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Machine Learning
Machine Learning
Curriculum
3 Sections
15 Lessons
3 Weeks
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Week 1
2
1.1
Classification Models – Logistic Regression
1.2
Classification Evaluation Metrics
Week 2
7
2.1
Classification Models – KNN and Decision Trees
2.2
Classification Models – SVM, Naïve Bayes
2.3
Hyperparameter Tuning
2.4
Ensemble Learning – Bagging and Random Forest
2.5
Ensemble Learning – Boosting Techniques
2.6
Unsupervised Learning
2.7
Unsupervised Learning – Clustering
Week 3
6
3.1
Dimensionality Reduction Techniques
3.2
Gradient Descent Techniques
3.3
Handling Imbalanced Data (SMOTE, Class Weights)
3.4
Saving Models (Joblib)
3.5
Model Deployment
3.6
A/B Testing
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