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

Creating visualization of various linear transformations

Developing clear understanding of eigenvalues and eigenvectors with applications in ML

How projection operators work to project data in various ML algorithms

Special types of matrices with their important properties

Vector spaces

Vector subspaces

Symmetric and orthogonal matrices

Statistical techniques required to analyze data

Statistical thinking needed for data analysis

Techniques from multivariable calculus useful in data analytics and machine learning

Optimization algorithms required in machine learning

Gradient Calculus

Testing of hypothesis

Writing code using common Python & R functionality

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

Preparing, analyzing, and interpreting basic inferential statistics results

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

Machine learning concepts and lifecycle of a ML project

Dimensionality reduction: Principal component analysis (PCA)

Linear and logistic regressions

Linear and kernel Support Vector Machines (SVM)

Clustering: K-means

Decision Trees

Neural Networks