Supervised ML Algorithms

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Section 1: K Nearest Neighbors

  1. K Nearest Neighbours Intuition
  2. When can kNN not work
  3. Distance Measures
  4. Cosine Similarity
  5. KNN for Regression Problems
  6. Weighted kNN
  7. Voronoi Diagram
  8. Measuring Effectiveness
  9. Overfitting vs Underfitting
  10. K Fold Cross Validation
  11. How to spot underfitting and overfitting areas graphically

Section 2: KD Tree and LSH

  1. Binary Search Tree (BST)
  2. Constructing the Tree - Part 1
  3. Constructing the Tree - Part 2
  4. How to navigate the KD Tree
  5. Drawbacks
  6. Introduction to Hashing
  7. LSH - Part 1
  8. LSH - Part 2

Section 3: Decision Trees

  1. Introduction to Decision Trees
  2. Example of reading a Decision Tree
  3. Entropy Part 1 - Understanding the Formula
  4. Entropy Part 2 - Example calculation from dataset
  5. Entropy Part 3 - Role in Building Decision Trees
  6. Information Gain
  7. GiniImpurity
  8. Constructing the Decision tree
  9. How to split numeric features
  10. Dealing with categorical features with many possible values
  11. How to avoid overfitting and Hyperparameters - Part 1
  12. How to avoid overfitting and Hyperparameters - Part 2
  13. Decision Trees for Regression Problems

Section 4: Naive Bayes

  1. What is conditional probability
  2. Basic ideas
  3. Bayes Theorem Proof
  4. Bayes Theorem Math Workout-Part 1
  5. Bayes Theorem Math Workout-Part 2
  6. Naive Bayes Algorithm
  7. Naive Bayes Calculations Example
  8. Naive bayes for Text Classification Problems
  9. Laplace Smoothing
  10. How to overcome the problem of small numbers
  11. Bias Variance Tradeoff
  12. Model Interpretability
  13. How Imbalanced data impacts Naive Bayes Models
  14. Impact of Outliers
  15. How Naive Bayes handles numeric features

Section 5: Support Vector Machines

  1. SVM Intuition
  2. Alternate interpretation
  3. SVM Part 1 - The Objective
  4. SVM Part 2 - Equation of Hyperplane from Basic Geometry
  5. SVM Part 3 - Why use -1 and +1 instead of 1 and 0
  6. SVM Part 4 - Understanding the objective formulation
  7. SVM Part 5 - Soft margin classifier and slack variables
  8. SVM Part 6 - Kernels and Mapping Function
  9. SVM Part 7 - Primal vs Dual Form
  10. SVM Part 8 - Support Vector Regression
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