Phase 1
ML Foundations
01
EDA & Statistics
distributions · outliers · correlation · hypothesis testing
02
Linear Regression
OLS · gradient descent · R² · residual analysis
03
Logistic Regression
sigmoid · log-loss · decision boundary · confusion matrix
04
Regularization
Ridge · Lasso · ElasticNet · bias-variance tradeoff
05
Advanced Regression
polynomial features · interaction terms · non-linear fits
Phase 2
Ensemble & Advanced
06
Decision Trees
information gain · Gini · pruning · depth control
07
Random Forest
bagging · feature subsets · OOB error · feature importance
08
Boosting
AdaBoost · Gradient Boosting · XGBoost · learning rate
09
Support Vector Machines
maximum margin · kernel trick · soft margin · SVR
10
Naive Bayes
Bayes theorem · conditional independence · text classification
Phase 3
Deep Learning
11
Clustering
K-Means · DBSCAN · hierarchical · silhouette score
12
PCA
eigenvectors · variance explained · dimensionality reduction
13
Model Selection
cross-validation · AIC · BIC · hyperparameter tuning
14
Neural Networks
perceptron · backprop · activations · dense layers
15
Convolutional Neural Networks
kernels · feature maps · pooling · transfer learning