Part 2: Core Machine Studying Ideas (4–6 months)
Objective: Perceive machine studying idea and begin constructing fashions.
Supervised Studying:
Linear Regression, Logistic Regression, Determination Timber, Random Forests.
Sources: Coursera Machine Studying by Andrew Ng, Palms-On Machine Studying with Scikit-Be taught, Keras & TensorFlow by Aurélien Géron.
Unsupervised Studying:
Ok-Means, PCA, Hierarchical Clustering.
Sources: Similar as above + YouTube channels like StatQuest.
Analysis Metrics:
Precision, Recall, F1 Rating, Confusion Matrix, AUC-ROC.
Mannequin Optimization:
Be taught cross-validation, hyperparameter tuning (GridSearch, RandomSearch).
Part 3: Deep Studying (6–9 months)
Objective: Transition from classical ML to constructing neural networks.
