Machine learning algorithms using Python programming

✍ Scribed by Gaurav Patil; Gopal Sakarkar; Prateek Dutta

• Publisher: Nova Science Publishers
• Year: 2021
• Tongue: English
• Leaves: 186
• Series: Internet of things and machine learning
• Category: Library

✦ Table of Contents

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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