Statistics
- Need for Statistics
- Application of Statistics
- Mean, Standard Deviation, Variance
- Central Limit Theorem
- Sample vs Population
- Hypothesis Testing
- T-tests
- Z-tests
- Assumptions of regression analysis
Python for Data Science and Machine Learning
- Power of Python
- Python types and environment setup
- Basics of Python
- Various libraries for DS and ML
- Numpy
- Pandas
- Scipy
- Data visualization
- matplotlib
- Seaborn
ML part
- Need, Application and opportunity of ML
- Data requirements
- Data import handling
- Data Cleaning (Blank, NaN, Scaling)
- Basics of Regression
- Algorithms for Regression
- Linear regression (Mathematics)
- Multivariate regression (Mathematics)
- Polynomial regression (Mathematics)
- SVM regression (Mathematics)
- Decision Tree (Mathematics)
- Random Forest (Mathematics)
- The standard approach for Regression
- Application of all the algorithms (Case Study)
- Performance evaluation and selection
- Basics of Classification
- Algorithms for Regression
- SVM Classification (Mathematics)
- Decision Tree (Mathematics)
- Random Forest (Mathematics)
- KNN (Mathematics)
- A standard approach for Classification
- Application of all the algorithms (Case Study)
- Performance evaluation and selection
Bonus
- Additional case study 1 (Share Price Prediction)
- Additional case study 2 (Flower classification)
- Additional case study 3 (Cyber Security)
