Machine learning algorithms using Python programming

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
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