Python and Data Science Libraries
- Installations
- Python Basics
- Data Structures
- Conditional Expressions and Cycles
- File Operations, Functions, Errors and Modules
- NumPy
- Pandas: Excel in the Python World
- Visualization with Matplotlib
Statistical and Exploratory Data Analysis
- Basic Statistics Concepts
- Probability Theory
- Statistical Distributions
- Population, Sample and Related Theorems
- Data Cleaning 1: Variable Types
- Data Cleaning 2: Missing Values
- Data Cleaning 3: Extreme Values
- Exploratory Data Analysis 1: Univariate Analysis
- Exploratory Data Analysis 2: Multivariate Analysis
- Feature Engineering 1: Data Modification
- Feature Engineering 2: Data selection and Dimension Reduction
Supervised Learning 1 – Regression and Classification Problems
- What is Regression?
- Basic Linear Regression and OLS
- Linear Regression Assumptions
- Understanding the Relationship Between the Target Variable and Features
- Measuring the Training Performance of the Regression Model
- Estimation by Linear Regression
- Extreme Compatibility and Regularization
- What is Classification?
- Classification by Logistic Regression
- Measuring Training Performance of Classification Models (Error Matrix)
- Unbalanced Class
- Naive Bayes
Supervised Learning 2 – Basic Machine Learning Algorithms
- Classification with KNN
- Regression with KNN
- Decision Trees
- Random Forests
- Classification with Random Forests
- Regression with Random Forests
- Decision Support Machines
- Classification with Decision Support Machines
- Regression with Decision Support Machines
- Gradient Boosting
- Classification with Gradient Boosting
- Regression with Gradient Boosting
Unattended Learning
- What is Unattended Learning?
- Kmeans
- Spectral Clustering
- Mean-shift
- Affinity Propagation
- How to Measure the Performance of Clustering Algorithms?
