CERTIFICATION IN Data Science & MACHINE LEARNING

[Note: The program comes in two formats: weekday format for fresh graduates and weekend format for working professionals.]

COURSE LEARNING

Upon completion of this course, you will be able to:

MODULE 1 - PYTHON PROGRAMMING

Introduction to programming and Python Data types | Operators |Expressions | Conditionals | Loops | Functions | Recursion | OOPS Concepts Data Structures | Numpy | Pandas | Error Handling | Scikit-learn library

MODULE 2 – Data Analysis

Introduction to EDA | Deal with missing data |Data Cleaning | Data visualization

MODULE 3 - SUPERVISED LEARNING

Introduction to Supervised Learning and Performance Analysis | Logistics Regression | K-NN | Data Balancing Techniques and Applications | Classification and Regression Trees | Decision Trees and Random Forest | Support Vector Machines (SVM)

MODULE 4 - UNSUPERVISED LEARNING

Introduction to Unsupervised Learning Clustering | K-Means Clustering | Hierarchical Agglomerative Clustering and DBSCAN

MODULE 5 - DEEP LEARNING

Introduction to Deep Neural Network

Convolutional Neural Network | Implementation of Convolutional Neural Network (ConvNet/CNN) | Architectures ConvNet/CNN and Demonstration

Course Project

·         IMAGE CLASSIFICATION

·         HOUSE PRICE PREDICTION

Course Duration and Learning pattern

Duration of Certificate – 4 Week

Classes’ distribution – 50% Theory Classes and 50% Implementation

Key Highlights

Assignments Students will receive timely assignment related with course topics.

Discussions Apart from classes you will get an extra session for topic discussion.

Quizzes Topic quizzes for learning with fun.

Group Requirements/Group Projects apart from session project you can work on group projects also.  

          Attendance Policy 85%

          Instructor Feedback One can directly report about the feedback of instructor.