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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English		Video and Text Based	Weekends

Course Overview

The PG Level Certification Course in Artificial Intelligence and Machine Learning by IIIT Hyderabad is a 9-month online course where three months are allotted for hands-on projects. This online certification course is designed for tech professionals equipping them with programming languages and tools. The PG Level Certification Course in Artificial Intelligence and Machine Learning online course from IIIT Hyderabad requires participants to complete this online certification course within 9 months. They can choose hands-on projects with advanced certification.

The PG Level Certification Course in Artificial Intelligence and Machine Learning syllabus covers various important topics taught by top faculty members. The course curriculum is specially designed for practitioners that will be delivered by live interactive lectures, mentored labs, projects and hackathons. Participants will also have opportunities to visit the campus and interact with an outstanding peer group.

After completing the PG Level Certification Course in Artificial Intelligence and Machine Learning training from IIIT Hyderabad via TalentSprint, participants can work with global organizations like Accenture, Capgemini, Microsoft, MOBIS, Teradata, Deloitte and others. Get more details about the PG Level Certification Course in Artificial Intelligence and Machine Learning.

The Highlights

Offered by IIIT Hyderabad
9 months online course
3 months hands-on projects
4 Units
2 hours of learning every day
2 campus visits
Certificate of completion

Programme Offerings

120 hours of live classes
2 hours of learning per day
Weekend batches
2 Campus visits
144 hours of hands-on projects
4 hackathons
40+ tools
250 hours of lab sessions
Certification

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIIT Hyderabad

The PG Level Certification Course in Artificial Intelligence and Machine Learning fee with Hands-on Projects (PG Certification Program in AI/ML) is Rs.300,000 + GST. The fee without Hands-on Projects (Advanced Certification Program in AI/ML) is Rs.200,000 + GST. Students can take advantage of scholarships and pay the course fee with flexible EMI options.

PG Level Certification Course in Artificial Intelligence and Machine Learning fee structure

Description	Amount in INR	EMI
PG Certification Program in AI/ML	Rs 300,000 + GST	Rs 14,750/ Month*
Advanced Certification Program in AI/ML	Rs 200,000 + GST	Rs 10,817/ Month*
Application Fee	Rs 2,000 + GST

Eligibility Criteria

Candidates must have a coding background to join the PG Level Certification Course in Artificial Intelligence and Machine Learning by IIIT Hyderabad delivered in association with TalentSprint.

Work Experience

The PG Level Certification Course in Artificial Intelligence and Machine Learning requires candidates to have a minimum of 1 year of work experience.

Certification Qualifying Details

Upon successful completion of the PG Level Certification Course in Artificial Intelligence and Machine Learning training, participants will receive a Certificate of Merit from IIIT Hyderabad.

What you will learn

Programming skillsMachine learningKnowledge of Artificial IntelligenceKnowledge of deep learning

By pursuing PG Level Certification Course in Artificial Intelligence and Machine Learning the training, students will:

Gain proficiency in Python programming and learn about calculus and linear algebra.
Have an understanding of programming, mathematics and classical algorithms.
Become familiar with Natural Language Processing (NLP) and Neural Networks.
Get exposed to Deep Learning and its associated concepts.

Who it is for

Computer ProgrammerPython ProgrammerArtificial Intelligence Engineer

ThePG Level Certification Course in Artificial Intelligence and Machine Learning training is best suited for professionals passionate to build expertise in Artificial Intelligence. The course is also apt for professionals keen to transition to roles such as Artificial Intelligence Engineer, Python Programmer, Computer Programmer, and others.

Admission Details

The admission process for the PG Level Certification Course in Artificial Intelligence and Machine Learning by IIIT Hyderabad via TalentSprint includes certain steps. These steps are as follows:

Step 1: Go through the official webpage - https://iiit-h.talentsprint.com/aiml/index.html

Step 2: Fill in the application form

Step 3: Wait for selection

Step 4: Pay the requisite fee to enrol in the course

Step 5: Get started with the course

Step 6: Become part of the Alumni network

The Syllabus

Proficiency in Python

Basics of Python
Data Types and Data Structures
Defining and Calling Functions
Python Library Packages

Calculus and Linear Algebra

Vectors
Norms
Matrices
Matrix Operations
Linear Dependence and Independence
Linear Transformations
Least Square Error
Derivatives
Chain Rule
Integration
Gradients

Basic CS Theory

Basic understanding of
- Algorithm Design Paradigms
- Python Classes
- Objects
- Methods

Probability and Statistics

Continuous and Discrete Random Variables
Mutually Exclusive/Non-exclusive Events
Independent and Dependent Events
Probability Distribution Functions
Cumulative Distribution Functions
Measures of Centre and Spread
Normalization and Standardization

Key Python Libraries (NumPy, SciPy, Pandas, Matplotlib)

Numpy
Scipy
Pandas
Matplotlib

Overview of AI and ML concepts

Demystifying ML
Modern AI

Principles of ML and Deeper Look at ML

Supervised vs Unsupervised Learning
Classification vs Regression
Training and Testing Methods
ML Pipeline

Data Processing and Visualization

Data Munging
Outlier Detection
Hands-on Data Visualization

Features Selection and Extraction

Feature Selection Methods
Feature Extraction Methods

Classification 1.0

k-NN Classification
Decision Tree
Linear Classifier

Regression 1.0

Linear Regression
Polynomial Regression
Logistic Regression

Dimensionality Reduction

Basic Mathematics and Hands-on Analysis

Representing Text and Language

Understanding standard NLP Libraries
Exploring Different Representations Methods

Sampling Methods (Bootstrap and Bagging)

Bootstrapping
Bagging

Projects

Literacy Rate Prediction
Aptitude Questions Classification

Intro to Natural Language Processing (NLP)

NLP Problem Formulation and Fundamental Solutions

Representation of Real-world Data

Exploring Word Embeddings

Performance Metrics

Methods to Measure the Efficacy of the ML models

Gradient Descent and Back Propagation

Single Layer Perceptron
Basic Mathematics
Practical Tricks and Tips

Non-Linear Solutions and Multi Layer Perceptron

Activation Functions
MLP Architecture

PCA with EigenFaces

PCA Algorithm
Underlying Mathematics
Applications of PCA

Support Vector Machines and Kernels

SVM Formulation
Non-linear Mapping
Popular Kernels

Manifold Learning and Unsupervised Learning

Non-Linear Dimensionality Reduction for visualization
Hierarchical Agglomerative Clustering
K-means clustering

Features for Perception (Image and Speech)

Classical and Modern Feature Representation for Visual Data
Classical and Modern Feature Representation for Audio/Speech

Projects

Author Identification
Customer Segmentation

Introduction to Deep Learning

Deep Learning Framework
Neural Network Module
Training a Neural Network

Pytorch

Basics of Pytorch
Pytorch Packages
Computation on GPU
Model Building
Model Training and Evaluation

Convolutional Neural Networks

Convolutional Layers and Backpropagation
Building Blocks for Convolutional Networks
Applications of CNN - Audio, Text and Image

Autoencoders

Intro to Autoencoders
Deep Autoencoder
De-noising Autoencoder

Recurrent Neural Networks

Recurrent Neural Network Architecture
Machine Learning with Sequential Data
Neural Network Language Models

Tensorflow and Keras

Intro to Keras and Keras Basics
Keras Models
- Sequential Model
- The Functional API
- Model Subclassing
Sequential model Steps and it's Parameters and Adding Layers
Building a Network in Keras
Loading and Save the Models with Example and Measuring the Performance

Data Engineering

ETL
Dimensional Data Modelling
Big Data Query

Overfitting and Generalization

Memorization and Generalization
Occam’s Razor
Ways to Prevent/Reduce Overfitting

Time Series

Classical Models (AR, MA, ARMA & ARIMA)
Neural Networks for Time Series Forecasting

Ensemble methods and Random Forest

Random Forest
Boosting and Adaboost

Human in the Loop Systems

Different Roles for Human in the Loop
Users in the ML Systems
Relevance Feedback
Active Learning

Projects

Image Classification
Identification of plant disease from leaf images

Appreciating CNNs

Visualizing CNNs
Improving CNNs
Popular CNN Architectures

RNN, LSTM and GRU

Vanishing Gradients
Understanding Recurrent Architectures
RNN, LSTM, GRU

Recommendation Systems

Introduction to Recommendation Systems
Social Recommender Systems
Collaborative Filtering
Hybrid Recommendation Models

Beyond AlexNet and Transfer Learning

Adapting Pre-trained Models for Desired Applications

Fast API

Machine Learning Deployment using
- Docker
- HTML
- Java Script
- Joblib and Pickle

Deployment and Practical Issues

Technical and Non-technical Pratical Issues in ML

Model Compression

Model Compression
Pruning
Quantization
Student-teacher Network

Reinforcement Learning and Applications

Reinforcement Learning Framework
Single and Multi agent RL
Q-Learning
Deep RL
RL Applications

Siamese Networks and GANs

Siamese Architecture
Generative Adversarial Networks

Computer Vision

Introduction to Computer Vision
Camera Model and Geometry
Problems in Computer Vision
Applications and Use Cases

ML in NLP

Sequence Generation and Information Extraction
Latest Text Embedding Models
Neural Machine Translation
Attention Mechanism for DL

ML in Speech

DNN based Speech Applications
DNN with Attention
Sequential Network
Encoder Decoder Models for Speech Recognition
Automatic Speech Recognition

Industry Lectures and talks

Talks from different domain experienced people

Projects

Retail sales forecasting
Image processing and Transfer Learning

Instructors

IIIT Hyderabad Frequently Asked Questions (FAQ's)

1: How can I attend the PG Level Certification Course in Artificial Intelligence and Machine Learning classes?

You can attend the PG Level Certification Course in Artificial Intelligence and Machine Learning classes through 120 hours of live interactive classes with 250 hours of lab sessions and two campus visits.

2: Is this online certification course fully designed for working professionals?

Yes, the PG Level Certification Course in Artificial Intelligence and Machine Learning training is designed for working professionals.

3: Who will teach this online course?

The course will be taken by accomplished academicians and industry experts redefining AI Research in the country.

4: Does this course offer exposure to industry trends?

The PG Level Certification Course in Artificial Intelligence and Machine Learning comprises industry lectures to understand the latest industry trends.

5: What is the selection process for this online certification course?

The candidates’ selection for PG Level Certification Course in Artificial Intelligence and Machine Learning is completely based on academic qualifications, professional experience, and a statement of purpose submitted along with the application.