Korbit’s Learning Modules

Korbit offers everything you need from A-Z on software and data skills in one place.
See our comprehensive list of learning modules below.

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    Python

    Hello Python

    • Hello Python
    • Variables and Types
    • Python Functions
    • Operators and Expressions
    • Input / Output
    • If Else
    • Code Debugging and Unit Tests
    • For Loops
    • While Loops
    Python

    NumPy Step by Step

    • Introduction to NumPy
    • Creating Arrays
    • Subsetting Arrays
    • Modifying Arrays
    • Arithmetic and Broadcasting in NumPy
    • Math Functions in NumPy
    • Random Functions in NumPy
    Python

    Pandas Step by Step

    • Introduction to Pandas
    • Creating and Saving DataFrames
    • Retrieving Basic DataFrame Information
    • Subsetting DataFrames by Rows and Columns
    • Modifying a DataFrame - Part I
    • Modifying a DataFrame - Part II
    • Aggregating and Grouping DataFrames
    • Combining DataFrames
    • Series
    Python

    Scikit-Learn: Linear Models

    • Ordinary Least Squares
    • Ridge Regression and Classification
    • Lasso Linear Models
    • Elastic Net Regression
    Python

    Transformer Models by Hugging Face

    • Natural Language Processing
    • Transformers, What Can They Do?
    • Transformers: More Examples!
    • How Do Transformers Work?
    • Transfer Learning with Transformer Models
    • General Architecture for Transformer Models
    • Encoder Models
    • Decoder Models
    • Sequence-to-Sequence Models
    • Bias and Limitations
    Python

    Using Hugging Face Transformers

    • Introduction to Using Transformers
    • Behind the Pipeline - Part I
    • Behind the Pipeline - Part II
    • Models
    • Tokenizers - Part I
    • Tokenizers - Part II
    • Handling Multiple Sequences
    • Putting It All Together
    Data science

    Exploratory Data Analysis

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

    RFM Analysis for Customer Segmentation

    • Exploratory Data Analysis
    • Overview of Data Preprocessing
    • Group By
    • Sort
    • RFM Analysis for Customer Segmentation
    Data science

    Retail Customer Segmentation

    • Part I: Data Preprocessing
    • Part II: Exploratory Data Analysis
    • Part III: RFM Scores
    Machine learning

    Welcome to Machine Learning

    • What is Machine Learning?
    • Linear Regression
    • Evaluation Metrics (Regression)
    • Logistic Regression Basic
    • Evaluation Metrics (Classification)
    • Unsupervised Learning
    • Reinforcement Learning
    Machine learning

    Branches of Machine Learning

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

    Linear Regression

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

    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

    Logistic Regression

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

    Naive Bayes’ Classifiers

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

    Predicting Credit Card Fraud

    • 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

    Prioritizing Sales Leads

    • 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

    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

    Splitting Data

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

    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

    Regularization

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

    Hyperparameter Tuning

    • Splitting Data
    • Splitting Data
    • Cost and Loss Functions
    • Cross Validation
    • Parameters vs. Hyperparameters
    • Hyperparameter Tuning
    • Grid Search
    Machine learning

    Feature Manipulation

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

    Handling Outliers

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

    CART Decision Trees and Random Forests

    • What is Machine Learning?
    • Introduction to Decision Trees
    • CART Decision Tree Splits
    • Decision Tree Selection Criteria
    • Introduction to Random Forests
    • Out of Bag Error for Random Forests
    Machine learning

    Predictive Maintenance

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

    Unsupervised Learning

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

    Dimensionality Reduction

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

    Clustering

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

    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

    Bike Usage

    • Part I: Introduction
    • Part II: Data Preprocessing
    • Part III: Correlations
    • Part IV: Linear Regression
    Machine learning

    Article Popularity

    • Part I: Setting Up the Problem
    • Part II: Tuning and Applying the Model
    • Part III: Evaluating the Model
    • Part IV: Feature Importance
    Deep learning

    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

    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

    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
    Mathematics

    Introduction to Functions

    • What is a Function?
    • Graphs of Functions
    • Function Operations
    • Function Asymptotes
    • Polynomials
    • Exponential Functions
    • Logarithms
    • Local and Global Extrema
    • Sigma Notation For Sum
    • Limits
    Mathematics

    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
    Mathematics

    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
    Statistics

    Introduction to Statistics

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