📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Applied ML Fundamentals (No Math Overload), from foundations to a certified capstone project.
Framing
• Features, labels and the supervised/unsupervised split in plain, worked terms
• The train/validation/test split and why a single accuracy figure misleads
• Data leakage — the single most common reason a "great" model fails in production
Data Prep
• Cleaning, encoding categoricals and scaling features with pandas and scikit-learn
• Handling missing values and class imbalance without inventing signal
• Building a scikit-learn Pipeline so preprocessing cannot leak from test into train
Algorithms
• Linear and logistic regression as the interpretable baseline to always beat first
• Decision trees and random forests, and the overfitting a single deep tree invites
• k-means clustering for unlabelled data and the k-selection trap
Evaluation
• Accuracy versus precision, recall, F1 and ROC-AUC — matching the metric to the problem
• Cross-validation instead of trusting one lucky split
• Diagnosing overfitting and underfitting from the gap between train and validation scores
Delivery
• Persisting a trained model with joblib and loading it for repeatable inference
• Wrapping the model in a simple prediction service others can call
• Watching for input drift so a model that was accurate stays accurate
Earn government-registered certification in Applied ML Fundamentals (No Math Overload)
e-Certificate and e-Marksheet issued on successful completion.
