8 Logistic Regression
9.2 Multinomial Logistic Regression
Data Mining
Welcome
Vectors and Matrices
1.1 Scalars and Vectors
1.2 Vector Operations
1.3 Matrices
1.4 Matrix Operations
1.5 Practice Problems
Introduction to Tensors
2.1 Tensors
2.2 Introduction to NumPy
2.3 Arithmetic & Indexing
2.4 Practice Problems
Advanced NumPy
3.1 Universal Functions
3.2 Statistical Methods
3.3 Numerical Linear Algebra
3.4 Applications (Optional)
3.5 Practice Problems
Introduction to Pandas
4.1 Essential Data Structures
4.2 Essential Functionality
4.3 Basic Descriptive Statistics
4.4 Data Acquisition
Data Preparation and Wrangling
5.1 Handling Missing Values
5.2 Data Transformation
5.3 Hierarchical Indexing
Exploratory Data Analysis (EDA)
6.1 Matplotlib and Seaborn
Midterm
Linear Regression
8.1 Classical Machine Learning
8.2 Linear Regression (Ordinary Least Squares)
8 Logistic Regression
9.1 Binary Logistic Regression
9.2 Multinomial Logistic Regression
9.3 Regression Evaluation
Practice Problems
Homeworks
Labs
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
References
8 Logistic Regression
9.2 Multinomial Logistic Regression
9.2 Multinomial Logistic Regression
9.1 Binary Logistic Regression
9.3 Regression Evaluation