Get updates on Eligibility, Admission, Placements Fees Structure
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
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
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)
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.
Yes, the PG Level Certification Course in Artificial Intelligence and Machine Learning training is designed for working professionals.
The course will be taken by accomplished academicians and industry experts redefining AI Research in the country.
The PG Level Certification Course in Artificial Intelligence and Machine Learning comprises industry lectures to understand the latest industry trends.
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.