Curriculum
7 Sections
41 Lessons
20 Weeks
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Introduction to Machine Learning
4
1.1
What is Machine Learning
1.2
Supervised Machine Learning
1.3
Unsupervised Machine Learning
1.4
Business Applications of Machine Learning
Machine Learning Lifecycle
5
2.1
Problem Framing
2.2
Data Collection
2.3
Model Evaluation
2.4
Deployment & Monitoring
2.5
Model Building
Linear Regression (Supervised Regression)
9
3.1
Business Use Cases (Sales Forecasting, Price Prediction, etc.)
3.2
Model Assumptions
3.3
Model Formula (Y = β0 + β1X + … + ε)
3.4
Coefficients Interpretation
3.5
Evaluation Metrics (R², MAE, MSE, RMSE)
3.6
Overfitting & Underfitting
3.7
Regularization (L1, L2) (Short Overview)
3.8
Full Python Code Walkthrough (sklearn and Statsmodels)
3.9
End to End Production Ready application building
Logistic Regression (Supervised Classification)
10
4.1
Business Use Cases (Churn, Fraud, Lead Conversion)
4.2
Sigmoid Function & Probability Output
4.3
Odds Ratio & Log-Odds Interpretation
4.4
Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
4.5
Confusion Matrix
4.6
Threshold Tuning
4.7
Multicollinearity (VIF)
4.8
Full Python Code Walkthrough (sklearn and Statsmodels)
4.9
End to End Production Ready application building
4.10
Business Use Cases (Customer Segmentation, Market Segmentation)
K-Means Clustering (Unsupervised Learning)
7
5.1
Clustering Objective & Centroid Logic
5.2
Choosing K (Elbow Method, Silhouette Score)
5.3
Standardization before Clustering
5.4
Interpretation of Clusters
5.5
Limitations of K-Means
5.6
Full Python Code Walkthrough (sklearn)
5.7
End to End Production Ready application building
Model Comparison & Selection
3
6.1
Regression vs Classification vs Clustering
6.2
When to use what?
6.3
Business scenario based model selection
Common Machine Learning Mistakes and Best Practices
3
7.1
Overfitting
7.2
Underfitting
7.3
Importance of validation
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