This page contains links and information about Library resources for Machine Learning, including:
Machine Learning.
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Module 1: Java for Data Science -- Chapter 1: Getting Started with Data Science -- Problems solved using data science -- Understanding the data science problem â#x80;#x93; Â solving approach -- Using Java to support data science -- Acquiring data for an application -- The importance and process of cleaning data -- Visualizing data to enhance understanding -- The use of statistical methods in data science -- Machine learning applied to data science
Using neural networks in data scienceDeep learning approaches -- Performing text analysis -- Visual and audio analysis -- Improving application performance using parallel techniques -- Assembling the pieces -- Summary -- Chapter 2: Data Acquisition -- Understanding the data formats used in data science applications -- Overview of CSV data -- Overview of spreadsheets -- Overview of databases -- Overview of PDF files -- Overview of JSON -- Overview of XML -- Overview of streaming data -- Overview of audio/video/images in Java
Data acquisition techniquesUsing the HttpUrlConnection class -- Web crawlers in Java -- Creating your own web crawler -- Using the crawler4j web crawler -- Web scraping in Java -- Using API calls to access common social media sites -- Using OAuth to authenticate users -- Handing Twitter -- Handling Wikipedia -- Handling Flickr -- Handling YouTube -- Searching by keyword -- Summary -- Chapter 3: Data Cleaning -- Handling data formats -- Handling CSV data -- Handling spreadsheets -- Handling Excel spreadsheets -- Handling PDF files -- Handling JSON
Using JSON streaming APIUsing the JSON tree API -- The nitty gritty of cleaning text -- Using Java tokenizers to extract words -- Java core tokenizers -- Third-party tokenizers and libraries -- Transforming data into a usable form -- Simple text cleaning -- Removing stop words -- Finding words in text -- Finding and replacing text -- Data imputation -- Subsetting data -- Sorting text -- Data validation -- Validating data types -- Validating dates -- Validating e-mail addresses -- Validating ZIP codes -- Validating names -- Cleaning images
Changing the contrast of an imageSmoothing an image -- Brightening an image -- Resizing an image -- Converting images to different formats -- Summary -- Chapter 4: Data Visualization -- Understanding plots and graphs -- Visual analysis goals -- Creating index charts -- Creating bar charts -- Using country as the category -- Using decade as the category -- Creating stacked graphs -- Creating pie charts -- Creating scatter charts -- Creating histograms -- Creating donut charts -- Creating bubble charts -- Summary
Machine Learning
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Summary : Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.
Machine Learning
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This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets
Practical Machine Learning
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ackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout 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 HadoopWho This Book Is ForThis book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges.
Machine Learning :Machine learning : a Bayesian and Optimization Perspective
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Feature Engineering for Machine Learning and Data Analytics
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Active Learning
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The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks."We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
Introduction to Machine Learning with Applications in Information Security
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Deep Learning Essentials : Your hands-on guide to the fundamentals of deep learning and neural network modeling.
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Deep Learning is one of the trending topics in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps when it comes to training efficient deep learning models, and apply them in various practical scenarios.
Machine Learning Algorithms for Problem Solving in Computational Applications
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This book addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems
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This book analyzes and compares the existing and most effective algorithms for mining through logical rules and shows how these approaches use shared concepts for mining logical rules, including item, item set, transaction, frequent itemset, maximal itemset, generator (non-redundant or irredundant itemset), closed itemset, support, and confidence
Machine Learning Techniques for Adaptive Multimedia Retrieval
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This book disseminates current information on multimedia retrieval, advancing the field of multimedia databases, and educating the multimedia database community on machine learning techniques for adaptive multimedia retrieval research, design and applications
Building Probabilistic Graphical Models with Python
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Network Anomaly Detection
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With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective
Machine Learning with R
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Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R
R Machine Learning by Example
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Get to grips with the concepts of machine learning through exciting real-world examples. Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning. Learn to build your own machine learning system with this example-based practical guide. Who This Book Is For. If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is the go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge of machine learning would be helpful but is not necessary. What You Will Learn. Utilize the power of R to handle data extraction, manipulation, and exploration techniques. Use R to visualize data spread across multiple dimensions and extract useful features. Explore the underlying mathematical and logical concepts that drive machine learning algorithms.
Python Machine Learning : Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
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Giving computers the ability to learn from data -- Training simple machine learning algorithms for classification -- A tour of machine learning classifiers using scikit-learn -- Building good training sets - data preprocessing -- Compressing data via dimensionality reduction -- Learning best practices for model evaluation and hyperparameter tuning -- Combining different models for ensemble learning -- Applying machine learning to sentiment analysis -- Embedding a machine learning model into a web application -- Predicting continuous target variables with regression analysis -- Working with unlabeled data - clustering analysis -- Implementing a multilayer artificial neural network from scratch -- Parallelizing neural network training and TensorFlow -- Going deeper - the mechanics of TensorFlow -- Classifying images with deep convolutional neural networks -- Modeling sequential data using recurrent neural networks.
TensorFlow Machine Learning Cookbook
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Getting Started with TensorFlow; Introduction; How TensorFlow Works; Declaring Tensors; Using Placeholders and Variables; Working with Matrices; Declaring Operations; Implementing Activation Functions; Working with Data Sources; Additional Resources; Chapter 2: The TensorFlow Way; Introduction; Operations in a Computational Graph; Layering Nested Operations; Working with Multiple Layers; Implementing Loss Functions; Implementing Back Propagation
Working with Batch and Stochastic TrainingCombining Everything Together; Evaluating Models; Chapter 3: Linear Regression; Introduction; Using the Matrix Inverse Method; Implementing a Decomposition Method; Learning The TensorFlow Way of Linear Regression; Understanding Loss Functions in Linear Regression; Implementing Deming regression; Implementing Lasso and Ridge Regression; Implementing Elastic Net Regression; Implementing Logistic Regression; Chapter 4: Support Vector Machines; Introduction; Working with a Linear SVM; Reduction to Linear Regression; Working with Kernels in TensorFlow
Implementing a Non-Linear SVMImplementing a Multi-Class SVM; Chapter 5: Nearest Neighbor Methods; Introduction; Working with Nearest Neighbors; Working with Text-Based Distances; Computing with Mixed Distance Functions; Using an Address Matching Example; Using Nearest Neighbors for Image Recognition; Chapter 6: Neural Networks; Introduction; Implementing Operational Gates; Working with Gates and Activation Functions; Implementing a One-Layer Neural Network; Implementing Different Layers; Using a Multilayer Neural Network; Improving the Predictions of Linear Models
Learning to Play Tic Tac ToeChapter 7: Natural Language Processing; Introduction; Working with bag of words; Implementing TF-IDF; Working with Skip-gram Embeddings; Working with CBOW Embeddings; Making Predictions with Word2vec; Using Doc2vec for Sentiment Analysis; Chapter 8: Convolutional Neural Networks; Introduction; Implementing a Simpler CNN; Implementing an Advanced CNN; Retraining Existing CNNs models; Applying Stylenet/Neural-Style; Implementing DeepDream; Chapter 9: Recurrent Neural Networks; Introduction; Implementing RNN for Spam Prediction; Implementing an LSTM Model
Stacking multiple LSTM LayersCreating Sequence-to-Sequence Models; Training a Siamese Similarity Measure; Chapter 10: Taking TensorFlow to Production; Introduction; Implementing unit tests; Using Multiple Executors; Parallelizing TensorFlow; Taking TensorFlow to Production; Productionalizing TensorFlow -- An Example; Chapter 11: More with TensorFlow; Introduction; Visualizing graphs in Tensorboard; There's more…; Working with a Genetic Algorithm; Clustering Using K-Means; Solving a System of ODEs; Index
Python Machine Learning Cookbook
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100 recipes that teach you how to perform various machine learning tasks in the real worldAbout This Book*Understand which algorithms to use in a given context with the help of this exciting recipe-based guide*Learn about perceptrons and see how they are used to build neural networks*Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniquesWho This Book Is ForThis book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn*Explore classification algorithms and apply them to the income bracket estimation problem*Use predictive modeling and apply it to real-world problems*Understand how to perform market segmentation using unsupervised learning*Explore data visualization techniques to interact with your data in diverse ways*Find out how to build a recommendation engine*Understand how to interact with text data and build models to analyze it*Work with speech data and recognize spoken words using Hidden Markov Models*Analyze stock market data using Conditional Random Fields*Work with image data and build systems for image recognition and biometric face recognition*Grasp how to use deep neural networks to build an optical character recognition systemIn DetailMachine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Advanced Machine Learning with Python.
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Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models togetherIn Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
Python Machine Learning By Example
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Learning Scikit-Learn: Machine Learning in Python
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The book adopts a tutorial-based approach to introduce the user to Scikit-learn. If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
Building Machine Learning Systems with Python - Second Edition
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Mastering Machine Learning with Scikit-Learn
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If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.
Building Machine Learning Systems with Python
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This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them. This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.
TensorFlow for Deep Learning
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Introduction to deep learning -- Introduction to TensorFlow primitives -- Linear and logistic regression with TensorFlow -- Fully connected deep networks -- Hyperparameter optimization -- Convolutional neural networks -- Recurrent neural networks -- Reinforcement learning -- Training large deep networks -- The future of deep learning.
Deep Learning with PyTorch : a practical approach to building neural network models using PyTorch.
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This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
Hands-On Data Science and Python Machine Learning.
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Getting Started; Installing Enthought Canopy; Giving the installation a test run; If you occasionally get problems opening your IPNYB files; Using and understanding IPython (Jupyter) Notebooks; Python basics -- Part 1; Understanding Python code; Importing modules; Data structures; Experimenting with lists; Pre colon; Post colon; Negative syntax; Adding list to list; The append function; Complex data structures; Dereferencing a single element; The sort function; Reverse sort; Tuples
Dereferencing an elementList of tuples; Dictionaries; Iterating through entries; Python basics -- Part 2; Functions in Python; Lambda functions -- functional programming; Understanding boolean expressions; The if statement; The if-else loop; Looping; The while loop; Exploring activity; Running Python scripts; More options than just the IPython/Jupyter Notebook; Running Python scripts in command prompt; Using the Canopy IDE; Summary; Chapter 2: Statistics and Probability Refresher, and Python Practice; Types of data; Numerical data; Discrete data; Continuous data; Categorical data; Ordinal data
Mean, median, and modeMean; Median; The factor of outliers; Mode; Using mean, median, and mode in Python; Calculating mean using the NumPy package; Visualizing data using matplotlib; Calculating median using the NumPy package; Analyzing the effect of outliers; Calculating mode using the SciPy package; Some exercises; Standard deviation and variance; Variance; Measuring variance; Standard deviation; Identifying outliers with standard deviation; Population variance versus sample variance; The Mathematical explanation; Analyzing standard deviation and variance on a histogram
Using Python to compute standard deviation and varianceTry it yourself; Probability density function and probability mass function; The probability density function and probability mass functions; Probability density functions; Probability mass functions; Types of data distributions; Uniform distribution; Normal or Gaussian distribution; The exponential probability distribution or Power law; Binomial probability mass function; Poisson probability mass function; Percentiles and moments; Percentiles; Quartiles; Computing percentiles in Python; Moments; Computing moments in Python; Summary
Chapter 3: Matplotlib and Advanced Probability ConceptsA crash course in Matplotlib; Generating multiple plots on one graph; Saving graphs as images; Adjusting the axes; Adding a grid; Changing line types and colors; Labeling axes and adding a legend; A fun example; Generating pie charts; Generating bar charts; Generating scatter plots; Generating histograms; Generating box-and-whisker plots; Try it yourself; Covariance and correlation; Defining the concepts; Measuring covariance; Correlation; Computing covariance and correlation in Python; Computing correlation -- The hard way
Computing correlation -- The NumPy way
Learning Scrapy
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Learn the art of efficient web scraping and crawling with PythonAbout This Book Extract data from any source to perform real time analytics. Full of techniques and examples to help you crawl websites and extract data within hours. A hands-on guide to web scraping and crawling with real-life problems and solutionsWho This Book Is ForIf you are a software developer, data scientist, NLP or machine-learning enthusiast or just need to migrate your company's wiki from a legacy platform, then this book is for you. It is perfect for someone , who needs instant access to large amounts of semi-structured data effortlessly.What You Will Learn Understand HTML pages and write XPath to extract the data you need Write Scrapy spiders with simple Python and do web crawls Push your data into any database, search engine or analytics system Configure your spider to download files, images and use proxies Create efficient pipelines that shape data in precisely the form you want Use Twisted Asynchronous API to process hundreds of items concurrently Make your crawler super-fast by learning how to tune Scrapy's performance Perform large scale distributed crawls with scrapyd and scrapinghubIn DetailThis book covers the long awaited Scrapy v 1.0 that empowers you to extract useful data from virtually any source with very little effort. It starts off by explaining the fundamentals of Scrapy framework, followed by a thorough description of how to extract data from any source, clean it up, shape it as per your requirement using Python and 3rd party APIs. Next you will be familiarised with the process of storing the scrapped data in databases as well as search engines and performing real time analytics on them with Spark Streaming. By the end of this book, you will perfect the art of scarping data for your applications with easeStyle and approachIt is a hands on guide, with first few chapters written as a tutorial, aiming to motivate you and get you started quickly. As the book progresses, more advanced features are explained with real world examples that can be referred while developing your own web applications.
MATLAB for Machine Learning.
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Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis.
Statistics for Machine Learning
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Chapter 1: Journey from Statistics to Machine Learning; Statistical terminology for model building and validation; Machine learning; Major differences between statistical modeling and machine learning; Steps in machine learning model development and deployment; Statistical fundamentals and terminology for model building and validation; Bias versus variance trade-off; Train and test data; Machine learning terminology for model building and validation
Linear regression versus gradient descentMachine learning losses; When to stop tuning machine learning models; Train, validation, and test data; Cross-validation; Grid search; Machine learning model overview; Summary; Chapter 2: Parallelism of Statistics and Machine Learning; Comparison between regression and machine learning models; Compensating factors in machine learning models; Assumptions of linear regression; Steps applied in linear regression modeling; Example of simple linear regression from first principles; Example of simple linear regression using the wine quality data
Example of multilinear regression -- step-by-step methodology of model buildingBackward and forward selection; Machine learning models -- ridge and lasso regression; Example of ridge regression machine learning; Example of lasso regression machine learning model; Regularization parameters in linear regression and ridge/lasso regression; Summary; Chapter 3: Logistic Regression Versus Random Forest; Maximum likelihood estimation; Logistic regression -- introduction and advantages; Terminology involved in logistic regression; Applying steps in logistic regression modeling
Example of logistic regression using German credit dataRandom forest; Example of random forest using German credit data; Grid search on random forest; Variable importance plot; Comparison of logistic regression with random forest; Summary; Chapter 4: Tree-Based Machine Learning Models; Introducing decision tree classifiers; Terminology used in decision trees; Decision tree working methodology from first principles; Comparison between logistic regression and decision trees; Comparison of error components across various styles of models
Remedial actions to push the model towards the ideal regionHR attrition data example; Decision tree classifier; Tuning class weights in decision tree classifier; Bagging classifier; Random forest classifier; Random forest classifier -- grid search; AdaBoost classifier; Gradient boosting classifier; Comparison between AdaBoosting versus gradient boosting; Extreme gradient boosting -- XGBoost classifier; Ensemble of ensembles -- model stacking; Ensemble of ensembles with different types of classifiers; Ensemble of ensembles with bootstrap samples using a single type of classifier
Machine Learning with Spark
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Getting up and running with Spark -- Designing a machine learning system -- Obtaining, processing, and preparing data with Spark -- Building a recommendation engine with Spark -- Building a classification model with Spark -- Building a regression model with Spark -- Building a clustering model with Spark -- Dimensionality reduction with Spark -- Advanced text processing with Spark -- Real-time machine learning with Spark streaming.
Quantum Machine Learning
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Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research.
Mastering Machine Learning with Spark 2.x.
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Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and "small data" machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.
Temporal Data Mining Via Unsupervised Ensemble Learning
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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
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This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
Graph-Based Semi-Supervised Learning
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While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of- the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
Robotic Vision
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This book offers comprehensive coverage of the current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world
Pattern Recognition and Machine Learning
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The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
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Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems
Applied Machine Learning for Smart Data Analysis
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The book focuses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results
Cloud Computing for Machine Learning and Cognitive Applications
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The Elements of Statistical Learning
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Large-Scale Kernel Machines
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Elements of Causal Inference
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The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data
The Voice in the Machine
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umanspeak -- The speech pioneers -- Artificial intelligence vs, real brute force -- The power of statistics -- There is no data like more data -- Let's have a dialogue -- An interlude at the end of the chain -- Becoming real -- The business of speech -- The future is not what it used to be.
An Introduction to Statistical Learning : with applications in R
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader
Deep Learning Innovations and Their Convergence with Big Data
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This book capture the state of the art trends and advancements in big data analytics, its technologies, and applications. The book also aims to identify potential research directions and technologies that will facilitate insight generation in various domains of science, industry, business, and consumer applications
Deep Learning for Coders with Fastai and Pytorch
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Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
