Machine Learning notebooks for refreshing concepts.
Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet.
Feature Selection: Imputing missing values, Encoding, Binarizing.
Feature Scaling: Min-Max Scaling, Normalizing, Standardizing.
Feature Extraction: CountVectorizer, DictVectorizer, TfidfVectorizer.
Linear & Multiple Regression
c. Assumptions in Linear Regression: Assumptions in Linear Regression, Dummy Variable Trap
d. Linear Regression using Scikit-learn: Simple and Multivariable Regression using Scikit-learn.
Backward Elimination: Method of Backward Elimination, P-values.
Logistic Regression
Regularization