Hashing, SQL aggregation/functions, ML begins: Feature Engineering

go deeper than Week 2's version

CampusX "100 Days of ML" (YouTube): Complete Case Analysis, Mean/Median imputation, Arbitrary value imputation, End of Distribution imputation, Missing Indicator

Aggregates in SQL, SQL Clauses, GROUP BY, MIN/MAX/SUM/AVG, COUNT, HAVING + Quiz + solve: Unique Subjects per Teacher, User Follower Count, Large Classes, Email Duplicates, Updated Bank Balances, Football Team Scores

CampusX: Categorical Encoding (One-Hot Encoding, Label Encoding, Ordinal Encoding, Target Encoding) + Feature Scaling (Standardization vs MinMax Normalization)

Numeric/NULL functions (ROUND, ABS, GREATEST, LEAST, IFNULL, COALESCE) + CASE statements + solve: Call Count Between Pairs, Find Continuous Ranges in Logs, First Login Analysis, Instant Food Delivery

CampusX: Outlier detection & handling (Z-score treatment, IQR method, Winsorization) + matching StatQuest videos + clean and feature-engineer a new raw dataset end-to-end

Build an end-to-end scikit-learn pipeline (ColumnTransformer, StandardScaler, OneHotEncoder, custom Imputers) on Housing dataset.

Quant M7 Percentage (Basic & Advance) + Verbal: Vocabulary and Grammar (Basic & Advance)

redo 2 hashing problems from memory

Andrew Ng ML Spec Course 1 Week 1; StatQuest Feature Engineering exercises; document outlier handling strategies.

Logical: Cubes (Basic & Advance) + Andrew Ng Machine Learning Specialization (Coursera) Course 1 Week 1 + String Functions (CONCAT, SUBSTRING, REPLACE, LIKE)