Machine Learning & Data Science (Class 8)
Master Data Cleaning, Min-Max Normalization, Linear Regression, Classification, Decision Trees, KNN majority voting, and K-Fold Cross-Validation!
Course Chapters & Learning Modules
Total 10 interactive modules with guided checkpoint challenges.
Data Cleaning & Normalization
Transform raw noisy datasets with missing values into standardized 0-to-1 normalized features for ML models.
Regression: Predicting Numerical Values
Fit linear trendlines (Y = mX + c) through scatter plots to forecast exam scores and weather temperatures accurately.
Classification: Sorting into Categories
Calculate decision boundaries that separate emails into Spam vs Inbox and medical scans into healthy categories.
Decision Trees: The Smart Question Flowchart
Explore transparent, interpretable Decision Trees that split data step-by-step through logical question branches.
K-Nearest Neighbors (KNN): Nearest Friends Rule
Classify mystery data points by querying the majority vote of K nearest neighbors in vector space.
Confusion Matrix: Measuring True vs False
Audit model reliability using 2x2 matrices tracking True Positives, True Negatives, Precision, and Recall.
Identifying Bias in Datasets
Eliminate demographic bias by balancing speech and vision datasets across accents, ages, and genders.
K-Fold Cross Validation: Robust Testing
Rotate datasets through 5 test folds to eliminate lucky test flukes and verify true model generalization.
The Data Scientist Mindset
Unlock real-world patterns in healthcare, climate change, and education to protect communities and save lives.
Final Master Class: Full Course Recap & 10-Question Mastery Quiz
Review all Class 8 Data Science concepts and test your mastery with the comprehensive 10-question final quiz!
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