Data Analysis From Scratch With Python: Step By Step Guide

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Document Outline

• Preface
  • Why the AI Sciences Books are different?
  • Step By Step Guide and Visual Illustrations and Examples
  • Who Should Read This?
• From AI Sciences Publisher
• Author Biography
• Table of Contents
  • Introduction
  • 2. Why Choose Python for Data Science & Machine Learning
    • Python vs R
    • Widespread Use of Python in Data Analysis
    • Clarity
  • 3. Prerequisites & Reminders
    • Python & Programming Knowledge
    • Installation & Setup
    • Is Mathematical Expertise Necessary?
  • 4. Python Quick Review
    • Tips for Faster Learning
  • 5. Overview & Objectives
    • Data Analysis vs Data Science vs Machine Learning
    • Possibilities
    • Limitations of Data Analysis & Machine Learning
    • Accuracy & Performance
  • 6. A Quick Example
    • Iris Dataset
    • Potential & Implications
  • 7. Getting & Processing Data
    • CSV Files
    • Feature Selection
    • Online Data Sources
    • Internal Data Source
  • 8. Data Visualization
    • Goal of Visualization
    • Importing & Using Matplotlib
  • 9. Supervised & Unsupervised Learning
    • What is Supervised Learning?
    • What is Unsupervised Learning?
    • How to Approach a Problem
  • 10. Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Decision Tree
    • Random Forest
  • 11. Classification
    • Logistic Regression
    • K-Nearest Neighbors
    • Decision Tree Classification
    • Random Forest Classification
  • 12. Clustering
    • Goals & Uses of Clustering
    • K-Means Clustering
    • Anomaly Detection
    • 13. Association Rule Learning
    • Explanation
    • Apriori
  • 14. Reinforcement Learning
    • What is Reinforcement Learning?
    • Comparison with Supervised & Unsupervised Learning
    • Applying Reinforcement Learning
  • 15. Artificial Neural Networks
    • An Idea of How the Brain Works
    • Potential & Constraints
    • Here’s an Example
  • 16. Natural Language Processing
    • Analyzing Words & Sentiments
    • Using NLTK
• Thank you !
• Sources & References
  • Software, libraries, & programming language
  • Datasets
  • Online books, tutorials, & other references
• Thank you !