Master Diploma in Artificial Intelligence & Machine Learning (MDAIML)

The Master diploma in Artificial Intelligence & Machine learning combines academic
excellence and industrial insight to hone one’s skills to design relevant Data Science
solutions to solve the problems faced by the world. Artificial Intelligence, Machine
Learning and Data Science are quickly becoming integrated into every imaginable
industrial sector. With this highly useful and versatile tool into one’s arsenal will open up
job opportunities for you like never before. Whether you are a fresher just out of college,
or a seasoned professional with years of experience, the field of AIML is welcoming and
rooting for you. The course is designed to introduce you to and make you equipped in all
aspects of AIML from foundational topics like statistics and probability to deep neural
networks. Apart from classroom sessions and hands-on exercises the course includes
15+ industry relevant projects.

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.

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



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

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
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)

× How can I help you?
Copy link
Powered by Social Snap