python machine learning packt

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices, Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem. To explore the chess example further, let's think of visiting certain locations on the chess board as being associated with a positive eventâfor instance, removing an opponent's chess piece from the board or threatening the queen. In other words, criminals use social engineering to gain confidential information from people, by taking advantage of human behavior. We can think of those hyperparameters as parameters that are not learned from the data but represent the knobs of a model that we can turn to improve its performance. Anaconda is a free—including commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering into one user-friendly, cross-platform distribution. 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. For example: Similarly, we store the target variables (here, class labels) as a 150-dimensional column vector: In previous sections, we discussed the basic concepts of machine learning and the three different types of learning. 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 courseis invaluable. 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). 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. Learn more machine learning algorithms, NLP and recommendation systems. 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. 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. A popular example of reinforcement learning is a chess engine. 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." To refer to single elements in a vector or matrix, we will write the letters in italics ( or , respectively). 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. 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. 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. In the second half of the 20th century, machine learning evolved as a subfield of artificial intelligence (AI) involving self-learning algorithms that derive knowledge from data in order to make predictions. An important point that can be summarized from David Wolpert's famous No free lunch theorems is that we can't get learning "for free" (The Lack of A Priori Distinctions Between Learning Algorithms, D.H. Wolpert, 1996; No free lunch theorems for optimization, D.H. Wolpert and W.G. 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's have a look at the basic terminology that we will be using throughout this book. Useful features could be the color, the hue, the intensity of the flowers, the height, and the flower lengths and widths. One legitimate question to ask is this: how do we know which model performs well on the final test dataset and real-world data if we don't use this test dataset for the model selection, but keep it for the final model evaluation? Apply supervised and unsupervised techniques to build real-world apps, … We have an exciting journey ahead, covering many powerful techniques in the vast field of machine learning. 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). Raw data rarely comes in the form and shape that is necessary for the optimal performance of a learning algorithm. 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. 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. The following figure illustrates the concept of linear regression. Occasionally, we will make use of pandas, which is a library built on top of NumPy that provides additional higher-level data manipulation tools that make working with tabular data even more convenient. discounts and great free content. Python Machine Learning Blueprints. A supervised learning task with discrete class labels, such as in the previous email spam filtering example, is also called a classification task. Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. The letters ("A," "B," "C," and so on) will represent the different unordered categories or class labels that we want to predict. 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. 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. Often we are working with data of high dimensionalityâeach observation comes with a high number of measurementsâthat can present a challenge for limited storage space and the computational performance of machine learning algorithms. Second edition of the bestselling book on Machine Learning. 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 cover more advanced machine learning algorithms using the scikit-learn open source machine learning library. 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. Useful features could be the color, hue, and intensity of the flowers, or the height, length, and width of the flowers. You’ll also get tips on … 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. Intuitively, we can think of those hyperparameters as parameters that are not learned from the data but represent the knobs of a model that we can turn to improve its performance. In order to address the issue embedded in this question, different techniques summarized as "cross-validation" can be used. 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. Together with a basic introduction to the relevant terminology, we will lay the groundwork for successfully using machine learning techniques for practical problem solving. Clustering is a great technique for structuring information and deriving meaningful relationships from data. Machine learning is eating the software world, and now deep learning is extending machine learning. Use features like bookmarks, note taking and highlighting while reading Python Machine Learning: Unlock deeper insights into Machine … Python Machine Learning - by PACKT January 23, 2021 Machine Learning Ebook, Python ebooks, Python Machine Learning - by PACKT DOWNLOAD Like Fanpage and Read online bellow⏬ If you want to find out how to use Python … Finally, we set up our Python environment and installed and updated the required packages to get ready to see machine learning in action. It contains all the supporting project files necessary to work … The following figure illustrates how clustering can be applied to organizing unlabeled data into three distinct groups based on the similarity of their features, x1 and x2: Another subfield of unsupervised learning is dimensionality reduction. 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. Packt Publishing Limited. Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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. In the later chapters, when we focus on a subfield of machine learning called deep learning, we will use the latest version of the TensorFlow library, which specializes in training so-called deep neural network models very efficiently by utilizing graphics cards. 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. Unsupervised learning not only offers useful techniques for discovering structures in unlabeled data, but it can also be useful for data compression in feature preprocessing steps. 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. It covers a wide range of powerful Python libraries including scikit-learn, Theano, and Keras. 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. 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. Not only is machine learning becoming increasingly important in computer science research, but it also plays an ever greater role in our everyday lives. 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. The following figure illustrates the concept of linear regression. Machine learning is a particularly integration-heavy discipline, in the sense that any AI/machine learning system is going to need to ingest large amounts of data from real-world sources as training data, or system input, so Python… Galton described the biological phenomenon that the variance of height in a population does not increase over time. Galton described the biological phenomenon that the variance of height in a population does not increase over time. It contains all the supporting project files necessary to work through the book from start to … The Anaconda installer can be downloaded at http://continuum.io/downloads, and an Anaconda quick-start guide is available at https://conda.io/docs/test-drive.html. 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. 1. An important point that can be summarized from David Wolpert's famous No free lunch theorems is that we can't get learning "for free" (The Lack of A Priori Distinctions Between Learning Algorithms, D.H. Wolpert 1996; No free lunch theorems for optimization, D.H. Wolpert and W.G. Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast vectorized operations on multidimensional arrays. For instance, in chess the outcome of each move can be thought of as a different state of the environment. Every chapter has been critically updated, and there are new chapters on key technologies. However, the set of class labels does not have to be of a binary nature. Scikit-learn is an open source Python library of popular machine learning algorithms that will allow us to build these types of systems. 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. We use lowercase, bold-face letters to refer to vectors and uppercase, bold-face letters to refer to matrices . However, a general scheme is that the agent in reinforcement learning tries to maximize the reward through a series of interactions with the environment. A second type of supervised learning is the prediction of continuous outcomes, which is also called regression analysis. Understand and experiment with machine learning techniques using TensorFlow and get to grips with neural networks to conduct deep learning. 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. In unsupervised learning, however, we are dealing with unlabeled data or data of unknown structure. Equipped with the latest updates, this third edition of Python Machine Learning By Example, provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, … The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Given a predictor variable x and a response variable y, we fit a straight line to this data that minimizes the distanceâmost commonly the average squared distanceâbetween the sample points and the fitted line. Other positions, however, are associated with states that will more likely result in losing the game, such as losing a chess piece to the opponent in the following turn. Those class labels are discrete, unordered values that can be understood as the group memberships of the instances. Thoroughly updated using the latest Python open source libraries, this book offers the 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. Raw data rarely comes in the form and shape that is necessary for the optimal performance of a learning algorithm. In this section, we will take a look at the three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning … 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. 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. You have limited access to content. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. 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. A good summary of the differences between Python 3.5 and 2.7 can be found at https://wiki.python.org/moin/Python2orPython3. More information about pip can be found at https://docs.python.org/3/installing/index.html. Giving Computers the Ability to Learn from Data. Another subcategory of supervised learning is regression, where the outcome signal is a continuous value. A typical example of a multiclass classification task is handwritten character recognition. Since the information about the current state of the environment typically also includes a so-called reward signal, we can think of reinforcement learning as a field related to supervised learning. We will follow the common convention to represent each example as a separate row in a feature matrix, X, where each feature is stored as a separate column. 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. 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 samplesâthe performance measured on the test data may be overly optimistic otherwise. This will become much clearer in later chapters when we see actual examples. Some of the selected features may be highly correlated and therefore redundant to a certain degree. Reducing the dimensionality of our feature space has the advantage that less storage space is required, and the learning algorithm can run much faster. We can now use the intercept and slope learned from this data to predict the target variable of new data: Another type of machine learning is reinforcement learning. As you will see in later chapters, many different machine learning algorithms have been developed to solve different problem tasks. If there is a relationship between the time spent studying for the test and the final scores, we could use it as training data to learn a model that uses the study time to predict the test scores of future students who are planning to take this test. In this scenario, our dataset is two-dimensional, which means that each example has two values associated with it: x1 and x2. Hopefully soon, we will add safe and efficient self-driving cars to this list. Clustering is an exploratory data analysis technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships. For example, the opponent may sacrifice the queen but eventually win the game. Use Python libraries to mine, process data and make predictive models. Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast and vectorized operations on multidimensional arrays. The word 'Packt' and the Packt logo are registered trademarks belonging to 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. The following table depicts an excerpt of the Iris dataset, which is a classic example in the field of machine learning. Unsupervised dimensionality reduction is a commonly used approach in feature preprocessing to remove noise from data, which can also degrade the predictive performance of certain algorithms, and compress the data onto a smaller dimensional subspace while retaining most of the relevant information. We will adopt these conventions throughout this book. To refer to single elements in a vector or matrix, we write the letters in italics ( or , respectively). Training example: A row in a table representing the dataset and synonymous with an observation, record, instance, or sample (in most contexts, sample refers to a collection of training examples). Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Thanks to the many powerful open source libraries that have been developed in recent years, there has probably never been a better time to break into the machine learning field and learn how to utilize powerful algorithms to spot patterns in data and make predictions about future events. For your convenience, in the following list, you can find a selection of commonly used terms and their synonyms that you may find useful when reading this book and machine learning literature in general: In previous sections, we discussed the basic concepts of machine learning and the three different types of learning. This book is written for Python version 3.7 or higher, and it is recommended that you use the most recent version of Python 3 that is currently available. If you need more information, then … Sign up to our emails for regular updates, bespoke offers, exclusive Complete The Machine Learning Workshop to unlock your Packt … 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. Packt Publishing Limited. #####Code repository for Python Machine Learning, published by Packt Publishing. 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. 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. 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. 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/. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better. Here, the term "supervised" refers to a set of training examples (data inputs) where the desired output signals (labels) are already known. 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. Tags: Machine Learning, Packt Publishing, Python, Reinforcement Learning, Sebastian Raschka Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from … 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. 99 ( 10% off ) 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.