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CMSC320 Section Dr. Alam

CMSC320 Textbook

About the Book

Introduction

Chapter 5 - Pandas

What is Pandas?
DataFrame: The Two-Dimensional Powerhouse
Adding a new column to DataFrame
Applying Aggregation Functions Directly to a DataFrame
Grouping Data with groupby
Key Takeaways
Interactive Pandas Playground

Chapter 6 - SQL

Chapter 6: Mastering SQL for Data Science with Python
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

Statistical Distribution Explorer

Chapter 12 - Intro to ML

Introduction to Machine Learning
Section 5. Your First Algorithm: k-Nearest Neighbors (KNN)
Sectioin 5. Evaluation, Boundaries, and Generalization (Some more ML Concepts)
Distance Metrics and Variants in KNN
Interactive K-Nearest Neighbors

Chapter 14 - Decision Tree

Introduction: The Power of Simple Questions
Section 2: The Tree-Building Algorithm: A Greedy, Recursive Process
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
Interactive Entropy and Information Gain

Chapter 17 - Clustering and PCA

Interactive K Means
Interactive PCA

Chapter 18. Neural Network

Chapter 18: Introduction to the Neural Network
18.1 Activation Functions
Interactive Activation Functions
Gradient Descent
18.2 Optimizers
Interactive Forward Propagation
Interactive Backward Propagation
Putting It All Together: How Neural Network Training Happens

Chapter 19 - Convolutional Neural Network

Introduction
Interactive CNN

Chapter 20 - NLPs

Introduction: What is Natural Language Processing?
Interactive N-Gram
Interactive Text Preprocessing
Interactive Text Frequency

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