Post Graduate Certificate in Artificial Intelligence and Machine Learning

• What is data science, history, learning path, why to learn it, its impact and its scope

• Participants sharing their Data Science journey, and where they are and what they do

• What do participants anticipate about data science and its scope?

• Vectors, Scalars, Matrix, Operations on Matrix, Determinants, Role of stats in DS, Types of data, Descriptive Stats, Intro to Probability, and Probability Distributions

• Inferential Statistics, Sampling, Estimation, Hypothesis, Type 1 and Type II errors, Z test, T test, Z score, and Confidence Interval

• Python basic Data Structures, Lists, Tuples, Sets, Dictionaries, Functions, and Loops

• Control Structures, File Handling, Comprehensions OOPs, Generators, and Libraries

• Excel based (Importing, Grouping, Pivots), SQL based (Aggregation), Git Fundamentals, Collaboration and Version Control

• Python for Data science (Numpy, Pandas, Matplotlib, SciPy, Scikit-learn, etc.)

• Data cleaning, feature selection, and normalization

• Hands-on exercises, case studies, and discussions

• Linear regression and evaluation metrics

• Multiple, Polynomial, Overfitting, Solution to Over Fitting

• Evaluation metrics

• Logistic regression, Decision tree, Random Forest, SVM, Model Deployment basics (store, load, predict)

• Basics, Distance Matrix and Applications

• How to implement clustering (Agglomerative clustering) and connect with business requirements, Algorithms (PCA, CFA), Association Rule Mining, Algorithm, DB Scan, Anomaly Detection (Nearest Neighbor, and Isolation Forest)

• Ensemble technique with examples (its difference from supervised and unsupervised learning); types (Bagging and boosting )

• Bagging and boosting, different algorithms and libraries (Adaboost, GB, XGB, Catboost)

• Types of timeseries data, AR and MA Modelling

• ARIMA, FB Prophet, and implement data

• Similarities - how to measure (PSN correlation, Cosine), Distances (Manhattan, Euclidean), and Use cases

• What is recommendation engine, its purpose, types and how to build (libraries)

• Cross-validation, neural networks, activation functions, and DL frameworks

• Cross-validation, neural network coding, and applications

• Perceptrons, the math behind perceptrons, and Python implementation

• Introduction to MLPs, forward propagation, Python implementation, introduction, math derivation, and Python implementation

• Introduction to optimisers, activation functions, loss functions and overfitting scenario

• Best practices in choosing optimizers, activation functions, loss functions, batch normalisation and dropout technique

• Convolution Neural Network (Filters - Feature Detectors, Pooling - Avg, Max, Padding and Stride), Basic Architecture

• Pre-trained Networks, Transfer Learning and Fine Tuning

• Recurrent Neural Networks (Temporal Nature of Data, Recurrent Mechanism, Types of RNN), LSTM Gates, and GRU Gates

• Applications, Drawbacks of RNN, LSTM and its drawbacks, GRU, Attention Mechanism, and Transformers

• Autoencoders, DBN, and RBM

• Applications of networks for various use cases

• VAE, GAN, Architecture, Training Process of Generator and Discriminator, DCGAN, WGAN and other GANs, Introduction, and sequence generation

• Implementation and application

• Introduction to NLP, Text Preprocessing, Text Tokenisation, and Word Embeddings

• Text Classification, Use of Sequence models (RNN, LSTM, GRU), NER, Information Extraction, and Machine Translation

• Introduction, Transformer architecture, Attention Mechanism, and types (GPT, BERT, T5, etc.)

• Machine Translation and Text Generation

• Basic image processing techniques, image scaling, object detection algorithms, and image segmentation algorithms

• Video analysis, self-supervised learning techniques, and few shot learning

• Basics of speech recognition, history, importance and applications

• Fundamentals of speech signals, feature extraction (MFCC, spectrogram), Speech Classification, Introduction to Speech Transcription (HMM, ASR models), and TTS systems

• Introduction to RL, History, key concepts, applications, case studies, Markov Decision Process, Dynamic Programming (Bellman Equations, Policy Evaluation and Improvement)

• Monte Carlo Methods, Temporal Difference Learning, Q learning and its implementation

• Integration of Deep Learning in RL, Multi-agent RL, hierarchical RL, meta-RL, practical applications

• Neural networks in RL, DQN Architecture and its implementation, Policy Gradient Methods

• LLM - definition, Pretraining objectives, fine-tuning, transfer learning, prompt engineering, applications, Other LLMs, and Embedding

• Prompt Engineering, Agents, and Vector DBs

• REST API concept, POST, GET, PUT, and DELETE

• Routing Applications

• Introduction to Streamlit, Text, and Widgets

• Building application on Streamlit

• Ethical considerations (banking, ecommerce sectors); pushing code to repository

• Responsible AI; Explainable AI; Registry, Model & Data Monitoring

Learners get to choose 2 out of 5 specialisation options.

• Cybersecurity

• Cloud Computing

• No-Code

• Internet of Things (IoT)

• Explainable AI (XAI)

• 2 Week Capstone Project

• Embedding Models & Agent Memory

• Vector Search & Chunking Strategies

• Advanced RAG Architectures & Tuning

• Learning & Adaptation (Reinforcement Learning, Human Feedback)

• Deployment Options (Cloud, Serverless, Embedded)

• Monitoring & Observability (LangSmith)

• Responsible Agentic AI (Risks, Bias, Privacy, Safety Layers)

• Industry Case Studies & Future Trends

• Interactive Design Exercise: Architect Your Own Agent

• Introduction to Chatbots

• Working with Intents

• Working with Entities

• Defining the Dialog

• Deploying your Chatbot

• Advanced Concepts – Part 1

• Advanced Concepts – Part 2

• Overview of Tensors

• Tensors 1D

• Two-Dimensional Tensors

• Derivatives in PyTorch

• Simple Dataset

• Dataset and Data Augmentation

• Introduction to Cloud Computing

• Roles and responsibilities in Cloud Computing

• Key Cloud Computing Service Models

• Types of Cloud Deployments

• Key companies in Cloud Computing

• Cloud Computing services and offerings

• Technical aspects of Cloud Computing

• Relevance of AI in Cloud Computing

• Recent AI developments in Cloud Computing

• AI use cases in Cloud Computing

• Future of AI in Cloud Computing

• Introduction to No-Code AI

• What people in No-Code AI do

• Key platforms in No-Code AI (e.g., Google AutoML DataRobot, H2O.ai)

• Overview of services provided by No-Code AI platforms

• Benefits of using No-Code AI (speed, accessibility, reduced technical complexity)

• Limitations of No-Code AI (customization, scalability)

• Technical aspects of No-Code AI tools (drag-and-drop interfaces, automated model building, and deployment)

• How AI is integrated into No-Code platforms

• Recent developments in No-Code AI

• AI use cases enabled by No-Code AI platforms industries adopting No-Code AI (finance, healthcare, retail, etc.)

• Ethical concerns and challenges in No-Code AI

• Future trends in No-Code AI (democratisation of AI, citizen data scientists)

• Introduction to AI and ML in IoT

• Key IoT devices and their role in AI and ML data collection

• IoT data processing and management for AI models (Edge vs. Cloud)

• AI Models for IoT (Predictive Models, Anomaly Detection, Classification)

• Machine learning algorithms used in IoT (Regression, Decision Trees, Neural Networks)

• Real-Time Data Analytics in IoT using AI and ML

• AI at the Edge: Challenges and Opportunities

• AI Use Cases in IoT (Predictive Maintenance, Smart Cities, Connected Vehicles)

• Deep Learning in IoT: Use Cases and Frameworks

• IoT Data Labelling for AI and ML Models

• AutoML for IoT Applications

• AI-Driven automation in IoT Systems

• Challenges of AI and ML integration in IoT (Data Quality, Privacy, Real-Time Processing)

• Future trends in AIML for IoT (Federated Learning, 5G, AIoT)

• Introduction to Explainable AI (XAI)

• Importance of Explainability in AI

• Roles in Explainable AI (Data Scientists, AI Ethicists, Domain Experts)

• Key companies working on Explainable AI (e.g., Google, IBM, Microsoft)

• Services provided by Explainable AI platforms

• Techniques for Explainable AI (LIME, SHAP, Saliency Maps, Integrated Gradients)

• Challenges in Explainable AI (complexity, transparency vs. accuracy tradeoff)

• How AI systems can be made explainable

• Recent developments in Explainable AI

• AI use cases where explainability is crucial (Healthcare, Finance, Legal, Autonomous Systems)

• Regulatory and ethical implications of Explainable AI

• Future trends in Explainable AI (regulation, democratization of explainability tools)

Yes, this is a 100% online AI and ML training programme by E&ICT Academy IIT Guwahati. It includes live interactive classes, recorded video sessions, hands-on AI and ML projects, and mentorship from IIT Guwahati faculty and domain experts. 

The E&ICT Academy IIT Guwahati artificial intelligence course covers: 

• Machine Learning Training (Regression, Classification, Clustering) 

• AI and ML Applications in business, healthcare, and finance 

• AI and ML Techniques for model building, feature engineering, and hyperparameter tuning 

• Deep Learning, Neural Networks, and NLP 

• Data Science and AI-driven Decision Making 

Yes, the E&ICT Academy IIT Guwahati AI course emphasizes hands-on AI and ML projects where participants work with real-world AI and ML tools like TensorFlow, PyTorch, and Scikit-learn to solve industry problems. 

This AI and ML training programme provides hands-on experience with: 

• Python, Jupyter Notebooks & Data Visualization 

• Machine Learning Algorithms & Deep Learning Architectures 

• TensorFlow, Keras & PyTorch for AI Model Development 

• AI and ML Applications in NLP, Computer Vision, and Predictive Analytics 

• Cloud-based AI and ML Tools for Deployment 

Yes, upon successfully completing this machine learning certificate programme, you will receive an AI and ML certification from the E&ICT Academy, IIT Guwahati, a credential recognised across industries. 

The E&ICT Academy, IIT Guwahati artificial intelligence certification offers: 

• Expert AI and ML training from IIT faculty 

• Practical AI and ML projects for hands-on learning 

• Industry-relevant AI and ML applications 

• Recognition from IIT Guwahati, one of India’s top institutions 

No prior experience in AI or ML is required. However, basic knowledge of Python programming, statistics, and data science concepts will be helpful. The E&ICT Academy, IIT Guwahati machine learning course is designed for both beginners and professionals. 

This E&ICT Academy, IIT Guwahati AI and ML certification programme prepares you for roles such as: 

• AI/ML Engineer 

• Data Scientist 

• Machine Learning Specialist 

• AI Researcher 

• Business Analyst with AI Expertise   

Completing this artificial intelligence certification enhances your resume and career prospects in AI and ML applications across industries. 

The E&ICT Academy, IIT Guwahati AI and ML course runs for 11 months, offering flexible learning options. Detailed fee structure and enrolment information can be found on the official course page.