CMSC320 Textbook#
Dr. Fardina Alam and Gavin Hung
Table of Contents#
About the Book
Chapter 5 - Pandas
- What is Pandas?
- Why Use Pandas?
- Installing Pandas
- Core Data Structures
- DataFrame: The Two-Dimensional Powerhouse
- Pandas Basic Operations
- Adding a new column to DataFrame
- Arithmetic Between Columns
- Applying Built-in Pandas/Numpy Functions
- Filtering Data in Pandas
- Applying Aggregation Functions Directly to a DataFrame
- More Advanced: Filtering Data & Apply Statistical Functions
- Grouping Data with
groupby - Exporting Pandas Data in Google Colab
- The pandas Ecosystem: How It Fits In
- When to Use Pandas (And When Not To)
- Key Takeaways
- Summary of Pandas: Key Features at a Glance
- Interactive Pandas Playground
Chapter 6 - SQL
- Chapter 6: Mastering SQL for Data Science with Python
- The Evolution of Databases and SQL
- Relational Databases: Core Concepts
- PK-FK Relationships
- The “Big 6” Elements of a SQL Select Statement
- Some More SQL Essentials
- SQL JOINs: Combining Data from Multiple Tables
- Key Takeaways: SQL
- Interactive SQL Playground
Chapter 7 - Probability and Distributions
Chapter 12 - Intro to ML
- Introduction to Machine Learning
- Section 5. Your First Algorithm: k-Nearest Neighbors (KNN)
- Steps of standard KNN Algorithm:
- Sectioin 5. Evaluation, Boundaries, and Generalization (Some more ML Concepts)
- Distance Metrics and Variants in KNN
- 1. Euclidean Distance (L2 Norm)
- 2. Manhattan (City Block) Distance (L1 Norm)
- 3. Hamming Distance
- 4. Cosine Similarity
- Weighted KNN
- Section 6: Ethics and Responsible ML
- Section 7: Hands-on Practice
- Section 8: Reflection Questions
- Knowledge Check
- Interactive K-Nearest Neighbors
Chapter 14 - Decision Tree
- Introduction: The Power of Simple Questions
- Section 1: Basic Concepts of Decision Tree
- Section 2: The Tree-Building Algorithm: A Greedy, Recursive Process
- Splitting Criteria (Attribute Selection Measures):
- Information Gain (IG)
- How Decision Trees Decide Which Feature to Split (Using Entropy and Information Gain)
- Detailed Example 01: How Decision Trees Decide Which Feature to Split
- Overfitting and Pruning
- Interactive Entropy and Information Gain