Post Graduate Certificate in Data Science & Machine Learning

No prior coding experience is required to successfully complete this program. You should, however, have exposure to high school mathematics. The course contains reading material and lectures on selected topics which bridge the gap between high school math

Clustering: K-means

Decision Trees

Developing clear understanding of eigenvalues and eigenvectors with applications in ML

Developing code to create insightful visualization using Matplotlib, Pandas, Seaborn, and R

Dimensionality reduction: Principal component analysis (PCA)

Gradient Calculus

How projection operators work to project data in various ML algorithms

Linear and kernel Support Vector Machines (SVM)

Linear and logistic regressions

Machine learning concepts and lifecycle of a ML project

Neural Networks

Optimization algorithms required in machine learning

Preparing, analyzing, and interpreting basic inferential statistics results

Special types of matrices with their important properties

Statistical techniques required to analyze data

Statistical thinking needed for data analysis

Symmetric and orthogonal matrices

Techniques from multivariable calculus useful in data analytics and machine learning

Testing of hypothesis

Vector spaces

Vector subspaces

Writing code using Pandas and R to work with data and perform data exploration tasks

Writing code using common Python & R functionality

Creating visualization of various linear transformations