Data Analysis From Scratch With Python: Step By Step Guide


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Data Analysis From Scratch With Python Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and... (Peters Morgan) (z-lib.org)

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Document Outline

  • Preface
  • 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
    • 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
    • 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 !

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