CURRICULUM OVERVIEW
Comprehensive Learning for Modern Machine Learning
Our curriculum provides a balanced mix of theory and hands-on practice. You’ll work through real-life scenarios to build, test, and optimize machine learning models, giving you the skills to tackle challenges in a variety of industries.
- Probability
- Covariance and correlation
- Graphically displaying single variable
- Measures of spread and location
- Bayes’ Theorem
- Supervised and unsupervised learning.
- Python libraries for machine learning.
- Applications of machine learning.
- 04 Merits of using python with ML.
- Training, testing, forecast and predicting.
- Program the best fit slope and best fit line.
- R squared and coefficient of determination theory.
- Model evaluation methods.
- Handling non-numerical data for ML.
- K-means with titanic dataset.
- K-means from scratch in python.
- Hierarchical clustering with mean shift introduction.
