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Notes
Notes
Machine Learning
Introduction
What is ML ?
Types of ML
Supervised Learning
Linear Regression
Meaning of 'Linear'
Meaning of 'Regression'
Linear Regression
Probabilistic Interpretation
Convex Function
Gradient Descent
Polynomial Regression
Data Splitting
Cross Validation
Bias Variance Tradeoff
Regularization
Regression Metrics
Assumptions
Logistic Regression
Binary Classification
Log Loss
Regularization
Log Odds
Probabilistic Interpretation
K Nearest Neighbors
KNN Introduction
KNN Optimizations
Curse Of Dimensionality
Bias Variance Tradeoff
Decision Tree
Decision Trees Introduction
Purity Metrics
Decision Trees For Regression
Regularization
Bagging
Random Forest
Extra Trees
Boosting
AdaBoost
Gradient Boosting Machine
GBDT Algorithm
GBDT Example
Advanced GBDT Algorithms
XgBoost
LightGBM
CatBoost
Support Vector Machine
SVM Intro
Hard Margin SVM
Soft Margin SVM
Primal Dual Equivalence
Kernel Trick
RBF Kernel
Support Vector Regression
Naive Bayes'
Naive Bayes Intro
Naive Bayes Issues
Naive Bayes Example
Unsupervised Learning
K Means
K Means
Lloyds Algorithm
K Means++
K Medoid
Clustering Quality Metrics
Silhouette Score
Hierarchical Clustering
Hierarchical Clustering
DBSCAN
DBSCAN
Gaussian Mixture Model
Gaussian Mixture Models
Latent Variable Model
Expectation Maximization
Anomaly Detection
Anomaly Detection
Elliptic Envelope
One Class SVM
Local Outlier Factor
Isolation Forest
RANSAC
Dimensionality Reduction
PCA
t-SNE
UMAP
Feature Engineering
Data Pre Processing
Categorical Variables
Feature Engineering
Data Leakage
Model Interpretability
ML System
Data Distribution Shift
Retraining Strategies
Deployment Patterns
Maths
Probability
Introduction to Probability
Conditional Probability
Independence of Events
Cumulative Distribution Function
Probability Mass Function
Probability Density Function
Expectation
Moment Generating Function
Joint & Marginal
Independent & Identically Distributed
Convergence
Law of Large Numbers
Markov's Inequality
Cross Entropy & KL Divergence
Parametric Model Estimation
Statistics
Data Distribution
Correlation
Central Limit Theorem
Confidence Interval
Hypothesis Testing
T-Test
Z-Test
Chi-Square Test
Performance Metrics
Linear Algebra
Vector Fundamentals
Matrix Operations
Eigen Value Decomposition
Principal Component Analysis
Singular Value Decomposition
Vector & Matrix Calculus
Vector Norms
Hyperplane
Calculus
Calculus Fundamentals
Optimization
Gradient Descent
Newton's Method
Notes
Machine Learning
Introduction
Introduction
Introduction To Machine Learning
less than a minute
Classical Machine Learning | Full Course Videos
End of Section
What is ML ?
What is Machine Learning ?
Types of ML
Types of Machine Learning