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Full Stack AI Developer Roadmap
·
Module 6
Machine Learning Fundamentals
1
Data Splitting Strategies
Split data into training, validation, and test sets appropriately
2
Linear Regression: Implementation and Interpretation
Implement linear regression and interpret coefficients
3
Logistic Regression for Binary Classification
Implement logistic regression for binary classification
4
Decision Trees and Feature Importance
Train decision trees and explain feature importance
5
Ensemble Models: Random Forest & Gradient Boosting
Build ensemble models: Random Forest (bagging) and Gradient Boosting
6
Confusion Matrix & Performance Metrics
Evaluate models using confusion matrix, precision, recall, F1-score
7
ROC Curves and AUC for Model Evaluation
Evaluate models using ROC curves and AUC metric
8
Cross-Validation and Hyperparameter Tuning with Grid Search
Perform cross-validation and tune hyperparameters using grid search
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Data Analysis and Visualization
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Deep Learning Foundations