Post Graduate Program in Artificial Intelligence and Machine Learning
Learn valuable skills for a promising career with the help of the Post Graduate Program in Artificial Intelligence and Machine Learning certification course.
Online
12 Months
2,75,000 INR Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study | Video and Text Based | Weekends |
Course Overview
The Post Graduate Program in Artificial Intelligence & Machine Learning is a comprehensive and hands-on course. The course is 12 months and is provided in a fully online mode. It covers cutting-edge technologies like Deep Learning, Computer Vision, NLP, and Reinforcement Learning, along with practical projects and industry insights.
The Post Graduate Program in Artificial Intelligence and Machine Learning certification by Great Learning and the University of Texas at Austin is designed by expert faculty to ensure a great learning experience. Through this programme, learners gain valuable skills and knowledge to excel in AI and ML roles.
In the Post Graduate Program in Artificial Intelligence and Machine Learning training, candidates will receive career support, resume reviews, and interview preparation. You will also get access to a vast job board with opportunities from 2800+ companies. Whether you are a beginner or an experienced professional, this programme can open doors to promising career prospects in the dynamic field of Artificial Intelligence and Machine Learning.
Also Read: Artificial Intelligence And Machine Learning Certification Courses
The Highlights
- Hands-On Experience
- Case Studies
- Offered by the University of Texas at Austin
- 12 Months Duration
- Online Bootcamp
- Flexible Learning
Programme Offerings
- Mentorship Session
- assignments
- Readings
- LIVE Webinar
- discussion forum
- Capstone Project
- Experiential Learning
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Great LearningTexas McCombs |
Post Graduate Program in Artificial Intelligence and Machine Learning Fee
Mode of Enrolment | Fee |
Total Fee | Rs. 2,75,000+GST |
Starting at ₹ 6,776/month
Eligibility Criteria
Educational Qualification
A bachelor’s degree in any discipline with a minimum of 50% marks is required.
What you will learn
By the end of the Post Graduate Program in Artificial Intelligence and Machine Learning certification syllabus, candidates will be able to have a comprehensive understanding of AI and ML concepts, algorithms, and technologies. He/She will also learn concepts of Deep Learning, Computer Vision, NLP, and Reinforcement Learning. Candidates will become job-ready with resume preparation, interview skills, and access to job opportunities from top companies. Candidates will get hands-on experience in implementing AI and ML projects, working with real-world datasets, and using popular tools and frameworks like TensorFlow, Keras, and OpenCV.
Who it is for
Post Graduate Program in Artificial Intelligence and Machine Learning classes are designed for individuals who are in the following fields:
Admission Details
Enrol in the Post Graduate Program in Artificial Intelligence and Machine Learning online course by following these simple steps:
Step 1: Access the course via: https://www.mygreatlearning.com/pg-program-artificial-intelligence-course
Step 2: Click on the “Apply Now” button.
Step 3: Fill up an application form.
Step 4: Submit the application and start by attending the demo class.
Step 5: You will receive an interview call for further processing.
Step 6: An offer letter will be provided to the selected candidate.
Application Details
In this application form, the applicant needs to provide their basic information, including their name, mobile number, and email address. They need to specify their work experience and any programming experience. The applicant needs to agree to the Terms and Conditions and the Privacy Policy before proceeding.
After that, they need to share their education details for their undergraduate programme. The applicant is required to select their degree type from the available options and provide information about their college/university name, year of graduation, and their CGPA or percentage. These details are necessary to determine if they meet the eligibility criteria, which require a minimum of 50% in any undergraduate programme. Once completed, the applicant can submit the form.
The Syllabus
- Introduction to AI Landscape
- Introduction to Key Terminology
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Generative AI
- Large Language Model
- History and Evolution of AI
- Business Problems and Solution Spaces Across Different Industries
- Python Programming Fundamentals
- Introduction to Python
- Environment Setup
- Google Colab
Introduction to Python
- Variables and Datatypes
- Data Structures
- Conditional and Looping Statements
- Functions
Data Manipulation
- NumPy Arrays and Functions
- Accessing and Modifying NumPy Arrays
- Saving and Loading NumPy Arrays
- Pandas Series (Creating, Accessing, and Modifying Series)
- Pandas DataFrames (Creating, Accessing, Modifying, and Combining DataFrames)
- Pandas Functions
- Saving and Loading Datasets Using Pandas
Exploratory Data Analysis
- Data Overview
- Univariate Analysis (Histogram, Boxplots, and Bar Graphs)
- Bivariate/Multivariate Analysis (Line Plot, Scatterplot, Lmplot, Jointplot, Violin Plot, Striplot, Swarmplot, Catplot, Pairplot, Heatmap)
- Customizing of Plots
- Missing Value Treatment
- Outlier Detection and Treatment
Linear Regression
- Introduction to Learning from Data
- Types of Machine Learning
- Business Problem and Solution Space
- Regression
- Correlation and Linear Relationships
- Simple and Multiple Linear Regression
- Categorical Variables in Linear Regression
- Regression Metrics
Decision Trees
- Business Problem and Solution Space
- Classification
- Introduction to Decision Trees
- Impurity Measures and Splitting Criteria
- Classification Metrics
- Pruning
- Decision Trees for Regression
K-means Clustering
- Business Problem and Solution Space
- Clustering
- Distance Metrics
- Introduction to Clustering
- Types of Clustering
- K-means Clustering
- t-SNE for Visualizing High-Dimensional Data
Bagging
- Introduction to Ensemble Techniques
- Introduction to Bagging
- Sampling with Replacement
- Introduction to Random Forest
Boosting
- Introduction to Boosting
- Boosting Algorithms (Adaboost, Gradient Boost, XGBoost)
- Stacking
Model Tuning
- Feature Engineering
- Cross-validation
- Oversampling and Undersampling
- Model Tuning and Performance
- Hyperparameter Tuning
- Grid Search
- Random Search
- Regularization
Introduction to Neural Networks
- Deep Learning and History
- Multi-layer Perceptron
- Types of Activation Functions
- Training a Neural Network
- Backpropagation
Optimizing Neural Networks
- Optimizers and Their Types
- Weight Initialization and Its Techniques
- Regularizations and Its Techniques
- Types of Neural Networks
Word Embeddings
- Introduction to NLP
- History of NLP
- Sentiment Analysis
- Introduction to Word Embeddings
- Word2Vec
- GloVe
- Semantic Search
Attention Mechanism and Transformers
- Introduction to Transformers
- Components of a Transformer
- Different Transformer Architectures
- Applications of Transformers
Large Language Models and Prompt Engineering
- Introduction to LLMs
- Working of LLMs
- Applications of LLMs
- Introduction to Prompt Engineering
- Strategies for Devising Prompts
Retrieval Augmented Generation
- External Knowledge Sources
- Data Chunking
- Vector Databases
- Retrieval-Augmented Generation (RAG)
- Evaluating RAG Systems
Introduction to AI Agent Workflows
- The Need for AI Agents
- The Role of LLMs in AI Agents
- The Need for External Tools
- Types of Tools
- The Need for Memory
- Short-term vs Long-term Memory
- Building Agentic AI Workflows with LangChain
Planning and Reasoning in AI Agents
- The Role of Planning
- The Role of Reasoning
- Task Decomposition
- Introduction to ReAct Framework
Evaluating AI Agents
- Grounding and Validation
- Agentic AI Evaluation Metrics (Task Completion Rate, Tool Call Accuracy, Reasoning Trajectory Coherence, Efficiency)
- Human-in-the-Loop Evaluation
ML Model Deployment
- Introduction to Model Deployment
- Serialization
- Deployment Using Streamlit
Deploying Generative AI Solutions
- Introduction to Containerization
- Docker
- Deployment using Flask
Data Retrieval & Aggregation Essentials
- Introduction to Databases and SQL
- Fetching data
- Filtering data
- Aggregating data
Querying Techniques for Relational Data Analysis
- In-built functions (Numeric, Datetime, Strings)
- Joins
- Window functions
Advanced Querying for Enhanced Proficiency and Insights
- Subqueries
- Order of query execution
Image Processing
- Overview of Computer Vision
- Color, Pixel and Image Representation
- Edge Detection
- Kernels
- Padding
- Strides and Pooling
- Flattening to a 1D Array
Convolutional Neural Networks
- ANN vs CNN
- CNN Architecture
- Introduction to Transfer Learning
- Common CNN Architectures
Advanced Reasoning and AI Agent Protocols
- Self-Reflection
- Plan-and-Execute
- LangGraph
- MCP
Multi-Agent Systems
- Challenges of Single-Agent LLM Systems
- Multi-Agent Systems as a Coordination Solution
- Common Multi-Agent Architectures
- Multi-Agent System Frameworks and Protocols
- Emergent Behavior in Multi-Agent Systems
- Agentic RAG
Securing Agentic AI Solutions
- LLM Security Framework: The CIA Triad (Confidentiality, Integrity, Availability)
- Common Security Risks in LLMs
- Prompt Injection Attacks
- Mitigation and Guardrail Layers
- Responsible AI Checklist
- Data Security and Privacy
- Agent Behavior Security
- Logging Decision Making for Transparency
- Access Control and Identity
Introduction to DevOps and MLOps
- Introduction to Git, Branching, Merging, and Remote Repositories
- Introduction to GitHub Actions
- Creating and Configuring Workflows
- Advanced Workflow Configuration
Building CI/CD Pipelines
- Overview of MLflow
- Basic Workflows
- Experiment Tracking
- Packaging Code and Running Projects
- Model Lifecycle Management
LLMOps for GenAI Solutions
- LLM Serving Infrastructure
- Scaling
- API Gateway
- Routing
- Load Balancing
- Inference Optimization
Instructors
Texas McCombs Frequently Asked Questions (FAQ's)
Yes, the programme includes 12+ hands-on projects that provide practical experience in implementing AI/ML solutions.
Yes, the programme provides career support with resume review, interview preparation, and access to a job board with job opportunities from 2800+ companies.
Yes, participants who successfully complete the programme will receive a certificate from the institute.
Yes, the programme offers financial aid and collaborations with financial partners for education loans at a 0% interest rate to eligible candidates.
Interested candidates can apply by filling out the online application form and providing the required information, including personal, educational, and professional details.