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    Post Graduate Program in Artificial Intelligence and Machine Learning

    BY
    Great Learning

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    Mode

    Online

    Duration

    12 Months

    Fees

    ₹ 275000

    Inclusive of GST

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 275,000  (Inclusive of GST)

    The fees for course Post Graduate Program in Artificial Intelligence and Machine Learning is -

    HeadAmount
    Programme feesRs. 3,75,000 + GST

     

    Zero Cost EMI Plans - 

    Admission Fees (payable on enrolment): ₹ 30,000 (including GST)

    Monthly Installment
    Installment No.Payment
    3 Month EMI₹ 1,43,400
    6 Month EMI₹ 71,700
    12 Month EMI₹ 35,850
    18 Month EMI₹ 23,900

    Note:

    1. Scholarship of 15k Available for selected candidates
    2. All amounts are including GST
    3. 2% processing charge for 12 month EMI and 3% processing for 18 month EMI

    EMI Plans - 

    Admission Fees (payable on enrolment): ₹ 30,000 (including GST)

    Monthly Installment
    Installment No.Payment
    24 Month EMI₹ 20,251
    36 Month EMI₹ 14,289
    48 Month EMI₹ 11,541
    60 Month EMI₹ 9,899

    Note:

    1. Scholarship of 15k Available for selected candidates
    2. All amounts are including GST
    3. 3% processing charge
    certificate availability

    Yes

    certificate providing authority

    Great Learning

    The syllabus

    Module 1 - Foundations

    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

    Module 2 - Machine Learning

    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

    Module 3 - Advanced Machine Learning

    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

    Module 4 - Introduction to Neural Networks

    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

    Module 5 - Natural Language Processing with Generative AI

    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

    Module 6 - AI Agents for Automation

    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

    Module 7 - Model Deployment

    Introduction to Model Deployment
    • Introduction to Model Deployment  
    • Serialization  
    • Deployment using Streamlit
    Containerization
    • Introduction to Containerization  
    • Docker  
    • Deployment using Flask

    Module 8 - Introduction to SQL

    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

    Module 9 - Introduction to Computer Vision

    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

    Module 10 - Advanced Agentic AI

    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

    Module 11 - MLOps and LLMOps for Scalable Deployment

    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

    Module 12: Capstone

    Instructors

    Dr Abhinanda Sarkar

    Dr Abhinanda Sarkar
    Academic Director
    Great Learning

    Other Bachelors, Other Masters, Ph.D

    Mr Mukesh Rao

    Mr Mukesh Rao
    Professor
    Great Learning

    Dr D Narayana

    Dr D Narayana
    Professor
    Great Learning

    Ph.D

    Dr Kumar Muthuraman

    Dr Kumar Muthuraman
    Professor
    Texas McCombs

    Ph.D

    Dr Pavankumar Gurazada
    Faculty
    Great Learning

    Other Bachelors, MBA

    Mr Bradford Tuckfield
    Data Scientist
    Freelancer

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