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Introducing Machine Learning

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  • Copyright 2020
  • Pages: 400
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-558839-1
  • ISBN-13: 978-0-13-558839-0

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



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Table of Contents


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


Chapter 2 Intelligent Software

Applied Artificial Intelligence

    Evolution of Software Intelligence

    Expert Systems

General Artificial Intelligence

    Unsupervised Learning

    Supervised Learning


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


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


Chapter 5 The Data Factor

Data Quality

    Data Validity

    Data Collection

Data Integrity






What’s a Data Scientist, Anyway?

    The Data Scientist at Work

    The Data Scientist Tool Chest

    Data Scientists and Software Developers


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


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


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


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


    Scatter Plots

    Scatter Plot Matrices

    Plotting at the Appropriate Scale


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





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



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


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


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


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


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


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


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



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


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


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


9780135565667    TOC    12/19/2019


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