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

Section 1: Machine Learning Techniques in R (excluded).

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Apache Spark 2.x Machine Learning Cookbook. by Amirghodsi, Siamak

Table of Contents: “…. ; See also ; Chapter 3: Spark's Three Data Musketeers for Machine Learning -- Perfect Together; Introduction; RDDs -- what started it all ...…”
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The Ethical Governance of Artificial Intelligence and Machine Learning in Healthcare. by Nguyen, Tina

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Network Anomaly Detection : a Machine Learning Perspective. by Bhattacharyya, Dhruba Kumar

Table of Contents: “…Networks and Anomalies; 3. An Overview of Machine Learning Methods; 4. Detecting Anomalies in Network Data; 5. …”
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Neural network projects with Python : the ultimate guide to using Python to explore the true power of neural networks through six projects by Loy, James

Table of Contents: “…Table of ContentsMachine Learning and Neural Networks 101Predicting Diabetes with Multilayer PerceptronsPredicting Taxi Fares with Deep Feedforward NetworksCats Versus Dogs -- Image Classification Using CNNsRemoving Noise from Images Using AutoencodersSentiment Analysis of Movie Reviews Using LSTMImplementing a Facial Recognition System with Neural NetworksWhat's Next?…”
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Data mining with decision trees : theory and applications by Rokach, Lior

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Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more by Atienza, Rowel

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Sequential methods in pattern recognition and machine learning / by Fu, K. S. (King Sun), 1930-1985

Table of Contents: “…Front Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. …”
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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R by Rai, Bharatendra K.

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R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet by Hodnett, Mark, Wiley, Joshua F.

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Machine learning with Scala quick start guide : leverage popular machine learning algorithms and techniques and implement them in Scala / by Karim, Md. Rezaul

Table of Contents: “…Introduction to Machine Learning with Scala --…”
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MACHINE LEARNING USING TENSORFLOW COOKBOOK : over 60 recipes on machine learning using deep ... learning solutions from kaggle masters and google. by Audevart, Alexia

Subjects: “…Machine learning.…”
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Hands-on deep learning with TensorFlow

Subjects: “…Machine learning.…”
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Nova. / Computers v crime

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Data science essentials advanced algorithms and visualizations by Boschetti, Alberto, Massaron, Luca

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Hands-on supervised machine learning with Python /

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Python for deep learning : build neural networks in Python.

Subjects: “…Deep learning (Machine learning)…”
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Applied probability & statistics : for computer science, data science & machine learning / Probability / statistics : by Nauman, Mohammad

Subjects: “…Machine learning.…”
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Machine learning and cognitive computing for mobile communications and wireless networks /

Table of Contents: “…Identification and Prediction 18 1.5.9 Services of Social Media 18 1.5.10 Medical Services 18 1.5.11 Recommendation for Products and Services 18 1.5.11.1 Machine Learning in Education 19 1.5.11.2 Machine Learning in Search Engine 19 1.5.11.3 Machine Learning in Digital Marketing 19 1.5.11.4 Machine Learning in Healthcare 19 1.6 Future of Machine Learning 20 1.7 Conclusion 22 References 23 2 Cognitive Computing: Architecture, Technologies and Intelligent Applications 25; Nilanjana Pradhan,…”
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TensorFlow Reinforcement Learning Quick Start Guide : Get up and Running with Training and Deploying Intelligent, Self-Learning Agents Using Python. by Balakrishnan, Kaushik

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Learning Apache Mahout : acquire practical skills in Big Data Analytics and explore data science with Apache Mahout / Acquire practical skills in Big Data Analytics and explore dat... by Tiwary, Chandramani

Table of Contents: “…Mahout API -- Java program exampleThe dataset; Parallel versus in-memory execution mode; Summary; Chapter 2: Core Concepts in Machine Learning; Supervised learning; Determine the objective; Decide the training data; Create and clean the training set; Feature extraction; Train the models; Bagging; Boosting; Validation; Holdout-set validation; K-fold cross validation; Evaluation; Bias-variance trade-off; Function complexity and amount of training data; Dimensionality of the input space; Noise in data; Unsupervised learning; Cluster analysis; Objective; Feature representation…”
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Computational trust models and machine learning /

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Natural language processing with TensorFlow : teach language to machines using Python's deep learning library by Ganegedara, Thushan

Subjects: “…Machine learning.…”
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Mathematics and Programming for Machine Learning with R From the Ground Up. by Claster, William B.

Subjects: “…Machine learning.…”
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Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data. by Chopra, Rohan

Table of Contents: “…Cover; FM; Copyright; Table of Contents; Preface; Chapter 1: Introduction to Data Science and Data Pre-Processing; Introduction; Python Libraries; Roadmap for Building Machine Learning Models; Data Representation; Independent and Target Variables; Exercise 1: Loading a Sample Dataset and Creating the Feature Matrix and Target Matrix; Data Cleaning; Exercise 2: Removing Missing Data; Exercise 3: Imputing Missing Data; Exercise 4: Finding and Removing Outliers in Data; Data Integration; Exercise 5: Integrating Data; Data Transformation; Handling Categorical Data…”
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Apache Spark Quick Start Guide : Quickly Learn the Art of Writing Efficient Big Data Applications with Apache Spark. by Mehrotra, Shrey

Table of Contents: “…; Spark architecture overview; Spark language APIs; Scala; Java; Python; R; SQL; Spark components; Spark Core; Spark SQL; Spark Streaming; Spark machine learning; Spark graph processing; Cluster manager; Standalone scheduler; YARN; Mesos; Kubernetes; Making the most of Hadoop and Spark; Summary; Chapter 2: Apache Spark Installation; AWS elastic compute cloud (EC2); Creating a free account on AWS; Connecting to your Linux instance…”
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TensorFlow Machine Learning Projects : Build 13 Real-World Projects with Advanced Numerical Computations Using the Python Ecosystem. by Jain, Ankit

Table of Contents: “…Soft placementGPU memory handling; Multiple graphs; Machine learning, classification, and logistic regression; Machine learning; Classification; Logistic regression for binary classification; Logistic regression for multiclass classification; Logistic regression with TensorFlow; Logistic regression with Keras; Summary; Questions; Further reading; Chapter 2: Using Machine Learning to Detect Exoplanets in Outer Space; What is a decision tree?…”
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Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems / by Koduvely, Hari M.

Subjects: “…Machine learning.…”
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Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python by Petrou, Theodore

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Machine learning and data science with Python : a complete beginners guide / Machine learning using Python : by Nelson, Abhilash

Subjects: “…Machine learning.…”
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Machine Learning Projects for Mobile Applications : Build Android and IOS Applications Using TensorFlow Lite and Core ML / by NG, Karthikeyan

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Mobile Landscapes in Machine Learning; Machine learning basics; Supervised learning; Unsupervised learning; Linear regression -- supervised learning; TensorFlow Lite and Core ML; TensorFlow Lite; Supported platforms; TensorFlow Lite memory usage and performance ; Hands-on with TensorFlow Lite ; Converting SavedModel into TensorFlow Lite format; Strategies; TensorFlow Lite on Android; Downloading the APK binary; TensorFlow Lite on Android Studio.…”
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Machine Learning for Healthcare Analytics Projects : Build Smart AI Applications Using Neural Network Methodologies Across the Healthcare Vertical Market. by Learning Solutions, Eduonix

Table of Contents: “…Cover; Title Page; Copyright and Credits; About Packt; Contributor; Table of Contents; Preface; Chapter 1: Breast Cancer Detection; Objective of this project; Detecting breast cancer with SVM and KNN models; Data visualization with machine learning; Relationships between variables; Understanding machine learning algorithms ; Training models ; Predictions in machine learning; Summary; Chapter 2: Diabetes Onset Detection; Detecting diabetes using a grid search; Introduction to the dataset; Preprocessing the dataset; Normalizing the dataset; Building our Keras model.…”
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AWS Certified Machine Learning Specialty The Definitive Guide to Passing the MLS-C01 Exam on the Very First Attempt. by Nanda, Somanath

Table of Contents: “…Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Section 1: Introduction to Machine Learning -- Chapter 1: Machine Learning Fundamentals -- Comparing AI, ML, and DL -- Examining ML -- Examining DL -- Classifying supervised, unsupervised, and reinforcement learning -- Introducing supervised learning -- The CRISP-DM modeling life cycle -- Data splitting -- Overfitting and underfitting -- Applying cross-validation and measuring overfitting -- Bootstrapping methods -- The variance versus bias trade-off -- Shuffling your training set…”
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Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / by Ratner, Bruce

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Mastering Machine Learning for Penetration Testing : Develop an Extensive Skill Set to Break Self-Learning Systems Using Python. by Chebbi, Chiheb

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning in Pentesting; Technical requirements; Artificial intelligence and machine learning ; Machine learning models and algorithms ; Supervised; Bayesian classifiers; Support vector machines; Decision trees ; Semi-supervised; Unsupervised; Artificial neural networks ; Linear regression ; Logistic regression; Clustering with k-means ; Reinforcement; Performance evaluation ; Dimensionality reduction; Improving classification with ensemble learning.…”
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Big Data, IoT, and Machine Learning Tools and Applications. by Agrawal, Rashmi

Table of Contents: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgement -- Editors -- Contributors -- Section I Applications of Machine Learning -- Chapter 1 Machine Learning Classifiers -- 1.1 Introduction -- 1.2 Machine Learning Overview -- 1.2.1 Steps in Machine Learning -- 1.2.2 Performance Measures for Machine Learning Algorithms -- 1.2.2.1 Confusion Matrix -- 1.3 Machine Learning Approaches -- 1.4 Types of Machine Learning -- 1.4.1 Supervised Learning -- 1.4.2 Unsupervised Learning -- 1.4.3 Semi-Supervised Learning…”
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Computer Vision and Machine Learning in Sustainable Mobility by Chatterjee, Sromona

Table of Contents: “…Intro -- Chapter 1: Introduction -- Chapter 2: Understanding the Scene Data- Pavement AreaGrouping in Images -- Chapter 3: Intelligent Road Maintenance- A MachineLearning Approach for Surface DefectDetection -- Chapter 4: Defect Detection on Road Surfaces Using FuzzyImage Descriptors and Keypoint Matching -- Chapter 5: Smart Infrastructure Monitoring: Development ofa Decision Support System for Vision-BasedRoad Crack Detection -- Chapter 6: Contribution and Conclusion…”
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Recent Methods from Statistics and Machine Learning for Credit Scoring. by Kraus, Anne

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Just Enough R! : an Interactive Approach to Machine Learning and Analytics. by Roiger, Richard J.

Table of Contents: “…Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. …”
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