Exploratory Data Analysis (EDA)
6.1 Matplotlib and Seaborn
Data Mining
Syllabus
Vectors and Matrices
1.1 Scalars and Vectors
1.2 Vector Operations
1.3 Matrices
1.4 Matrix Operations
1.5 Practice Problems
Thinking in Arrays
2.1 Tensors
2.2 Introduction to NumPy
2.3 Arithmetic & Indexing
2.4 Broadcasting
2.5 Universal Functions
2.6 Aggregation and Array Methods
2.7 Practice Problems
Linear Algebra in Numpy
3.1 Numerical Linear Algebra
3.2 Projection and Orthogonality
3.3 SVD, Low-Rank Approximation, and PCA
3.4 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
References
Exploratory Data Analysis (EDA)
6.1 Matplotlib and Seaborn
6.1 Matplotlib and Seaborn
: 90 minutes
See
Lab 7
.
Exploratory Data Analysis (EDA)
Linear Regression