APPROACHING ANY MACHINE LEARNING PROBLEM

The repository contains a list of projects which I have worked on while reading the book Approaching Any Machine Learning Problem.

📚NOTEBOOKS:

01. SUPERVISED AND UNSUPERVISED LEARNING

• The Supervised Unsupervised notebook contains all the fundamental dependencies required to understand Supervised and Unsupervised Learning, Cross Validation, Decision Trees, Classification and Regression.

02. EVALUATION METRICS

• The Evaluation Metrics notebook contains implementation of classification metrics such as accuracy, precision, recall, sensitivity and specificity and regression metrics such as mean absolute error, mean squared error, r2 and mcc score.