional Neural Networks, Building blocks of convolutional neural networks, Putting everything together to build a CNN, Implementing a deep convolutional neural network using TensorFlow, Modeling Sequential Data Using Recurrent Neural Networks, Implementing a multilayer RNN for sequence modeling in TensorFlow, Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs, Project two – implementing an RNN for character-level language modeling in TensorFlow, https://wiki.python.org/moin/Python2orPython3, https://docs.python.org/3/installing/index.html, Unlock the full Packt library with a FREE trial, Instant online access to over 7,500+ books and videos, Constantly updated with 100+ new titles each month, Breadth and depth in over 1,000+ technologies. He observed that the height of parents is not passed on to their children, but instead, their children's height regresses toward the population mean. We have an exciting journey ahead, covering many powerful techniques in the vast field of machine learning. However, a general scheme is that the agent in reinforcement learning tries to maximize the reward by a series of interactions with the environment. We use the training dataset to train and optimize our machine learning model, while we keep the test dataset until the very end to evaluate the final model. But before we can compare different models, we first have to decide upon a metric to measure performance. Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Instead of requiring humans to manually derive rules and build models from analyzing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models and make data-driven decisions. The Anaconda installer can be downloaded at https://docs.anaconda.com/anaconda/install/, and an Anaconda quick start guide is available at https://docs.anaconda.com/anaconda/user-guide/getting-started/. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world. In addition, the final reward will also depend on how the opponent plays. Reducing the dimensionality of our feature space has the advantage that less storage space is required, and the learning algorithm can run much faster. Here, the agent decides upon a series of moves depending on the state of the board (the environment), and the reward can be defined as win or lose at the end of the game: There are many different subtypes of reinforcement learning. Finally, we set up our Python environment and installed and updated the required packages to get ready to see machine learning in action. After we have successfully installed Python, we can execute pip from the terminal to install additional Python packages: Already installed packages can be updated via the --upgrade flag: A highly recommended alternative Python distribution for scientific computing is Anaconda by Continuum Analytics. Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Here, the agent decides upon a series of moves depending on the state of the board (the environment), and the reward can be defined as win or lose at the end of the game: There are many different subtypes of reinforcement learning. After we have selected a model that has been fitted on the training dataset, we can use the test dataset to estimate how well it performs on this unseen data to estimate the generalization error. Together with a basic introduction to the relevant terminology, we will lay the groundwork for successfully using machine learning techniques for practical problem solving. Python is one of the most popular programming languages for data science and therefore enjoys a large number of useful add-on libraries developed by its great developer and open-source community. Useful features could be the color, hue, and intensity of the flowers, or the height, length, and width of the flowers. 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. As it happens, many terms and concepts have been rediscovered or redefined and may already be familiar to you but appear under different names. In other words, criminals use social engineering to gain confidential information from people, by taking advantage of human behavior. In this chapter, we will cover the following topics: The three types of learning and basic terminology, The building blocks for successfully designing machine learning systems, Installing and setting up Python for data analysis and machine learning. It is important to note that the parameters for the previously mentioned procedures, such as feature scaling and dimensionality reduction, are solely obtained from the training dataset, and the same parameters are later reapplied to transform the test dataset, as well as any new data instances—the performance measured on the test data may be overly optimistic otherwise. You have limited access to content. Artificial Intelligence with Python. Tags: Machine Learning, Packt Publishing, Python, Reinforcement Learning, Sebastian Raschka Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from … Given a feature variable, x, and a target variable, y, we fit a straight line to this data that minimizes the distance—most commonly the average squared distance—between the data points and the fitted line. We are living in an age where data comes in abundance; using self-learning algorithms from the field of machine learning, we can turn this data into knowledge. A second type of supervised learning is the prediction of continuous outcomes, which is also called regression analysis. While we will cover classification algorithms quite extensively throughout the book, we will also explore different techniques for regression analysis and clustering. As we will see in later chapters, many different machine learning algorithms have been developed to solve different problem tasks. Code repositories for the 1st and 2nd edition are available at. For example, each classification algorithm has its inherent biases, and no single classification model enjoys superiority if we don't make any assumptions about the task. The following table depicts an excerpt of the Iris dataset, which is a classic example in the field of machine learning. Finally, we also cannot expect that the default parameters of the different learning algorithms provided by software libraries are optimal for our specific problem task. discounts and great free content. In unsupervised learning, however, we are dealing with unlabeled data or data of unknown structure. In my opinion, machine learning, the application and science of algorithms that make sense of data, is the most exciting field of all the computer sciences! The following diagram shows a typical workflow for using machine learning in predictive modeling, which we will discuss in the following subsections: Let's begin with discussing the roadmap for building machine learning systems. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology. Later in this book, in addition to machine learning itself, we will introduce different techniques to preprocess a dataset, which will help you to get the best performance out of different machine learning algorithms. Another subcategory of supervised learning is regression, where the outcome signal is a continuous value: Classification is a subcategory of supervised learning where the goal is to predict the categorical class labels of new instances, based on past observations. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Get up and running with programming machine learning algorithms by leveraging scikit-learn's flexibility to build the ... basic Python syntax, how to develop software in python, how to work in a team, and an introduction to data science and machine learning with Python. Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch, Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn. Eventually, we set up our Python environment and installed and updated the required packages to get ready to see machine learning in action. The following figure illustrates the concept of a binary classification task given 30 training examples; 15 training examples are labeled as the negative class (minus signs) and 15 training examples are labeled as the positive class (plus signs). To augment our learning experience and visualize quantitative data, which is often extremely useful to intuitively make sense of it, we will use the very customizable Matplotlib library. Building Machine Learning Systems with Python Master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide ... been a technical reviewer for the following Packt Publishing books: Python 3 Object Oriented Programming, Python 2.6 Graphics Cookbook, and Python … It covers a wide range of powerful Python libraries including scikit-learn, Theano, and Keras. To refer to single elements in a vector or matrix, we write the letters in italics ( or , respectively). After we have selected a model that has been fitted on the training dataset, we can use the test dataset to estimate how well it performs on this unseen data to estimate the so-called generalization error. Every chapter has been critically updated, and there are new chapters on key technologies. Python Machine Learning: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics - Kindle edition by Raschka, Sebastian. Considering the example of email spam filtering, we can train a model using a supervised machine learning algorithm on a corpus of labeled emails, emails that are correctly marked as spam or not-spam, to predict whether a new email belongs to either of the two categories. Not only is machine learning becoming increasingly important in computer science research, but it is also playing an ever-greater role in our everyday lives. Understand regression analysis, core machine learning techniques and take your first steps with deep learning with Tensorflow. Paperback: 622 pages; ebook available in Kindle format, Epub, PDF. In this scenario, our dataset is two-dimensional, which means that each example has two values associated with it: x1 and x2. In regression analysis, we are given a number of predictor (explanatory) variables and a continuous response variable (outcome), and we try to find a relationship between those variables that allows us to predict an outcome. Tags: Data Analysis, Free ebook, Machine Learning, Packt Publishing, Python Two free ebooks: "Building Machine Learning Systems with Python" and "Practical Data Analysis" will give your skills a boost and … It is important to note that the parameters for the previously mentioned procedures, such as feature scaling and dimensionality reduction, are solely obtained from the training dataset, and the same parameters are later reapplied to transform the test dataset, as well as any new data samples—the performance measured on the test data may be overly optimistic otherwise. To determine whether our machine learning algorithm not only performs well on the training set but also generalizes well to new data, we also want to randomly divide the dataset into a separate training and test set. Therefore, we will make frequent use of hyperparameter optimization techniques that help us to fine-tune the performance of our model in later chapters. 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. Useful features could be the color, the hue, the intensity of the flowers, the height, and the flower lengths and widths. For instance, in chess, the outcome of each move can be thought of as a different state of the environment. Python is available for all three major operating systems—Microsoft Windows, macOS, and Linux—and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org. After successfully installing Anaconda, we can install new Python packages using the following command: Existing packages can be updated using the following command: Throughout this book, we will mainly use NumPy's multidimensional arrays to store and manipulate data. Reducing the dimensionality of our feature space has the advantage that less storage space is required, and the learning algorithm can run much faster. It acts as both a step-by-step tutorial, and a reference you'll keep … For example, it allows marketers to discover customer groups based on their interests, in order to develop distinct marketing programs. In the following chapter, we will start this journey by implementing one of the earliest machine learning algorithms for classification, which will prepare us for Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn, where we will cover more advanced machine learning algorithms using the scikit-learn open source machine learning library. The Iris dataset contains the measurements of 150 Iris flowers from three different species—Setosa, Versicolor, and Virginica. Machine learning is eating the software world, and now deep learning is extending machine learning. Each state can be associated with a positive or negative reward, and a reward can be defined as accomplishing an overall goal, such as winning or losing a game of chess. In supervised learning, we know the right answer beforehand when we train our model, and in reinforcement learning, we define a measure of reward for particular actions by the agent. Other positions, however, are associated with a negative event, such as losing a chess piece to the opponent in the following turn. However, our machine learning system would be unable to correctly recognize any of the digits zero to nine, for example, if they were not part of our training dataset. 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. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition software, reliable web search engines, and challenging chess-playing programs. Many machine learning algorithms also require that the selected features are on the same scale for optimal performance, which is often achieved by transforming the features in the range [0, 1] or a standard normal distribution with zero mean and unit variance, as we will see in later chapters. To explore the chess example further, let's think of visiting certain locations on the chess board as being associated with a positive event—for instance, removing an opponent's chess piece from the board or threatening the queen. Now, we can use a supervised machine learning algorithm to learn a rule—the decision boundary represented as a dashed line—that can separate those two classes and classify new data into each of those two categories given its x1 and x2 values: However, the set of class labels does not have to be of a binary nature. As you will see in later chapters, many different machine learning algorithms have been developed to solve different problem tasks. 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. In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions with the environment. The "Python Machine Learning (2nd edition)" book code repository and info resource - tlalarus/python-machine-learning-book-2nd-edition Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Those class labels are discrete, unordered values that can be understood as the group memberships of the instances. The following figure illustrates the concept of linear regression. In certain cases, dimensionality reduction can also improve the predictive performance of a model if the dataset contains a large number of irrelevant features (or noise), that is, if the dataset has a low signal-to-noise ratio. In this section, we will discuss the other important parts of a machine learning system accompanying the learning algorithm. Packt Publishing Limited. In practice, it is therefore essential to compare at least a handful of different algorithms in order to train and select the best performing model. However, the set of class labels does not have to be of a binary nature. We have an exciting journey ahead, covering many powerful techniques in the vast field of 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. While this section provides a basic overview of reinforcement learning, please note that applications of reinforcement learning are beyond the scope of this book, which primarily focusses on classification, regression analysis, and clustering. We will learn about the fundamental differences between the three different learning types and, using conceptual examples, we will develop an understanding of the practical problem domains where they can be applied: The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. We can relate this concept to the popular saying, "I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail" (Abraham Maslow, 1966). The following figure shows an example where nonlinear dimensionality reduction was applied to compress a 3D Swiss Roll onto a new 2D feature subspace: Now that we have discussed the three broad categories of machine learning—supervised, unsupervised, and reinforcement learning—let us have a look at the basic terminology that we will be using throughout the book. In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. While classification models allow us to categorize objects into known classes, we can use regression analysis to predict the continuous outcomes of target variables. 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. Please make sure that the version numbers of your installed packages are equal to, or greater than, those version numbers to ensure the code examples run correctly: In this chapter, we explored machine learning at a very high level and familiarized ourselves with the big picture and major concepts that we are going to explore in the following chapters in more detail. However, our machine learning system will be unable to correctly recognize any of the digits between 0 and 9, for example, if they were not part of the training dataset. Here, each flower example represents one row in our dataset, and the flower measurements in centimeters are stored as columns, which we also call the features of the dataset: To keep the notation and implementation simple yet efficient, we will make use of some of the basics of linear algebra. The additional packages that we will be using throughout this book can be installed via the pip installer program, which has been part of the Python Standard Library since Python 3.3. 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. This book is written for Python version 3.5.2 or higher, and it is recommended you use the most recent version of Python 3 that is currently available, although most of the code examples may also be compatible with Python 2.7.13 or higher. Packt Publishing Ltd. (September 20th, 2017) From the back cover: Machine learning is eating the software world, and now deep learning … Note that in the field of machine learning, the predictor variables are commonly called "features," and the response variables are usually referred to as "target variables." A second type of supervised learning is the prediction of continuous outcomes, which is also called regression analysis. This bundle was live from Dec 30, 2019 to Jan 20, 2020 with 22,427 bundles sold, leading to $323,449.20 raised. To determine whether our machine learning algorithm not only performs well on the training dataset but also generalizes well to new data, we also want to randomly divide the dataset into a separate training and test dataset. Packt Publishing Limited. In supervised learning, we know the right answer beforehand when we train a model, and in reinforcement learning, we define a measure of reward for particular actions carried out by the agent. The Iris dataset consisting of 150 samples and four features can then be written as a matrix : For the rest of this book, unless noted otherwise, we will use the superscript i to refer to the ith training sample, and the subscript j to refer to the jth dimension of the training dataset. 1. Clustering is a great technique for structuring information and deriving meaningful relationships from data. We are living in an age where data comes in abundance; using self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Clustering is a great technique for structuring information and deriving meaningful relationships from data. Read Packt’s extensive Learning Path Python: Beginner’s Guide to Artificial Intelligence. Through its interaction with the environment, an agent can then use reinforcement learning to learn a series of actions that maximizes this reward via an exploratory trial-and-error approach or deliberative planning. A supervised learning task with discrete class labels, such as in the previous email spam filtering example, is also called a classification task. In regression analysis, we are given a number of predictor (explanatory) variables and a continuous response variable (outcome or target), and we try to find a relationship between those variables that allows us to predict an outcome. In order to address the issue embedded in this question, different techniques summarized as "cross-validation" can be used. Build real-world applications with Python centered on … Download it once and read it on your Kindle device, PC, phones or tablets. The version numbers of the major Python packages that were used for writing this book are mentioned in the following list. All rights reserved, Access this book, plus 7,500 other titles for, Get all the quality content you’ll ever need to stay ahead with a Packt subscription – access over 7,500 online books and videos on everything in tech, https://www.nature.com/articles/nature21056, https://docs.python.org/3/installing/index.html, https://docs.anaconda.com/anaconda/install/, https://docs.anaconda.com/anaconda/user-guide/getting-started/, Giving Computers the Ability to Learn from Data, Building intelligent machines to transform data into knowledge, The three different types of machine learning, Introduction to the basic terminology and notations, A roadmap for building machine learning systems, Training Simple Machine Learning Algorithms for Classification, Artificial neurons – a brief glimpse into the early history of machine learning, Implementing a perceptron learning algorithm in Python, Adaptive linear neurons and the convergence of learning, A Tour of Machine Learning Classifiers Using scikit-learn, First steps with scikit-learn – training a perceptron, Modeling class probabilities via logistic regression, Maximum margin classification with support vector machines, Solving nonlinear problems using a kernel SVM, K-nearest neighbors – a lazy learning algorithm, Building Good Training Datasets – Data Preprocessing, Partitioning a dataset into separate training and test datasets, Assessing feature importance with random forests, Compressing Data via Dimensionality Reduction, Unsupervised dimensionality reduction via principal component analysis, Supervised data compression via linear discriminant analysis, Using kernel principal component analysis for nonlinear mappings, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, Using k-fold cross-validation to assess model performance, Debugging algorithms with learning and validation curves, Fine-tuning machine learning models via grid search, Looking at different performance evaluation metrics, Combining Different Models for Ensemble Learning, Bagging – building an ensemble of classifiers from bootstrap samples, Leveraging weak learners via adaptive boosting, Applying Machine Learning to Sentiment Analysis, Preparing the IMDb movie review data for text processing, 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, Embedding a Machine Learning Model into a Web Application, Serializing fitted scikit-learn estimators, Setting up an SQLite database for data storage, Turning the movie review classifier into a web application, Deploying the web application to a public server, Predicting Continuous Target Variables with Regression Analysis, Implementing an ordinary least squares linear regression model, Fitting a robust regression model using RANSAC, Evaluating the performance of linear regression models, Turning a linear regression model into a curve – polynomial regression, Dealing with nonlinear relationships using random forests, Working with Unlabeled Data – Clustering Analysis, Grouping objects by similarity using k-means, Organizing clusters as a hierarchical tree, Locating regions of high density via DBSCAN, Implementing a Multilayer Artificial Neural Network from Scratch, Modeling complex functions with artificial neural networks, A few last words about the neural network implementation, Parallelizing Neural Network Training with TensorFlow, Building input pipelines using tf.data – the TensorFlow Dataset API, Choosing activation functions for multilayer neural networks, Going Deeper – The Mechanics of TensorFlow, TensorFlow's computation graphs: migrating to TensorFlow v2, TensorFlow Variable objects for storing and updating model parameters, Computing gradients via automatic differentiation and GradientTape, Simplifying implementations of common architectures via the Keras API, Classifying Images with Deep Convolutional Neural Networks, Putting everything together – implementing a CNN, Gender classification from face images using a CNN, Modeling Sequential Data Using Recurrent Neural Networks, Implementing RNNs for sequence modeling in TensorFlow, Understanding language with the Transformer model, Generative Adversarial Networks for Synthesizing New Data, Introducing generative adversarial networks, Improving the quality of synthesized images using a convolutional and Wasserstein GAN, Reinforcement Learning for Decision Making in Complex Environments, Leave a review - let other readers know what you think, Unlock the full Packt library with a FREE trial, Instant online access to over 7,500+ books and videos, Constantly updated with 100+ new titles each month, Breadth and depth in over 1,000+ technologies, The three types of learning and basic terminology, The building blocks for successfully designing machine learning systems, Installing and setting up Python for data analysis and machine learning.

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