Post Graduate Certificate in Data Science & Machine Learning

Course 1: Programming for AI & Data Analytics- In this course, you will learn and develop the necessary knowledge and skills for the most prevalent programming languages in the Data Science domain: Python and R. You will learn Python and R basics, data structures, programming constructs, and how to work with data using key Python packages Pandas, Matplotlib, Seaborn, and R. Course 2: Linear Algebra Basics- This course provides all conceptual knowledge from Linear Algebra required in the domain of Data Science and Machine Learning. First, you will be introduced to real vector spaces and then to the linear transformations and their representations in terms of matrices. You will learn the importance of eigenpairs in machine learning and various concepts such as orthogonality and projection. Course 3: Foundations of Data Analytics- This course is divided into two parts. In the first part, you will be introduced to the concepts from probability theory and statistics with strong relevance in machine learning and data science. In the second part, youll explore gradient calculus and numerical optimization algorithms like gradient descent, stochastic gradient descent, etc.Course 4: Machine Learning Techniques for AI & Data Analytics- In this course, you will learn Machine Learning (ML) concepts and algorithms. This course will cover machine learning concepts and popular supervised and unsupervised learning techniques.Course 5: Selected projects from Kaggle- As a data scientist or ML engineer you are supposed to implement the selected machine learning project and compete with your peer-group. Through this industry style project, you will learn to:- - Adjust to team work environments and gain industry-like experience in a machine learning project - Familiarize oneself with Kaggle platform and understand the objective of the analytics problem in the project - Download data and produce first prototype of the ML model - Experiment with their ML model to improve its performance - Submit the project and share the learnings as a t