PG Program in Machine Learning &
Artificial Intelligence

Learn from IIBM Institute of Data Science that makes you job-ready and build a rewarding career

India’s top ranked program  | 9 Months | Online Mentorship

Fees: INR 70,000 + GST

Batch start: 15th august

Enquire: +91-9808811409

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Why we’re ranked #1

Comprehensive
Curriculum

  • Designed for working professionals
  • Modelling, Forecasting, Data Science, Analytics, Data visualisation and more

Personalised
Mentorship

  • Designed for working professionals
  • Modelling, Forecasting, Data Science, Analytics, Data visualisation and more

Dedicated Career
Assistance

  • Job opportunities shared by 350+ companies
  • 7000+ Alumni transitions with 48% avg salary hike

Join India's #1 Machine Learning & Artificial Intelligence Program

Online Mentorship | 09 Months

Module 1: Fundamentals of Data Analytics

2 Hours

 Insurance, Automobile, Retail , Banking, Retial, Marketing, Aviation, Defence, Social Services, Computer Vision

Insights, Reports, Historical Performance, Trend, Visualization

Descriptive, Diagnostic, Predictive, Prescriptive, Exploratory 

Difference between AI, Machine Learning, Deep Learning and Data Science

Module 2: Basics concepts in Statistics for Data Analytics

4 Hours

Undertand difference between Population vs Sample, importance of statistical concepts in data science and ML models

Know the foundation principal in statistics – Central Limit Theorem

Understand the importance of Mean, Medium, Mode of a variable

Understand the importance of Variance, Standard Deviation of a variable

Different scales of measuring data – Nominal, Ordinal, Interval, Ratio

Application of central tendencies for data analysis

Usage of correlation, regression concepts for data analysis

Module 3: Advanced concepts in Statistics for Data Analytics

6 Hours

Understand different types of data distribuitions – Uniform, Binomial, Poisson, Normal, Logarithmic, ExponentialHypoth

Learn to Perform Null Hypothesis and p-value to find the significant variables

Learn to perform t-test, z-test to measure the variance between the means of two samples or population

Learn techniques like ANOVA (1-way, 2-way, w/o replication), ANCOVA, f-test to compare the variance betweeen variables

Perform Chi-square test to evaluate distribution of sample same as expected population under study

Introduction to probability

Types of events

Marginal Probability

Baye’s Theorem

Module 4: Python essential for Data Science

15 Hours

What is Python?

Why is Python essential for Data Science?

Versions of Python

How to install Python

Anaconda Distribution

How to use Jupyter Notebooks

Command line basics

GitHub overview

How to execute Python scripts from command line

Python Data Types

Programming Concepts

Python, Operators

Conditional Statement, Loops

Lists, Tuples, Dictionaries, Sets

Methods and Functions

Errors and Exception Handling

Object Oriented Programming in Python

Modules and Packages

NumPy overview

Arrays & Matrices

NumPy basic operations, functions

NumPy for Data Analysis

Importing Pandas

Pandas overview

Pandas Series and Data Frames

Dealing with missing data

GroupBy, Merging, Concatenating and Joining

Data Input & Output

Why visualize data?

Importing MatplotLib

Chart: Line Chart, Bar Charts and Pie Charts

Plotting from Pandas object

Object Oriented Plotting: Setting axes limits and ticks

Multiple Plots

Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors

Importing Seaborn

Seaborn overview

Distribution and Categorical Plotting

Matrix plots & Grids

Regression Plots

Style & Color

Review Session

Module 5: Data Science with Python

11 Hours

Key Terms in Data Science

Introduction to Supervised Learning,Unsupervised Learning

What is Reinforcement Learning?

Regression

Classification

Data Science Life Cycle

Data Science in cloud

Univariate

Bivariate

Multivariate

Missing Values

Outliers treatment

imbalance Data Handeling

Standardization / Normalization 

Project1

Data Science Library
Scikit learn

Module 6: Machine Learning with Python

25 Hours

Data Preparation
Stationary Data
Trends /Seasonility
ARIMA Model
SARIMA & Other Models
Project 11
Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function
Tensor Flow Introduction (1 Hour)
Building Artificial Neural Networks (ANN) with Python 1 Hours
Project 5
Introduction to OpenCV
Learning Basic Image manipulations Introduction to CNN
CNN Institution
Convolution Operation Pooling, Flattening Building a CNN using Python
Project 6 (1 Hour)
Introduction to RNN
Sequence prediction using RNN
Introduction to LSTM
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data
Project 7
Deploying an Scikit-learn model [train, deploy]
Deploying a Keras model [train, deploy]
Autoencoders and Generative Adversarial Networks

Using Azure ML Studio

 

Module 7: Artificial intelligence with Python

30 Hours

Basics of NLP
Removing Stop Words
Stemming & lemmatization
Parts of speech tagging
TFIDF vectorizer
Senmiment Analysis
Text Classification with Linear Models
Language Modelling with Probabilistic Graphical Models and Neural Networks
Word Embeddings and Topic Models
Machine Translation and Sequence-To-Sequence Models
Project: Building a Chatbot
Basics of Bayesian probability theory
Expectation-Maximization algorithm and Gaussian Mixture Models
Variational Inference and Latent Dirichlet Allocation
Markov Chain Monte Carlo
Bayesian Neural Networks and Variational Autoencoders
Gaussian Processes and Bayesian Optimization
Project: Forensics to generate images of suspects
Introduction to Reinforcement Learning
Model-Based Reinforcement Learning (Dynamic Programming)
Model-Free Reinforcement Learning (SARSA, Monte Carlo, Q-Learning)
Approximate and Deep Reinforcement Learning (Deep Q-Learning)
Policy Gradient Reinforcement Learning
Advanced Topics on Exploration and Planning
Project
Cognitive Services for Speech
Cognitive Services for Vesion

Module 8: Capstone Project

10 Hours

Languages and Tools covered

and more…

Capstone Projects

Data sets from the industry

100 +
Projects
completed
22 +
Domains

 

100 +
Papers published
at IIMB

Faculty

Learn from leading academicians in the field of data science and business analytics and several experienced industry practitioners from top organizations.

22 +
Professors
22 +
Industry Mentors
100 +
Award winning faculties

Program Fees

PG Program in Machine Learning & Artificial Intelligence

Rs. 70,000 + GST

Post Graduate Certificate from
  • Personalised weekly online mentorship sessions
  • 9-month Program
  • 103+ hours of online learning content
  • Dedicated career support through interview workshops and 1:1 mentorship
  • IIBM Institute – curated jobs portal and exclusive career fairs
  • Individual doubt-solving with expert mentors
  • 13 real-world projects guided by industry experts
  • Access to IIBM Confluence – Industry and Peer Networking EventsML

 

Candidates can pay the course fee through Netbanking, Credit/Debit cards, Cheque or DD. Also, with our corporate financial partnerships avail education loans at 0% interest rate*.    

 

                                                                                       

Application Process

Step 1 : Fill the application form

 

Apply by filling a simple online application form

Step 2 : Interview Process

 

Go through a screening call with the Admission Director’s office.

 

Step 3 : Join Program 

 

 An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission fee.

Upcoming Application Deadline

Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.

                                   Deadline: Today

Still have queries?
Contact Us

Please fill in the form and an expert from the
admissions office will call you in the next 4 working
hours. You can also reach out to us at
 pgpds@iibmindia.in or +91-9808811409