Learning Commons | Cardinal Cushing Library
Library Catalog - Books/Videos

Reproducible Data Science with Pachyderm : Learn How to Build Version-Controlled, End-to-end Data Pipelines Using Pachyderm 2. 0. by Karslioglu, Svetlana

Table of Contents: “…Table of Contents The Problem of Data Reproducibility Pachyderm Basics Pachyderm Pipeline Specification Installing Pachyderm Locally Installing Pachyderm on a Cloud Platform Creating Your First Pipeline Pachyderm Operations Creating an End-to-End Machine Learning Workflow Distributed Hyperparameter Tuning with Pachyderm Pachyderm Language Clients Using Pachyderm Notebooks.…”
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Data analytics made easy : use machine learning and data storytelling in your work without writing... any code. by Mauro, Andrea de

Table of Contents: “…Getting Started with KNIME Transforming Data What is Machine Learning? Applying Machine Learning at Work Getting Started with Power BI Visualizing Data Effectively Telling Stories with Data Extending Your Toolbox.…”
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Artificial intelligence, machine learning, and deep learning / by Campesato, Oswald

Table of Contents: “…Chapter 2: Introduction to Machine Learning --…”
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Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills. / by Denis Rothman (author)

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Mastering TensorFlow 1.x : Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras. by Fandango, Armando

Table of Contents: “…Keras normalization layersKeras noise layers; Adding Layers to the Keras Model; Sequential API to add layers to the Keras model; Functional API to add layers to the Keras Model; Compiling the Keras model; Training the Keras model; Predicting with the Keras model; Additional modules in Keras; Keras sequential model example for MNIST dataset; Summary; Chapter 4: Classical Machine Learning with TensorFlow; Simple linear regression; Data preparation; Building a simple regression model; Defining the inputs, parameters, and other variables; Defining the model; Defining the loss function.…”
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Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. by Smith, Patrick D.

Table of Contents: “…Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold -- 1997-2005; Deep learning and the future -- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks -- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory…”
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Comprehensive analysis of extreme learning machine and continuous genetic algorithm for robust classification of epilepsy from EEG signals by Rajaguru, Harikumar, Prabhakar, Sunil Kumar

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Supervised Machine Learning

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Scala for Machine Learning. by Nicolas, Patrick R.

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started; Mathematical notation for the curious; Why machine learning?; Classification; Prediction; Optimization; Regression; Why Scala?…”
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Statistics for Machine Learning. by Dangeti, Pratap

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; 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.…”
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Machine Learning With Go. by Whitenack, Daniel

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Machine Learning for Developers. by Bonnin, Rodolfo

Table of Contents: “…Cover -- Title Page -- Copyright -- Credits -- Foreword -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction -- Machine Learning and Statistical Science -- Machine learning in the bigger picture -- Types of machine learning -- Grades of supervision -- Supervised learning strategies -- regression versus classification -- Unsupervised problem solvingâ#x80;#x93;clustering -- Tools of the tradeâ#x80;#x93;programming language and libraries -- The Python language -- The NumPy library…”
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MATLAB for Machine Learning. by Ciaburro, Giuseppe

Table of Contents: “…Cover; Title Page; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started with MATLAB Machine Learning; ABC of machine learning; Discover the different types of machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; Choosing the right algorithm; How to build machine learning models step by step; Introducing machine learning with MATLAB; System requirements and platform availability; MATLAB ready for use; Statistics and Machine Learning Toolbox; Datatypes.…”
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F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

Table of Contents: “…Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning; Objective; Getting in touch; Different areas where machine learning is being used; Why use F♯?…”
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Advanced Machine Learning with Python. by Hearty, John

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Unsupervised Machine Learning; Principal component analysis; PCA -- a primer; Employing PCA; Introducing k-means clustering; Clustering -- a primer; Kick-starting clustering analysis; Tuning your clustering configurations; Self-organizing maps; SOM -- a primer; Employing SOM; Further reading; Summary; Chapter 2: Deep Belief Networks; Neural networks -- a primer; The composition of a neural network; Network topologies; Restricted Boltzmann Machine; Introducing the RBM.…”
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The frontiers of machine learning : 2017 Raymond and Beverly Sackler U.S -U.K. Scientific Forum.

Table of Contents: “…Introduction -- Machine learning challenges -- The future of machine learning -- Appendix.…”
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Java for Data Science. by Reese, Richard M.

Table of Contents: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started with Data Science; Problems solved using data science; Understanding the data science problem -- 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 science; Deep learning approaches.…”
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Machine Learning with Scikit-Learn Quick Start Guide : Classification, Regression, and Clustering Techniques in Python. by Jolly, Kevin

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introducing Machine Learning with scikit-learn; A brief introduction to machine learning; Supervised learning; Unsupervised learning; What is scikit-learn?…”
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Hands-On Transfer Learning with Python : Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. by Sarkar, Dipanjan

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Fundamentals; Why ML?; Formal definition; Shallow and deep learning; ML techniques; Supervised learning; Classification; Regression; Unsupervised learning; Clustering; Dimensionality reduction; Association rule mining; Anomaly detection; CRISP-DM; Business understanding; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Standard ML workflow; Data retrieval; Data preparation; Exploratory data analysis…”
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Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. by Thanaki, Jalaj

Table of Contents: “…Cover; Copyright; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Credit Risk Modeling; Introducing the problem statement; Understanding the dataset; Understanding attributes of the dataset; Data analysis; Data preprocessing; Basic data analysis followed by data preprocessing; Number of dependents; Feature engineering for the baseline model; Finding out Feature importance; Selecting machine learning algorithms; K-Nearest Neighbor (KNN); Logistic regression; AdaBoost; GradientBoosting; RandomForest; Training the baseline model; Understanding the testing matrix.…”
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Feature Engineering Made Easy : Identify unique features from your dataset in order to build powerful machine learning systems. by Ozdemir, Sinan

Table of Contents: “…; Understanding the basics of data and machine learning; Supervised learning; Unsupervised learning; Unsupervised learning example â#x80;#x93; marketing segments; Evaluation of machine learning algorithms and feature engineering procedures; Example of feature engineering procedures â#x80;#x93; can anyone really predict the weather?…”
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Python Machine Learning Cookbook. by Joshi, Prateek

Table of Contents: “…Building function compositions for data processingBuilding machine learning pipelines; Finding the nearest neighbors; Constructing a k-nearest neighbors classifier; Constructing a k-nearest neighbors regressor; Computing the Euclidean distance score; Computing the Pearson correlation score; Finding similar users in the dataset; Generating movie recommendations; Chapter 6: Analyzing Text Data; Introduction; Preprocessing data using tokenization; Stemming text data; Converting text to its base form using lemmatization; Dividing text using chunking; Building a bag-of-words model.…”
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Python machine learning / by Lee, Wei-Meng

Table of Contents: “…Introduction to machine learning -- Extending Python using NumPy -- Manipulating tabular data using Pandas -- Data visualization using matplotlib -- Getting started with Scikit-learn for Machine Learning -- Supervised learning : linear regression -- Supervised learning : classification using logistic regression -- Supervised learning : classification using support vector machines -- Supervised learning : classification using K-Nearest Neighbors (KNN) -- Unsupervised learning : clustering using K-Means -- Using Azure Machine Learning Studio -- Deploying machine learning models.…”
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Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow / by Raschka, Sebastian

Table of Contents: “…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.…”
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Machine learning with R : expert techniques for predictive modeling / by Lantz, Brett

Table of Contents: “…Introducing machine learning -- Managing and understanding data -- Lazy learning -- classification using nearest neighbors -- Probabilistic learning -- classification using naive Bayes -- Divide and conquer -- classification using decision trees and rules -- Forecasting numeric data -- regression methods -- Black box methods -- neural networks and support vector machines -- Finding patterns -- market basket analysis using association rules -- Finding groups of data -- clustering with k-means -- Evaluation model performance -- Improving model performance -- Specialized machine learning topics.…”
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Machine learning in bioinformatics /

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Machine Learning in Java : Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, 2nd Edition. by Bhatia, AshishSingh

Table of Contents: “…Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; Non-Euclidean distances…”
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Machine Learning with Swift : Artificial Intelligence for iOS. by Sosnovshchenko, Oleksandr

Table of Contents: “…Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Machine Learning; What is AI?; The motivation behind ML; What is ML?…”
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Machine learning with R : expert techniques for predictive modeling to solve all your data analysis problems / by Lantz, Brett

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introducing Machine Learning; The origins of machine learning; Uses and abuses of machine learning; Machine learning successes; The limits of machine learning; Machine learning ethics; How machines learn; Data storage; Abstraction; Generalization; Evaluation; Machine learning in practice; Types of input data; Types of machine learning algorithms; Matching input data to algorithms; Machine learning with R; Installing R packages; Loading and unloading R packages; Summary.…”
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Machine learning for the web / by Isoni, Andrea

Table of Contents: “…Preface; Introduction to Practical Machine Learning Using Python; General machine-learning concepts; Machine-learning example; Installing and importing a module (library); Preparing, manipulating and visualizing data -- NumPy, pandas and matplotlib tutorials; Using NumPy; Arrays creation; Array manipulations; Array operations; Linear algebra operations; Statistics and mathematical functions; Understanding the pandas module; Exploring data; Manipulate data; Matplotlib tutorial; Scientific libraries used in the book; When to use machine learning; Summary; Unsupervised Machine Learning.…”
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Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics / by Raschka, Sebastian

Table of Contents: “…Giving computers the ability to learn from data -- Training 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 -- Training artificial neural networks for image recognition -- Parallelizing neural network training with Theano.…”
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Machine Learning for Mobile : Practical Guide to Building Intelligent Mobile Applications Powered by Machine Learning. by Gopalakrishnan, Revathi

Table of Contents: “…Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning on Mobile; Definition of machine learning; When is it appropriate to go for machine learning systems?…”
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Supervised Machine Learning Optimization Framework and Applications with SAS and R. by Kolosova, Tanya

Subjects: “…Supervised learning (Machine learning)…”
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Machine learning in Python : essential techniques for predictive analysis / by Bowles, Michael

Subjects: “…Machine learning.…”
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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications / by Lantz, Brett

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introducing Machine Learning; The origins of machine learning; Uses and abuses of machine learning; Ethical considerations; How do machines learn?…”
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Optimization for machine learning /

Table of Contents: “…Introduction : Optimization and machine learning / S. Sra, S. Nowozin, and S.J. Wright -- Convex optimization with sparsity-inducing norms / F. …”
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Machine Learning for Healthcare Handling and Managing Data. by Agrawal, Rashmi

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Editors -- List of Contributors -- Chapter 1 Fundamentals of Machine Learning -- 1.1 Introduction -- 1.2 Data in Machine Learning -- 1.3 The Relationship between Data Mining, Machine Learning, and Artificial Intelligence -- 1.4 Applications of Machine Learning -- 1.4.1 Machine Learning: The Expected -- 1.4.2 Machine Learning: The Unexpected -- 1.5 Types of Machine Learning -- 1.5.1 Supervised Learning -- 1.5.1.1 Supervised Learning Use Cases -- 1.5.2 Unsupervised Learning…”
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Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / by Masood, Faisal, Brigoli, Ross

Table of Contents: “…Table of Contents Challenges in Machine Learning Understanding MLOps Exploring Kubernetes The Anatomy of a Machine Learning Platform Data Engineering Machine Learning Engineering Model Deployment and Automation Building a Complete ML Project Using the Platform Building Your Data Pipeline Building, Deploying and Monitoring Your Model Machine Learning on Kubernetes.…”
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Least Squares Support Vector Machines. by Suykens, Johan A. K.

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Process, Data and Classifier Models for Accessible Supervised Classification Problem Solving. by De Bruyne, Steven

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