Korbit’s Data Science Learning Modules

Korbit offers everything you need from A-Z about data science in one place.
See our comprehensive list of learning modules below.

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Comprehensive Course Catalogue

Explore Korbit’s learning modules and projects

Korbit Platform

1. Tutorial

Welcome to Korbit
Python

2. Hello Python

  • - Hello Python
  • - Variables and Types
  • - Operators and Expressions
  • - If Else
  • - For Loops
  • - While Loops
Mathematics

3. Probability Basics

  • - Introduction to Probability
  • - Discrete vs. Continuous Random Variables
  • - Expected Value vs. Sample Mean
  • - Variance and Standard Deviation
  • - Binomial Distribution
  • - Normal Distribution
  • - Joint and Marginal Probability
  • - Distributions
  • - Conditional Probability
  • - Bayes' Theorem
Mathematics

4. Linear Algebra Basics

  • - Why Learn Linear Algebra?
  • - Vectors, Matrices and Scalars
  • - Multiplication and Transpose
  • - Dot Product
  • - Norm and Euclidean Distance
  • - Identity Matrix
Statistics

5. Introduction to Statistics

  • - Introduction to Statistics
  • - Levels of Measurement and Sampling Techniques
  • - Graphs
  • - Mean, Median and Mode
  • - Percentiles and Quartiles
Data science

6. Exploratory Data Analysis

  • - Descriptive Statistics
  • - Introduction to Graphs
  • - What is a Dataset?
  • - Exploratory Data Analysis
Data science

7. RFM Analysis for Customer Segmentation

  • - Exploratory Data Analysis
  • - Overview of Data Preprocessing
  • - Group By
  • - Sort
  • - RFM Analysis for Customer Segmentation
Machine learning

8. What is Machine Learning?

  • - What is Machine Learning?
  • - Supervised Learning
  • - Classification
  • - Regression
  • - Unsupervised Learning
  • - Clustering
  • - Reinforcement Learning
Machine learning

9. Linear Regression

  • - Linear Regression
  • - Interpolation vs. Extrapolation
  • - Evaluation Metrics (Regression)
  • - Linear Regression with Categorical Features
  • - Conditions for Linear Regression
  • - Handling Outliers in Linear Regression
Machine learning

10. Logistic Regression

  • - What is Machine Learning?
  • - Classification
  • - Binary Classification
  • - Logistic Regression Basic
  • - Sigmoid Function
  • - Evaluation Metrics (Classification)
Machine learning

11. Data Preprocessing

  • - Overview of Data Preprocessing
  • - Data Cleaning
  • - Handling Outliers
  • - Splitting Data
  • - Feature Engineering
  • - One-Hot Encoding
  • - Feature Importance
  • - Feature Scaling
  • - Dimensionality Reduction
  • - Feature Selection
  • - Principal Component Analysis
Machine learning

12. Classification

  • - Classification
  • - Binary Classification
  • - Logistic Regression Basic
  • - Sigmoid Function
  • - Evaluation Metrics (Classification)
  • - Binary Classification for Imbalanced Classes
  • - Naive Bayes’ Classifiers
  • - K-Nearest Neighbours
Machine learning

13. Foundational Machine Learning Theory

  • - Splitting Data
  • - Cost and Loss Functions
  • - Cross Validation
  • - Parameters vs. Hyperparameters
  • - Hyperparameter Tuning
  • - Overview of Regularization
  • - L1 vs. L2 Regularization
Machine learning

14. CART Decision Trees and Random Forests

  • - Introduction to Decision Trees
  • - CART Decision Tree Splits
  • - Decision Tree Selection Criteria
  • - Introduction to Random Forests
Machine learning

15. Unsupervised Learning

  • - Unsupervised Learning
  • - Clustering
  • - K-Means Clustering
  • - Dimensionality Reduction
  • - Principal Component Analysis
Machine learning

16. Supervised Learning

  • - Motivation
  • - Supervised Learning
  • - Linear Approximators
  • - Generalized Linear Approximators
  • - Overfitting and Underfitting
  • - Bias and Variance: Cross-Validation
  • - Bias-Variance Decomposition
  • - Overview of Logistic Regression
  • - Gradient Descent
  • - Regularization for Logistic Regression
Machine learning

17. Predicting Credit Card Fraud

  • - What is a Dataset?
  • - Exploratory Data Analysis
  • - Splitting Data
  • - Feature Scaling
  • - Principal Component Analysis
  • - Logistic Regression Basic
  • - Evaluation Metrics (Classification)
  • - Predicting Credit Card Fraud with Logistic Regression
Machine learning

18. Predictive Maintenance

  • - Introduction to Decision Trees
  • - CART Decision Tree Splits
  • - Decision Tree Selection Criteria
  • - Introduction to Random Forests
  • - Overview of Data Preprocessing
  • - Feature Engineering
  • - Feature Scaling
  • - Feature Selection
  • - Splitting Data
  • - Cross Validation
  • - Hyperparameter Tuning
  • - Predictive Maintenance
Machine learning

19. Prioritizing Sales Lead

  • - Exploratory Data Analysis
  • - Overview of Data Preprocessing
  • - One-Hot Encoding
  • - Feature Importance
  • - Feature Scaling
  • - Feature Selection
  • - Feature Scaling
  • - Logistic Regression Basic
  • - Evaluation Metrics (Classification)
  • - Prioritizing Sales Leads
Machine learning

20. Handling Outliers

  • - Introduction to Graphs
  • - Data Cleaning
  • - Handling Outliers
  • - Handling Outliers in Linear Regression
Machine learning

21. Feature Manipulation

  • - Feature Engineering
  • - One-Hot Encoding
  • - Feature Scaling
  • - Dimensionality Reduction
  • - Feature Importance
  • - Feature Selection
  • - Principal Component Analysis
Machine learning

22. Splitting Data

  • - What is a Dataset?
  • - Splitting Data
  • - Cross Validation
Machine learning

23. Regularization

  • - Splitting Data
  • - Cost and Loss Functions
  • - Overview of Regularization
  • - L1 vs. L2 Regularization
Machine learning

24. Dimensionality Reduction

  • - Unsupervised Learning
  • - Dimensionality Reduction
  • - Feature Scaling
  • - Feature Selection
  • - Principal Component Analysis
Machine learning

25. Clustering

  • - Unsupervised Learning
  • - Clustering
  • - K-Means Clustering
Machine learning

26. Naive Bayes’ Classifiers

  • - Classification
  • - Evaluation Metrics (Classification)
  • - Joint and Marginal Probability Distributions
  • - Conditional Probability
  • - Bayes' Theorem
  • - Naive Bayes’ Classifiers
Machine learning

27. Hyperparameter Tuning

  • - Splitting Data
  • - Cost and Loss Functions
  • - Cross Validation
  • - Parameters vs. Hyperparameters
  • - Hyperparameter Tuning
Deep learning

28. Introduction to Neural Networks

  • - The Rise of Deep Learning
  • - An Artificial Neuron
  • - Example: 'OR' Neuron Using Sigmoid Activation
  • - Example: Neuron with Rectified Linear Function
  • - One-Layer Neural Network
  • - Example: 'XOR' Neural Network
  • - Deep Training Neural Networks
Deep learning

29. Training Neural Networks

  • - Stochastic Gradient Descent
  • - The Backpropagation Algorithm
  • - Optimization Difficulties
  • - Optimization Algorithms
  • - Visualizing How a Neural Network Is Trained
  • - Example: Data Preprocessing
  • - Overview of Model Selection
Deep learning

30. Convolutional and Recurrent Neural Networks

  • - Object Detection Task
  • - Overview of Convolutional Neural Networks
  • - Convolutional Layers
  • - Pooling Layers
  • - A Complete Object Detection Model
  • - Sentiment Classification Task
  • - Recurrent Neural Networks
  • - Deep Recurrent Neural Networks