Day 1: Datatypes and Strings in Python
Day 2: Operations in Python
Day 3: Lists, Tuples, Dictionaries in Python
Day 4: Conditional Statements
Day 5: Loops and Functions in Python
Day 6: File handling and Module Handling
Day 7: Numpy, Pandas, Matplotlib, Scikit-learn, and Seaborn
Day 8: Importing Dataset, Taking care of missing data, Encoding Data and Train Test Split
Day 9: Linear Regression (Simple, Multiple and Polynomial)
Day 10: Support Vector Regression
Day 11: Decision Tree Regression and Random Forest Regression
Day 12: Evaluation of a Regression Model performance and Model Selection
Day 13: Logistic Regression and K-Nearest Neighbours
Day 14: Support Vector Machine and Kernel SVM
Day 15: Naive Bayes
Day 16: Decision tree and Random Forest
Day 17: Evaluation of a Classification Model performance and Model Selection
Day 18: K-means and Hierarchial Clustering