Supervised ML Algorithms
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Section 1: K Nearest Neighbors
- K Nearest Neighbours Intuition
- When can kNN not work
- Distance Measures
- Cosine Similarity
- KNN for Regression Problems
- Weighted kNN
- Voronoi Diagram
- Measuring Effectiveness
- Overfitting vs Underfitting
- K Fold Cross Validation
- How to spot underfitting and overfitting areas graphically
Section 2: KD Tree and LSH
- Binary Search Tree (BST)
- Constructing the Tree - Part 1
- Constructing the Tree - Part 2
- How to navigate the KD Tree
- Drawbacks
- Introduction to Hashing
- LSH - Part 1
- LSH - Part 2
Section 3: Decision Trees
- Introduction to Decision Trees
- Example of reading a Decision Tree
- Entropy Part 1 - Understanding the Formula
- Entropy Part 2 - Example calculation from dataset
- Entropy Part 3 - Role in Building Decision Trees
- Information Gain
- GiniImpurity
- Constructing the Decision tree
- How to split numeric features
- Dealing with categorical features with many possible values
- How to avoid overfitting and Hyperparameters - Part 1
- How to avoid overfitting and Hyperparameters - Part 2
- Decision Trees for Regression Problems
Section 4: Naive Bayes
- What is conditional probability
- Basic ideas
- Bayes Theorem Proof
- Bayes Theorem Math Workout-Part 1
- Bayes Theorem Math Workout-Part 2
- Naive Bayes Algorithm
- Naive Bayes Calculations Example
- Naive bayes for Text Classification Problems
- Laplace Smoothing
- How to overcome the problem of small numbers
- Bias Variance Tradeoff
- Model Interpretability
- How Imbalanced data impacts Naive Bayes Models
- Impact of Outliers
- How Naive Bayes handles numeric features
Section 5: Support Vector Machines
- SVM Intuition
- Alternate interpretation
- SVM Part 1 - The Objective
- SVM Part 2 - Equation of Hyperplane from Basic Geometry
- SVM Part 3 - Why use -1 and +1 instead of 1 and 0
- SVM Part 4 - Understanding the objective formulation
- SVM Part 5 - Soft margin classifier and slack variables
- SVM Part 6 - Kernels and Mapping Function
- SVM Part 7 - Primal vs Dual Form
- SVM Part 8 - Support Vector Regression