- Duration: 1 Month
- Schedule: Saturdays & Sundays (3 hours per day)
- Total Hours: 24 Hours
1. Introduction to ML course:
- Overview of topics to be covered in the course
- Motivation for the course
- Expected Outcomes of the course
- Brief about software/tools
2. Exploratory Data Analysis:
Focus on pre-processing, missing values, working with outliers, demo on EDA
- Summary and skewness, Box-plot, covariance and correlation, encoding, scaling and normalisation, multicollinearity.
- Univariate/Bi Variate/Multi Variate Analysis
3. Logistic Regression:
- What is the need of LR?
- Goodness of fit measures
- What is the need of LR?
4. Decision Trees
- Construction of decision tree for classification
- Random forest classifier
5. Dimension Reduction Techniques
Principal component analysis (PCA)
6. Ensemble Methods:
- Bagging
- Boosting
- AdaBoost
- Applications of Ensemble methods
7. Capstone Project.
Students will be presented with a real-time industry scenario and accompanying datasets. Your task will be to develop a comprehensive solution, adhering to professional standards in architecture design, data pipelines, coding best practices, and culminating in a demonstrable project for evaluation.
8. Mock Interviews.
You'll be interviewed by experienced Machine Learning practitioners.
9. Brainstorming session with AI Leaders
Strategic Dialogue with AI Visionaries.
