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
Day 8: Pandas, Matplotlib and Seaborn
Day 9: Scikit learn
Day 10: Importing Dataset, Taking care of missing data, Encoding Data and Train Test Split
Day 11: Linear Regression (Simple, Multiple and Polynomial)
Day 12: Support Vector Regression
Day 13: Decision Tree Regression and Random Forest Regression
Day 14: Evaluation of a Regression Model performance and Model Selection
Day 15: Logistic Regression and K-Nearest Neighbours
Day 16: Support Vector Machine and Kernel SVM
Day 17: Naive Bayes
Day 18: Decision tree and Random Forest
Day 19: Evaluation of a Classification Model performance and Model Selection
Day 20: K-means and Hierarchial Clustering