Master Diploma in AI and ML

Course Description

MDAIML program is comprised of 6 levels:

Foundational Courses – Statistics and Linear Algebra (20 hrs) Foundational Courses – Programming (40 hrs) Data Wrangling & Visualisation (40 hrs) Machine Learning (150 hrs) Neural Networks and Deep Learning ( 100 hrs) Capstone Project (50 hrs)

Training Methodology

Expert trainers in the respective fields, handle each level. Our faculty consists of experienced instructors who possess hands-on experience with real time concepts Students can access the State-of-the -Art labs 24×7. Our training crew give personal attention to every student through the theory and practicals.

Pre–Requisites Plus Two and above.

Anyone interested in having a career in Machine Learning and Artificial Intelligence can pursue this course.

Modes Off Training

In-house classes with individual sessions for each level. The batches are scheduled as per the convenience of the students. Freshers, College going students and working professionals can attend.

Duration: 400 Hrs

SYLLABUS

Fundamentals – Statistics and Linear Algebra (20 hrs)

Basics of Statistics, Sampling Techniques Basics of Probability Different distributions, Bayes Theorem Inferential Statistics, Estimation Hypothesis Testing Linear Algebra, Matrix operations Transformations

Fundamentals – Programing (40 hrs)

Python – Installation and Setup Operators and Control Structures Data Types in Python Functions, Modules Object Oriented Programming Packages for Data Science – Matplotlib, Numpy, Scipy, Pandas, Scikit learn, etc.

Data wrangling and Visualization (40 hrs)

Reading different file formats Data clean-up, investigation, formatting Data Exploration & Analysis Dealing with missing values Basics of Data Visualisation Data Visualisation using packages

Machine Learning (150 hrs)

Introduction to ML Supervised Learning Classification and Regression Ensemble Methods Unsupervised Learning Clustering Introduction to Re-inforcement Learning

Neural Networks and Deep Learning (100 hrs)

Neural Networks Basics Shallow Neural Networks Deep Neural Networks Parameters and Hyperparameters

Capstone Project (50 hrs)