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SECTION 1: K Nearest Neighbors
K Nearest Neighbours Intuition
When can kNN not work
Download Resources
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
Course Review
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
Gini Impurity
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