MA477 - Theory and Applications of Data Science
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Course CalendarCourse Guide
Lecture Notes
Lesson 1 -General OverviewLesson 2 -Intro to NumPy
Lesson 3 -Intro to Pandas
Lesson 4 -Matplotlib
Lesson 5 -Seaborn
Lesson 6 -KNN Regressor
Lesson 7 -Linear Regression
Lesson 8 -Cross-Validation
Lesson 9 -Shrinkage Methods
Lesson 10 -KNN Classifier
Lesson 11 -Logistic Regression
Lesson 12 -Lab
Lesson 13 -Naive Bayes Classifier
Lesson 14 - n-Grams & Regular Expressions
Lesson 15 - Tokenization & Speech Tagging
Lesson 16 - Classifying Names by Gender
Lesson 17 - Decision Trees
Lesson 18 - Random Forests
Lesson 19 - Voting Classifier & Bagging
Lesson 20 - Boosting Models (AdaBoost)
Lesson 21 - K Means Clustering
Lesson 22 - Data Pre-processing with K-Means Clustering
Lesson 22 - Image Segmentation with K-Means Clustering
Lesson 23 - Principal Component Analysis (PCA)
Lesson 24 - Artificial Neural Networks (Part 1)
Lesson 25 - Artificial Neural Networks (Part 2)
Lesson 26/27 - A 2-Layer Neural Network
Video Lectures
Lecture | Video Link |
---|---|
Decision Trees for Classification and Regression | Lesson 17 - Decision Trees |
Ensemble Models: Random Forests | Lesson 18 - Random Forests |
Ensemble Models: Voting Classifier | Lesson 19 - Voting Classifier |
Ensemble Models: Adaptive Boosting | Lesson 20 - Adaptive Boosting |
Clustering Methods: K-Means Clustering | Lesson 21 - K Means Clustering |
Data Pre-processing with K-Means Clustering | Lesson 22 - Applications of K-Means Clustering (Part 1) |
Image Segmentation with K-Means Clustering | Lesson 22 - Applications of K-Means Clustering (Part 2) |