Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library

Table of Contents:
Introduction Part I Laying the Groundwork of Machine Learning Chapter 1 How Humans Learn The Journey Toward Thinking Machines The Dawn of Mechanical Reasoning Godel’s Incompleteness Theorems Formalization of Computing Machines Toward the Formalization of Human Thought The Birth of Artificial Intelligence as a Discipline The Biology of Learning What Is Intelligent Software, Anyway? How Neurons Work The Carrot-and-Stick Approach Adaptability to Changes Artificial Forms of Intelligence Primordial Intelligence Expert Systems Autonomous Systems Artificial Forms of Sentiment Summary Chapter 2 Intelligent Software Applied Artificial Intelligence Evolution of Software Intelligence Expert Systems General Artificial Intelligence Unsupervised Learning Supervised Learning Summary Chapter 3 Mapping Problems and Algorithms Fundamental Problems Classifying Objects Predicting Results Grouping Objects More Complex Problems Image Classification Object Detection Text Analytics Automated Machine Learning Aspects of an AutoML Platform The AutoML Model Builder in Action Summary Chapter 4 General Steps for a Machine Learning Solution Data Collection Data-Driven Culture in the Organization Storage Options Data Preparation Improving Data Quality Cleaning Data Feature Engineering Finalizing the Training Dataset Model Selection and Training The Algorithm Cheat Sheet The Case for Neural Networks Evaluation of the Model Performance Deployment of the Model Choosing the Appropriate Hosting Platform Exposing an API Summary Chapter 5 The Data Factor Data Quality Data Validity Data Collection Data Integrity Completeness Uniqueness Timeliness Accuracy Consistency What’s a Data Scientist, Anyway? The Data Scientist at Work The Data Scientist Tool Chest Data Scientists and Software Developers Summary Part II Machine Learning In .NET Chapter 6 The .NET Way Why (Not) Python? Why Is Python So Popular in Machine Learning? Taxonomy of Python Machine Learning Libraries End-to-End Solutions on Top of Python Models Introducing ML.NET Creating and Consuming Models in ML.NET Elements of the Learning Context Summary Chapter 7 Implementing the ML.NET Pipeline The Data to Start From Exploring the Dataset Applying Common Data Transformations Considerations on the Dataset The Training Step Picking an Algorithm Measuring the Actual Value of an Algorithm Planning the Testing Phase A Look at the Metrics Price Prediction from Within a Client Application Getting the Model File Setting Up the ASP.NET Application Making a Taxi Fare Prediction Devising an Adequate User Interface Questioning Data and Approach to the Problem Summary Chapter 8 ML.NET Tasks and Algorithms The Overall ML.NET Architecture Involved Types and Interfaces Data Representation Supported Catalogs Classification Tasks Binary Classification Multiclass Classification Clustering Tasks Preparing Data for Work Training the Model Evaluating the Model Transfer Learning Steps for Building an Image Classifier Applying Necessary Data Transformations Composing and Training the Model Margin Notes on Transfer Learning Summary Part III Fundamentals of Shallow Learning Chapter 9 Math Foundations of Machine Learning Under the Umbrella of Statistics The Mean in Statistics The Mode in Statistics The Median in Statistics Bias and Variance The Variance in Statistics The Bias in Statistics Data Representation Five-number Summary Histograms Scatter Plots Scatter Plot Matrices Plotting at the Appropriate Scale Summary Chapter 10 Metrics of Machine Learning Statistics vs. Machine Learning The Ultimate Goal of Machine Learning From Statistical Models to Machine Learning Models Evaluation of a Machine Learning Model From Dataset to Predictions Measuring the Precision of a Model Preparing Data for Processing Scaling Standardization Normalization Summary Chapter 11 How to Make Simple Predictions: Linear Regression The Problem Guessing Results Guided by Data Making Hypotheses About the Relationship The Linear Algorithm The General Idea Identifying the Cost Function The Ordinary Least Square Algorithm The Gradient Descent Algorithm How Good Is the Algorithm? Improving the Solution The Polynomial Route Regularization Summary Chapter 12 How to Make Complex Predictions and Decisions: Trees The Problem What’s a Tree, Anyway? Trees in Machine Learning A Sample Tree-Based Algorithm Design Principles for Tree-Based Algorithms Decision Trees versus Expert Systems Flavors of Tree Algorithms Classification Trees How the CART Algorithm Works How the ID3 Algorithm Works Regression Trees How the Algorithm Works Tree Pruning Summary Chapter 13 How to Make Better Decisions: Ensemble Methods The Problem The Bagging Technique Random Forest Algorithms Steps of the Algorithms Pros and Cons The Boosting Technique The Power of Boosting Gradient Boosting Pros and Cons Summary Chapter 14 Probabilistic Methods: Naïve Bayes Quick Introduction to Bayesian Statistics Introducing Bayesian Probability Some Preliminary Notation Bayes’ Theorem A Practical Code Review Example Applying Bayesian Statistics to Classification Initial Formulation of the Problem A Simplified (Yet Effective) Formulation Practical Aspects of Bayesian Classifiers Naïve Bayes Classifiers The General Algorithm Multinomial Naïve Bayes Bernoulli Naïve Bayes Gaussian Naïve Bayes Naïve Bayes Regression Foundation of Bayesian Linear Regression Applications of Bayesian Linear Regression Summary Chapter 15 How to Group Data: Classification and Clustering A Basic Approach to Supervised Classification The K-Nearest Neighbors Algorithm Steps of the Algorithm Business Scenarios Support Vector Machine Overview of the Algorithm A Quick Mathematical Refresher Steps of the Algorithm Unsupervised Clustering A Business Case: Reducing the Dataset The K-Means Algorithm The K-Modes Algorithm The DBSCAN Algorithm Summary Part IV Fundamentals of Deep Learning Chapter 16 Feed-Forward Neural Networks A Brief History of Neural Networks The McCulloch-Pitt Neuron Feed-Forward Networks More Sophisticated Networks Types of Artificial Neurons The Perceptron Neuron The Logistic Neuron Training a Neural Network The Overall Learning Strategy The Backpropagation Algorithm Summary Chapter 17 Design of a Neural Network Aspects of a Neural Network Activation Functions Hidden Layers The Output Layer Building a Neural Network Available Frameworks Your First Neural Network in Keras Neural Networks versus Other Algorithms Summary Chapter 18 Other Types of Neural Networks Common Issues of Feed-Forward Neural Networks Recurrent Neural Networks Anatomy of a Stateful Neural Network LSTM Neural Networks Convolutional Neural Networks Image Classification and Recognition The Convolutional Layer The Pooling Layer The Fully Connected Layer Further Neural Network Developments Generative Adversarial Neural Networks Auto-Encoders Summary Chapter 19 Sentiment Analysis: An End-to-End Solution Preparing Data for Training Formalizing the Problem Getting the Data. Manipulating the Data Considerations on the Intermediate Format Training the Model Choosing the Ecosystem Building a Dictionary of Words Choosing the Trainer Other Aspects of the Network The Client Application Getting Input for the Model Getting the Prediction from the Model Turning the Response into Usable Information Summary Part V Final Thoughts Chapter 20 AI Cloud Services for the Real World Azure Cognitive Services Azure Machine Learning Studio Azure Machine Learning Service Data Science Virtual Machines On-Premises Services SQL Server Machine Learning Services Machine Learning Server Microsoft Data Processing Services Azure Data Lake Azure Databricks Azure HDInsight .NET for Apache Spark Azure Data Share Azure Data Factory Summary Chapter 21 The Business Perception of AI Perception of AI in the Industry Realizing the Potential What Artificial Intelligence Can Do for You Challenges Around the Corner End-to-End Solutions Let’s Just Call It Consulting The Borderline Between Software and Data Science Agile AI Summary 9780135565667 TOC 12/19/2019