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 & 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
  • Addition and Scalar
  • 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