Machine Learning Algorithms Using Python Programming

Description

"The machine learning field is concerned with the question of how to create computer programs that automatically improve information. In recent years, many successful electronic learning applications have been made, from data mining systems that learn to detect fraudulent credit card transactions, filtering programs that learn user readings, to private cars that learn to drive on public highways. At the same time, there have been significant developments in the concepts and algorithms that form the basis for this field. Machine learning is programming computers to optimize a performance criterion using example data or past experience. The goal of this textbook is to present the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning"-- Contents Preface Chapter 1 Python Concept and Interpreter 1.1. Python 1.1.1. What Is Python 1.1.2. Installation of Python On Windows Installing on Other Systems Choosing the Right Python Version 1.2. Interpreter 1.2.1. IDLE What Is IDLE? How to Use IDLE? 1.2.2. Google Colab How to Use Google Colab? Notebook’s Description 1.2.3. Jupyter What Is Jupyter Notebook? How to Install Jupyter Notebook? Installing Jupyter Notebook Using Anaconda Installing Jupyter Notebook Using Pip How to Run the Code in Jupyter Notebook? 1.2.4. Atom What Is Atom? How to Install Atom? How to Use Atom? Executing the Code 1.3. Libraries 1.3.1. Numpy 1.3.2. Pandas 1.3.3. Scikit-Learn 1.3.4. Matlplotlib 1.3.5. Seaborn Links and References Used in This Chapter Links References Chapter 2 Foundation of Machine Learning 2.1. What Is Machine Learning? 2.1.1. Application of Machine Learning Image Recognition Speech Recognition Traffic Prediction Product Recommendations Self-Driving Cars Email Spam and Malware Filtering Virtual Personal Assistant Online Fraud Detection Stock Market Trading Medical Diagnosis Automatic Language Translation 2.1.2. Dataset What Is Dataset? Types of Data Why Is Data Important? 2.1.3. Why Machine Learning in Solving Problems? 2.2. Technique of Machine Learning 2.2.1. Regression 2.2.2. Classification 2.3. Types of Machine Learning 2.3.1. Supervised Learning Applications of Supervised Learning 2.3.2. Unsupervised Learning Applications of Unsupervised Learning in Companies 2.3.3. Reinforcement Learning Applications of Reinforcement Learning Links and References Used in this Chapter Links References Chapter 3 Data Pre-Processing 3.1. What Is Data Preprocessing? 3.2. Features in Machine Learning 3.2.1. What Is the Feature? 3.2.2. Data Type 3.2.3. Categorical of Variable 3.3. Data Quality Assessment 3.3.1. Missing Values 3.3.2. Exploring Dataset 3.4. Feature Encoding 3.5. Splitting the Dataset Links and References Used in This Chapter Links References Chapter 4 Supervised Learning 4.1. Introduction 4.2. Linear Regression 4.2.1. Types of Linear Regression 4.3. Logistic Regression 4.3.1. Types of Logistic Regression 4.4. Naïve Bayes 4.5. Bayes’ Theorem 4.5.1. Types of Naive Bayes Algorithms 4.6. Decision Tree 4.7. K-Nearest Neighbours 4.8. Linear Discriminant Analysis 4.9. Support Vector Machine Types of SVM 4.10. Application of Supervised Learning Links and References Used in This Chapter Links References Chapter 5 Unsupervised Learning 5.1. Introduction 5.2. K-Means for Clustering Problems 5.3. Clustering 5.3.1. Exclusive (Partitioning) 5.3.2. Agglomerative 5.3.3. Overlapping 5.4. Principal Component Analysis 5.5. Singular Value Decomposition 5.6. Independent Component Analysis 5.7. Application of Unsupervised Machine Learning Links and References Used in This Chapter Links References Chapter 6 Reinforcement Learning 6.1. Introduction 6.2. Terms Used in Reinforcement Learning 6.3. Key Feature of Reinforcement Learning 6.4. Elements of Reinforcement Learning 6.5. How Does Reinforcement Learning Works? 6.6. Types of Reinforcement Learning 6.6.1. Positive Reinforcement 6.6.2. Negative Reinforcement 6.7. Markov Decision Process 6.7.1. Markov Property 6.8. Reinforcement Learning Algorithm 6.9. Q-Learning 6.9.1. What is ‘Q’ in Q-Learning? 6.9.2. Q-Table 6.10. Difference between Reinforcement Learning and Supervised Learning 6.11. Reinforcement Learning Application Links and References Used in This Chapter Links References Chapter 7 Kernel Machines 7.1. Introduction 7.2. Kernel Methods 7.3. Optimal Separating Hyperplane (OSH) 7.4. Kernel Trick 7.5. Kernel Regression 7.6. Kernel Dimensionality Reduction 7.7. Kernel Function 7.8. Kernel Properties 7.9. Choosing the Right Kernel References Chapter 8 Data Visualization 8.1. What Is Data Visualization 8.2. Why to Use Data Visualization? 8.3. Types of Data Visualization 8.3.1. Temporal 8.3.2.Hierarchical 8.3.3. Network 8.3.4. Multidimensional 8.3.5. Geospatial 8.4. Common Graph Types 8.4.1. Bar Chart When Do I Use a Bar Chart Visualization? Best Practices for a Bar Chart Visualization 8.4.2. Line Chart When Do I Use a Line Chart Visualization? Best Practices for a Line Chart Visualization 8.4.3. Scatterplot When Do I Use a Scatter Plot Visualization? Best Practices for a Scatter Plot Visualization 8.4.4. Sparkline When Do I Use a Sparkline Visualization? Best Practices for a Sparkline Visualization 8.4.5. Pie Chart When Do I Use a Pie Chart Visualization? Best Practices for a Pie Chart Visualization 8.4.6. Gauge When Do I Use a Gauge Visualization? Best Practices for a Gauge Visualization 8.4.7. Waterfall Chart When Do I Use a Waterfall Chart Visualization? Best Practices for a Waterfall Chart Visualization 8.4.8. Funnel Chart When Do I Use a Funnel Chart Visualization? Best Practices for a Funnel Chart Visualization 8.4.9. Heat Map When Do I Use a Heat Map Visualization? Best Practices for a Heat Map Visualization 8.4.10. Histogram When Do I Use a Histogram Visualization? Best Practices for a Histogram Visualization 8.4.11. Box Plot When Do I Use a Box Plot Visualization? Best Practices for a Box Plot Visualization 8.4.12. Maps When Do I Use a Map Visualization? Best Practices for a Map Visualization 8.4.13. Tables When Do I Use a Table Visualization? Best Practices for a Table Visualization 8.4.14. Indicators 8.4.15. Area Chart 8.5. Tools 8.5.1. Tableau What Is Tableau? Features of Tableau Company Uses Tableau Advantages of Tableau 8.5.2. Google Spreadsheet What Is Google Spreadsheet? Features of Google Spreadsheet Advantages of Google spreadsheet Company Uses Google Spreadsheets 8.5.3. Excel What Is Excel? Feature of Excel Company Uses Excel Advantages of Excel Links and References Use in This Chapter Links References About the Authors Index Blank Page Blank Page