Duration: 6 months

Content:

• Introduction to Machine Learning
• Getting Started with Scikit-learn
• Installation & setup
• Dataset loading (built-in datasets)
• Understanding features & labels
• Handling missing values
• Encoding categorical data:
  • Label Encoding
  • One-Hot Encoding
• Feature scaling:
  • Standardization
  • Normalization
• Train-test split
• Linear Regression
• Multiple Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• Evaluation metrics:
  • MAE, MSE, RMSE
  • R² score 
• Support Vector Machine (SVM)
• Confusion Matrix
• Accuracy, Precision, Recall, F1-score
• Cross-validation
• Bias vs Variance
• Overfitting & Underfitting
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN (basic)
• Elbow method
• Feature selection techniques
• PCA (Principal Component Analysis)
• When and why to reduce dimensions
• Hyperparameter tuning:
  • Grid Search
  • Random Search
• Pipeline creation

General Course (Offline batch Only): ₹35000

Private Classroom (Online / Offline): ₹35000

Enroll Now

Enroll Now