Machine Learning Essentials

This repository contains the course materials and resources for the Machine Learning Essentials course.

Course Description

This course is designed to provide a beginner-friendly introduction to machine learning. It is intended for those who are new to machine learning, but have some experience with programming. The course will cover the following topics:

• Feature engineering and data preprocessing
  • Data Imputation
    • Numerical
    • Categorical
  • Data Scaling
    • Standardization
    • Normalization
  • Data Encoding
    • One-hot encoding
    • Label encoding
  • Handling Outliers
    • Removing outliers
    • Replacing outliers
    • Capping outliers
    • Discretization
  • Grouping Operations
    • Categorical columns
    • Numerical columns
  • Feature Split
  • Log Transform
  • Binning
  • Scrubbing
  • Generating Polynomial and Interaction Features
• Supervised learning
  • Tree-based models
    • Decision Trees
    • Ensemble Methods
      • Bagging
        • Random Forests
      • Boosting
        • AdaBoost
        • Gradient Boosting
        • XGBoost
      • voting
  • Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Interpolation
    • Ordinary Least Square Regression
    • Ridge Regression
    • Lasso Regression
  • Classification
    • Logistic Regression
    • K-Nearest Neighbors
    • Support Vector Machines
    • Naive Bayes
    • Artificial Neural Networks
• Unsupervised learning
  • Clustering
    • K-Means
    • Hierarchical Clustering
  • Dimensionality Reduction
    • Principal Component Analysis
    • Random Projection
  • Association Rule Learning
    • Apriori
    • FP-Growth
• Model Selection
  • Resampling Methods
    • Random Split
    • Time Based Split
    • K-Fold Cross Validation
    • Stratified K-Fold Cross Validation
    • Bootstrapping
  • Probabilistic Methods
    • Akaiki Information Criterion
    • Bayesian Information Criterion
    • Minimum Description Length
    • Structural Risk Minimization
  • Trade Off Methods
    • Bias-Variance Trade Off
    • Precision-Recall Trade Off
    • Overfitting vs Underfitting
• Model Evaluation
  • Regression Metrics
    • Mean Absolute Error
    • Mean Squared Error
    • Root Mean Squared Error
    • Relative Squared Log Error
    • R2 Score
    • Adjusted R2 Score
  • Classification Metrics
    • Accuracy
    • Precision
    • Recall
    • F1 Score
    • ROC Curve
    • AUC Score
    • Log Loss
    • Confusion Matrix
    • Gain and Lift Charts
    • Kolmogorov-Smirnov Chart
  • Clustering Metrics
    • Dunn Index
    • Silhouette Coefficient
    • Elbow Method
    • Devis-Bouldin Index
    • Fowlkes-Mallows Index
    • Homegeneity, Completeness, and V-Measure
    • Mutual Information
  • Dimensionality Reduction Metrics
    • Reconstruction Error
    • Explained Variance Ratio
  • Association Rule Learning Metrics
    • Support
    • Confidence
    • Lift
    • Leverage
    • Conviction

Recommended Packages

The following packages are required for this course:

• Python (>= 3.6)
• NumPy (>= 1.19.5)
• Pandas (>= 1.1.5)
• Matplotlib (>= 3.2.2)
• Seaborn (>= 0.11.1)
• Scikit-Learn (>= 0.24.1)
• Pillow (>= 8.1.0)