Machine learning : concepts, tools and data visualization

This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book. Contents About the Author Preface Book Reviews Part I Artificial Intelligence Chapter 1 Summary of Artificial Intelligence 1.1 Definition of Artificial Intelligence 1.2 History of Artificial Intelligence 1.2.1 The Beginning of Artificial Intelligence 1.2.2 Early Artificial Intelligence 1.2.3 The Stagnation of Artificial Intelligence 1.2.4 The Reactivation of Artificial Intelligence (1969–1990) 1.2.5 The Augustan Era (Platinum Age) of Artificial Intelligence (1980–present) 1.3 Classification of Artificial Intelligence 1.3.1 Strong Artificial Intelligence 1.3.2 Weak Artificial Intelligence 1.4 Practice Questions Chapter 2 Machine Learning 2.1 Definition of Machine Learning 2.1.1 Machine Learning and Data Mining 2.2 Classification of Machine Learning 2.3 Supervised Learning 2.3.1 Classification 2.3.2 Regression 2.3.3 Reinforcement Learning 2.4 Unsupervised Learning 2.5 The Difference Between How Machine Learning and Statistics Work 2.6 Considerations for Performing Machine Learning 2.7 Resources for Machine Learning 2.7.1 Kaggle 2.7.2 Public Data Portal 2.8 Practice Questions Chapter 3 Deep Learning 3.1 Definition and Concepts of Deep Learning 3.1.1 Perceptron 3.1.2 Multilayer Perceptron 3.2 Types of Artificial Neural Network 3.2.1 DNN 3.2.2 CNN 3.2.3 RNN 3.3 Practice Questions Chapter 4 Case Study 4.1 AlphaGo 4.1.1 System Configuration 4.1.2 Algorithm Implementation 4.2 IBM Watson 4.3 Practice Questions Chapter 5 Microsoft Azure Machine Learning Studio 5.1 Introduction of Microsoft Azure Machine Learning Studio 5.2 Microsoft Azure Machine Learning Studio Sign-up 5.3 Introduction of Microsoft Azure Machine Learning Studio Function 5.4 Practice Questions Chapter 6 Create Prediction Model using Microsoft Azure Machine Learning Studio 6.1 Microsoft Azure Machine Learning Studio Experiment Configuration and Functions 6.2 Microsoft Azure Machine Learning Studio Experiment Tutorial 6.3 Practice Making Microsoft Azure Machine Learning Studio Experiment Prediction Models 6.3.1 Importing Data to Azure Cloud 6.3.2 Visualize Data Set 6.3.3 Data Preprocessing 6.3.4 Feature Definition 6.3.5 Machine Learning Algorithm Selection and Implementation 6.3.6 Predicting with New Data 6.4 Practice Questions Chapter 7 Create Prediction Models using Microsoft Azure Machine Learning Studio Web Service Deployment 7.1 Create Prediction Models using Microsoft Azure Machine Learning Studio Web Service Deployment Tutorial 7.2 Web Service Model in the R and Python languages 7.2.1 Integrating the Web Service with the R Language 7.2.2 Integrating the Web Service with Python 7.3 Practice Questions Chapter 8 Creating a Prediction Model using Microsoft Azure Machine Learning Studio Script Integration 8.1 R Script Integration 8.1.1 Viewing Data using R Script 8.1.2 Implement Decision Tree using R Script 8.2 Python Script Integration 8.2.1 Implement K-Means using Python Script 8.3 Practice Questions Part II Exercises Chapter 9 Exercises 9.1 Predicting Car Price Using Regression 9.2 Classify News Article Category 9.3 Exploring Credit Risk Groups Using Anomaly Detection 9.4 Predicting the Number of People Getting on and Off at Gangnam Station in the Morning Rush Hours 9.5 Heart Disease Prediction 9.6 Find Similar Companies Using K-Means Clustering 9.7 Practice Questions Part III Visualization Chapter 10 Visualization 10.1 Definition of Visualization 10.2 Purpose and Function of Data Visualization 10.2.1 Purpose of Data Visualization 10.2.2 Function of Data Visualization 10.3 Practice Questions Chapter 11 Visualization with Power BI 11.1 Introduction of Power BI 11.2 Download and log in to Power BI Desktop 11.3 Configure Power BI Desktop Screen 11.4 Data Import 11.4.1 Import Open Data from Data World 11.4.2 Importing Excel File Data 11.5 Introduction of Power BI Visualization Graph 11.5.1 How to use Visualizations in Power BI 11.5.2 Types of Visualization Chart 11.6 Using Learning Results with Azure Machine Learning Studio 11.6.1 Excel File 11.7 Practice Questions Chapter 12 Visualization with R in Power BI 12.1 Introduction of R 12.2 How to use the R Script Editor 12.3 Visualization for Data Analysis Using Power BI R Script 12.3.1 Numerical Univariate Plot 12.3.2 Categorical Univariate Plot 12.3.3 Numerical Bivariate Plot 12.3.4 Categorical Bivariate Plot 12.4 Practice Questions Bibliography