Practical Machine Learning

Description

About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully Apply the appropriate Machine learning technique to address a real-world problem Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application. The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies. Cover Copyright Credits Foreword About the Author Acknowledgments About the Reviewers www.PacktPub.com Preface Chapter 1: Introduction to Machine learning Machine learning Definition Core Concepts and Terminology What is learning? Data Labeled and unlabeled data Tasks Algorithms Models Data and inconsistencies in Machine learning Under-fitting Over-fitting Data instability Unpredictable data formats Practical Machine learning examples Types of learning problems Classification Clustering Forecasting, prediction or regression Simulation Optimization Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning Deep learning Performance measures Is the solution good? Mean squared error (MSE) Mean absolute error (MAE) Normalized MSE and MAE (NMSE and NMAE) Solving the errors: bias and variance Some complementing fields of Machine learning Data mining Artificial intelligence (AI) Statistical learning Data science Machine learning process lifecycle and solution architecture Machine learning algorithms Decision tree based algorithms Bayesian method based algorithms Kernel method based algorithms Clustering methods Artificial neural networks (ANN) Dimensionality reduction Ensemble methods Instance based learning algorithms Regression analysis based algorithms Association rule based learning algorithms Machine learning tools and frameworks Summary Chapter 2: Machine learning and Large-scale datasets Big data and the context of large-scale Machine learning Functional versus Structural – A methodological mismatch Commoditizing information Theoretical limitations of RDBMS Scaling-up versus Scaling-out storage Distributed and parallel computing strategies Machine learning: Scalability and Performance Too many data points or instances Too many attributes or features Shrinking response time windows – need for real-time responses Highly complex algorithm Feed forward, iterative prediction cycles Model selection process Potential issues in large-scale Machine learning Algorithms and Concurrency Developing concurrent algorithms Technology and implementation options for scaling-up Machine learning MapReduce programming paradigm High Performance Computing (HPC) with Message Passing Interface (MPI) Language Integrated Queries (LINQ) framework Manipulating datasets with LINQ Graphics Processing Unit (GPU) Field Programmable Gate Array (FPGA) Multicore or multiprocessor systems Summary Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem Introduction to Apache Hadoop Evolution of Hadoop (the platform of choice) Hadoop and its core elements Machine learning solution architecture for big data (employing Hadoop) The Data Source layer The Ingestion layer The Hadoop Storage layer The Hadoop (Physical) Infrastructure layer – supporting appliance Hadoop platform / Processing layer The Analytics layer The Consumption layer Explaining and exploring data with Visualizations Security and Monitoring layer Hadoop core components framework Writing to and reading from HDFS Handling failures HDFS command line RESTFul HDFS MapReduce MapReduce architecture What makes MapReduce cater to the needs of large datasets? MapReduce execution flow and components Developing MapReduce components Hadoop 2.x Hadoop ecosystem components Hadoop installation and setup Installing Jdk 1.7 Creating a system user for Hadoop (dedicated) Disable IPv6 Steps for installing Hadoop 2.6.0 Starting Hadoop Hadoop distributions and vendors Summary Chapter 4: Machine Learning Tools, Libraries, and Frameworks Machine learning tools – A landscape Apache Mahout How does Mahout work? Installing and setting up Apache Mahout Setting up Maven Setting-up Apache Mahout using Eclipse IDE Setting up Apache Mahout without Eclipse Mahout Packages Implementing vectors in Mahout R Installing and setting up R Integrating R with Apache Hadoop Approach 1 – Using R and Streaming APIs in Hadoop Approach 2 – Using the Rhipe package of R Approach 3 – Using RHadoop Summary of R/Hadoop integration approaches Implementing in R (using examples) Julia Installing and setting up Julia Downloading and using the command line version of Julia Using Juno IDE for running Julia Using Julia via the browser Running the Julia code from the command line Implementing in Julia (with examples) Using variables and assignments Numeric primitives Data structures Working with Strings and String manipulations Packages Interoperability Graphics and plotting Benefits of adopting Julia Integrating Julia and Hadoop Python Toolkit options in Python Implementation of Python (using examples) Installing Python and setting up scikit-learn Apache Spark Scala Programming with Resilient Distributed Datasets (RDD) Spring XD Summary Chapter 5: Decision Tree based learning Decision trees Terminology Purpose and uses Constructing a Decision tree Handling missing values Considerations for constructing Decision trees Decision trees in a graphical representation Inducing Decision trees – Decision tree algorithms Greedy Decision trees Benefits of Decision trees Specialized trees Oblique trees Random forests Evolutionary trees Hellinger trees Implementing Decision trees Using Mahout Using R Using Spark Using Python (scikit-learn) Using Julia Summary Chapter 6: Instance and Kernel Methods Based Learning Instance-based learning (IBL) Nearest Neighbors Value of k in KNN Distance measures in KNN Case-based reasoning (CBR) Locally weighed regression (LWR) Implementing KNN Using Mahout Using R Using Spark Using Python (scikit-learn) Using Julia Kernel methods-based learning Kernel functions Support Vector Machines (SVM) Inseparable Data Implementing SVM Using Mahout Using R Using Spark Using Python (Scikit-learn) Using Julia Summary Chapter 7: Association Rules based learning Association rules based learning Association rule – a definition Apriori algorithm Rule generation strategy FP-growth algorithm Apriori versus FP-growth Implementing Apriori and FP-growth Using Mahout Using R Using Spark Using Python (Scikit-learn) Using Julia Summary Chapter 8: Clustering based learning Clustering-based learning Types of clustering Hierarchical clustering Partitional clustering The k-means clustering algorithm Convergence or stopping criteria for the k-means clustering K-means clustering on disk Advantages of the k-means approach Disadvantages of the k-means algorithm Distance measures Complexity measures Implementing k-means clustering Using Mahout Using R Using Spark Using Python (scikit-learn) Using Julia Summary Chapter 9: Bayesian learning Bayesian learning Statistician's thinking Important terms and definitions Probability Types of probability Distribution Bernoulli distribution Binomial distribution Bayes' theorem Naïve Bayes classifier Multinomial Naïve Bayes classifier The Bernoulli Naïve Bayes classifier Implementing Naïve Bayes algorithm Using Mahout Using R Using Spark Using scikit-learn Using Julia Summary Chapter 10: Regression based learning Regression analysis Revisiting statistics Properties of expectation, variance, and covariance ANOVA and F Statistics Confounding Effect modification Regression methods Simple regression or simple linear regression Multiple regression Polynomial (non-linear) regression Generalized Linear Models (GLM) Logistic regression (logit link) Odds ratio in logistic regression Poisson regression Implementing linear and logistic regression Using Mahout Using R Using Spark Using scikit-learn Using Julia Summary Chapter 11: Deep learning Background The human brain Neural networks Neuron Synapses Artificial neurons or perceptrons Neural Network size Neural network types Backpropagation algorithm Softmax regression technique Deep learning taxonomy Convolutional neural networks (CNN/ConvNets) Convolutional layer (CONV) Pooling layer (POOL) Fully connected layer (FC) Recurrent Neural Networks (RNNs) Restricted Boltzmann Machines (RBMs) Deep Boltzmann Machines (DBMs) Autoencoders Implementing ANNs and Deep learning methods Using Mahout Using R Using Spark Using Python (Scikit-learn) Using Julia Summary Chapter 12: Reinforcement Learning Reinforcement Learning (RL) The context of Reinforcement Learning Examples of Reinforcement Learning Evaluative Feedback The Reinforcement Learning problem – the world grid example Markov Decision Process (MDP) Basic RL model – agent-environment interface Delayed rewards The policy Reinforcement Learning – key features Reinforcement learning solution methods Dynamic Programming (DP) Generalized Policy Iteration (GPI) Monte Carlo methods Temporal difference (TD) learning Sarsa - on-Policy TD Q-Learning – off-Policy TD Actor-critic methods (on-policy) R Learning (Off-policy) Summary Chapter 13: Ensemble learning Ensemble learning methods The wisdom of the crowd Key use cases Recommendation systems Anomaly detection Transfer learning Stream mining or classification Ensemble methods Supervised ensemble methods Unsupervised ensemble methods Implementing ensemble methods Using Mahout Using R Using Spark Using Python (Scikit-learn) Using Julia Summary Chapter 14: New generation data architectures for Machine learning Evolution of data architectures Emerging perspectives & drivers for new age data architectures Modern data architectures for Machine learning Semantic data architecture The business data lake Semantic Web technologies Vendors Multi-model database architecture / polyglot persistence Vendors Lambda Architecture (LA) Vendors Summary Index