Content

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This is what you can already find here:

Data Science Concepts

• The Data Science Lifecycle
• Supervised versus Unsupervised Learning
• Metrics for Classification Problems
• Metrics for Regression Problems
• Feature Selection Methods
• Bias and Variance
• Ensemble Models - Illustrated
• Loss Functions in Machine Learning
• Gradient Descent

Classical Machine Learning

Supervised Machine Learning

• Linear Regression
• Linear Regression - Analytical Solution & Example
• Logistic Regression
• Decision Trees - Explained
• Decision Trees for Classification - Example
• Decision Trees for Regression - Example
• Random Forests - Explained
• AdaBoost - Explained
• AdaBoost for Classification - Example
• AdaBoost for Regression - Example
• Gradient Boost for Regression - Explained
• Gradient Boost for Regression - Example
• Gradient Boost for Classification - Explained
• Gradient Boost for Classification - Example
• Gradient Boosting Variants - Sklearn vs. XGBoost vs. LightGBM vs. CatBoost

Unsupervised Machine Learning

• Understanding K-Means Clustering
• Principal Component Analysis (PCA)

Deep Learning

• Introduction to Deep Learning
• Backpropagation - Step by Step

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