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
- Input / Output
- 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
- Matrix Multiplication
- 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

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