Tree-based Models
12.2 Ensemble Models
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)
Logistic Regression
9.1 Binary Logistic Regression
9.2 Multinomial Logistic Regression
Support Vector Machines
10.1 Polynomial Logistic Regression
10.2 Support Vector Machines
Kernel Methods
11.1 K Nearest Neighbors
Tree-based Models
12.1 Decision Trees
12.2 Ensemble Models
Unsupervised Learning
11.2 Clustering
Homeworks
Labs
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
References
Tree-based Models
12.2 Ensemble Models
12.2 Ensemble Models
: 60 minutes
12.1 Decision Trees
Unsupervised Learning