Korbit offers everything you need from A-Z about data science in one place.

See our comprehensive list of learning modules below.

Advisors from

Comprehensive Course Catalogue

Korbit Platform

Welcome to Korbit

Python

- Hello Python

- Variables and Types

- Operators and Expressions

- Input / Output

- If Else

- For Loops

- While Loops

- Variables and Types

- Operators and Expressions

- Input / Output

- If Else

- For Loops

- While Loops

Mathematics

- 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

- 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

- Why Learn Linear Algebra?

- Vectors, Matrices and Scalars

- Addition and Scalar

- Multiplication and Transpose

- Dot Product

- Norm and Euclidean Distance

- Matrix Multiplication

- Identity Matrix

- Vectors, Matrices and Scalars

- Addition and Scalar

- Multiplication and Transpose

- Dot Product

- Norm and Euclidean Distance

- Matrix Multiplication

- Identity Matrix

Statistics

- Introduction to Statistics

- Levels of Measurement and Sampling Techniques

- Graphs

- Mean, Median and Mode

- Percentiles and Quartiles

- Levels of Measurement and Sampling Techniques

- Graphs

- Mean, Median and Mode

- Percentiles and Quartiles

Data science

- Descriptive Statistics

- Introduction to Graphs

- What is a Dataset?

- Exploratory Data Analysis

- Introduction to Graphs

- What is a Dataset?

- Exploratory Data Analysis

Data science

- Descriptive Statistics

- Introduction to Graphs

- What is a Dataset?

- Exploratory Data Analysis

- Introduction to Graphs

- What is a Dataset?

- Exploratory Data Analysis

Machine learning

- What is Machine Learning?

- Supervised Learning

- Classification

- Regression

- Unsupervised Learning

- Clustering

- Reinforcement Learning

- Supervised Learning

- Classification

- Regression

- Unsupervised Learning

- Clustering

- Reinforcement Learning

Machine learning

- Linear Regression

- Interpolation vs. Extrapolation

- Evaluation Metrics (Regression)

- Linear Regression with Categorical Features

- Conditions for Linear Regression

- Handling Outliers in Linear Regression

- Interpolation vs. Extrapolation

- Evaluation Metrics (Regression)

- Linear Regression with Categorical Features

- Conditions for Linear Regression

- Handling Outliers in Linear Regression

Machine learning

- What is Machine Learning?

- Classification

- Binary Classification

- Logistic Regression Basic

- Sigmoid Function

- Evaluation Metrics (Classification)

- Classification

- Binary Classification

- Logistic Regression Basic

- Sigmoid Function

- Evaluation Metrics (Classification)

Machine learning

- 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

- Data Cleaning

- Handling Outliers

- Splitting Data

- Feature Engineering

- One-Hot Encoding

- Feature Importance

- Feature Scaling

- Dimensionality Reduction

- Feature Selection

- Principal Component Analysis

Machine learning

- Classification

- Binary Classification

- Logistic Regression Basic

- Sigmoid Function

- Evaluation Metrics (Classification)

- Binary Classification for Imbalanced Classes

- Naive Bayes’ Classifiers

- K-Nearest Neighbours

- Binary Classification

- Logistic Regression Basic

- Sigmoid Function

- Evaluation Metrics (Classification)

- Binary Classification for Imbalanced Classes

- Naive Bayes’ Classifiers

- K-Nearest Neighbours

Machine learning

- Splitting Data

- Cost and Loss Functions

- Cross Validation

- Parameters vs. Hyperparameters

- Hyperparameter Tuning

- Overview of Regularization

- L1 vs. L2 Regularization

- Cost and Loss Functions

- Cross Validation

- Parameters vs. Hyperparameters

- Hyperparameter Tuning

- Overview of Regularization

- L1 vs. L2 Regularization

Machine learning

- Introduction to Decision Trees

- CART Decision Tree Splits

- Decision Tree Selection Criteria

- Introduction to Random Forests

- CART Decision Tree Splits

- Decision Tree Selection Criteria

- Introduction to Random Forests

Machine learning

- Unsupervised Learning

- Clustering

- K-Means Clustering

- Dimensionality Reduction

- Principal Component Analysis

- Clustering

- K-Means Clustering

- Dimensionality Reduction

- Principal Component Analysis

Machine 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

- 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

- 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

- 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

- 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

- 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

- 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

- Overview of Data Preprocessing

- One-Hot Encoding

- Feature Importance

- Feature Scaling

- Feature Selection

- Logistic Regression Basic

- Evaluation Metrics (Classification)

- Prioritizing Sales Leads

Machine learning

- Introduction to Graphs

- Data Cleaning

- Handling Outliers

- Handling Outliers in Linear Regression

- Data Cleaning

- Handling Outliers

- Handling Outliers in Linear Regression

Machine learning

- Feature Engineering

- One-Hot Encoding

- Feature Scaling

- Dimensionality Reduction

- Feature Importance

- Feature Selection

- Principal Component Analysis

- One-Hot Encoding

- Feature Scaling

- Dimensionality Reduction

- Feature Importance

- Feature Selection

- Principal Component Analysis

Machine learning

- What is a Dataset?

- Splitting Data

- Cross Validation

- Splitting Data

- Cross Validation

Machine learning

- Splitting Data

- Cost and Loss Functions

- Overview of Regularization

- L1 vs. L2 Regularization

- Cost and Loss Functions

- Overview of Regularization

- L1 vs. L2 Regularization

Machine learning

- Unsupervised Learning

- Dimensionality Reduction

- Feature Scaling

- Feature Selection

- Principal Component Analysis

- Dimensionality Reduction

- Feature Scaling

- Feature Selection

- Principal Component Analysis

Machine learning

- Unsupervised Learning

- Clustering

- K-Means Clustering

- Clustering

- K-Means Clustering

Machine learning

- Classification

- Evaluation Metrics (Classification)

- Joint and Marginal Probability Distributions

- Conditional Probability

- Bayes' Theorem

- Naive Bayes’ Classifiers

- Evaluation Metrics (Classification)

- Joint and Marginal Probability Distributions

- Conditional Probability

- Bayes' Theorem

- Naive Bayes’ Classifiers

Machine learning

- Splitting Data

- Cost and Loss Functions

- Cross Validation

- Parameters vs. Hyperparameters

- Hyperparameter Tuning

- Cost and Loss Functions

- Cross Validation

- Parameters vs. Hyperparameters

- Hyperparameter Tuning

Deep learning

- 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

- 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

- Stochastic Gradient Descent

- The Backpropagation Algorithm

- Optimization Difficulties

- Optimization Algorithms

- Visualizing How a Neural Network Is Trained

- Example: Data Preprocessing

- Overview of Model Selection

- The Backpropagation Algorithm

- Optimization Difficulties

- Optimization Algorithms

- Visualizing How a Neural Network Is Trained

- Example: Data Preprocessing

- Overview of Model Selection

Deep learning

- 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

- Overview of Convolutional Neural Networks

- Convolutional Layers

- Pooling Layers

- A Complete Object Detection Model

- Sentiment Classification Task

- Recurrent Neural Networks

- Deep Recurrent Neural Networks