C-DAC
PG Diploma in Artificial Intelligence
The objective of the PG-DAI course is to present in-depth knowledge and applications in Artificial Intelligence using tools and case studies. Upon completion of this course, participants will be empowered to use computational techniques in the area of Artificial Intelligence, Natural Language Processing, Machine Learning, and Deep Learning-based applications.
The educational eligibility criteria for the PG-DAI course is
Graduate in Engineering (10+2+4 or 10+3+3 years) in IT / Computer Science / Electronics / Telecommunications / Electrical / Instrumentation, OR
MSc/MS (10+2+3+2 years) in Computer Science, IT, Electronics OR
Graduate in any discipline of Engineering, OR
Post Graduate Degree in Mathematics/ Statistics/ Physics, OR
MCA, MCM
The candidate must have 60% in the qualifying degree.
Course Content
Advanced Programming using Python - 90 Hours
Python Programming: Introduction to Python, Basic Syntax, Data Types, Variables, Operators, Input/output, Flow of Control (Modules, Branching), If, If-else, Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Introduction, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions, and Functional Programming, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, matplotlib, seaborn,
Advanced Python: Object Oriented, OOPs concept, Class and object, Decorators, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, Python Libraries, Web-based frameworks: Flask and Django, Request & URL lib.
Self-Study: Mathematical computing with Python, Data migration and visualization: Pandas and Matplotlib, Pycharm, Anaconda, Data manipulation with Pandas
AI Compute Platforms, Applications & Trends - 120 Hours
Apache Spark
Apache Spark APIs for large-scale data processing: Basics of Spark, Deploying to a Cluster Spark Streaming, Spark ML lib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Connecting DB’s with Spark, spark session, spark context, spark data frames, ETL jobs using spark.
AI Future Trends
DevOps for AI/ML
Git/GitHub: Introduction to Version control systems, Creating GitHub repository, Using Git – Introduction to git commands.
Introduction to containers: Introduction to DevOps, Introduction to Containers, Advantages of using container-based applications, Installing docker and using basic docker commands, Building your own container-based application image, Networking in Docker, Managing containers – Logs / Resources
Introduction to Kubernetes, Need for Kubernetes, Introduction to Kubernetes cluster – Basic terms - Management node, Worker Nodes, Pods, Deployment, Service Types, etc., Working with Kubernetes Cluster – Creating deployment, Exposing Deployment as a service, Managing your applications. Rolling application updates etc.
CI/CD with Jenkins: Introduction to CI/CD, Using Jenkins to build a CI/CD pipeline.
Cloud Computing:
Cloud Computing Basics, Understanding Cloud Vendors (AWS/Azure/GCP), Definition, Characteristics, Components, Cloud provider, SAAS, PAAS, IAAS, and other Organizational scenarios of clouds, Administering & Monitoring cloud services, benefits, and limitations, Deploy application over cloud. Comparison among SAAS, PAAS, IAAS, Cloud Products and Solutions, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, and Dashboard.
Exploring cloud services for AI/ML
Self-Study: AI applications in Financial Services including Insurance banking, stock markets & other financial markets like Forex–and Artificial Economics, AI applications in Health Sciences & other Scientific Applications, AI in Cloud Environment. Deployment of Models on a distributed platform.
Data Analytics - 100 Hours
Introduction to Business Analytics using some case studies, Summary Statistics, Making the Right Business Decisions based on data, Statistical Concepts, Descriptive Statistics and its measures, Probability theory, Probability Distributions (Continuous and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and Estimation, Statistical Interfaces, Predictive modeling and analysis, Bayes’ Theorem, Central Limit Theorem, Data Exploration & preparation, Concepts of Correlation, Covariance, Pearson Correlation, Outliers, Regression Analysis, Forecasting Techniques, Simulation, and Risk Analysis, Optimization, Linear, Nonlinear, Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data Analysis, Hypothesis Techniques, Z-Test, chi-Square Test, Skewness.
Predictive Modeling (From Correlation To Supervised Segmentation): Identifying Informative Attributes, Segmenting Data By Progressive Attributive, Models, Induction And Prediction, Supervised Segmentation, Visualizing Segmentations, Trees As Set Of Rules, Probability Estimation; Overfitting And Its Avoidance, Generalization, Holdout Evaluation Vs. Cross Validation;
Decision Analytics: Evaluating Classifiers, Analytical Framework, Evaluation, Baseline, Performance And Implications For Investments In Data; Evidence And Probabilities: Explicit Evidence Combination With Bayes Rule, Probabilistic Reasoning; Intro to KNME tool.
Python Libraries –Numpy, Scipy, Pandas
Deep Neural Networks - 100 Hours
Introduction to Deep Neural Networks, RNN, CNN, LSTM, Deep Belief Networks, semantic Hashing, Training deep neural network, Tensorflow 2.x, Pytorch, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (A log model), Transfer Learning, Inductive, unsupervised Transductive, Deep Learning Tools & Techniques, Tuning Deep Learning Models, Trends in Deep Learning, Application of Multi-Processing in DL, Deep Learning Case Studies.
Reinforcement Learning:
Basics of Reinforcement Learning
Markov Decision Processes: Gridworld, Choosing rewards, Markov Property, Markov Decision Process, Future Rewards, Value Functions, The Bellman Equation, Bellman Example, Optimal Policy & Optimal Value Function
Dynamic Programming: Iterative Policy Evaluation, Designing your RL program, Code – Gridworld, Iterative Policy Evaluation, Windy Gridworld, Iterative Policy for Windy Gridworld, Policy iteration, Value iteration
Monte Carlo: Policy evaluation, Monte Carlo control (MCC), MCC without exploring starts
Temporal Difference Learning: Introduction, TD (0), SARSA, Q Learning
Approximation Methods: Linear Models for Reinforcement Learning, Feature Engineering, Approximation methods for prediction and control, Carole
Fundamental of Artificial Intelligence - 40 Hours
Introduction to AI, Evolution & Revolution of AI, Ethics of AI, Structure of AI, Responsible AI, Real world Implications, Intelligent Agents, Uninformed Search, Constraint Satisfaction Search, Combinatorial Optimization Problems, Heuristic & Meta-heuristics, Genetic Algorithms for Search, Game Trees, Supervised & Unsupervised Learning, Knowledge Representation, Propositional and Predicate Logic, Inference and Resolution for Problem Solving, Rules and Expert Systems, Artificial Life, Emergent Behavior, Genetic Algorithms
Mathematics for AI - 50 Hours
i. Linear Algebra (30 Hrs)
• Vectors, definition, scalars, addition, scalar multiplication, inner product (dot product), vector projection, cosine similarity, orthogonal vectors, normal and ortho-normal vectors, vector norm, vectors pace, linear combination, linear span, linear independence, basis vectors
• Matrices definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, Hadamard product, functions, linear transformation, determinant, identity matrix, invertible matrix and inverse, rank, trace, the popularity of matrices-symmetric, diagonal, orthogonal, ortho-normal, positive definite matrix
• Eigen values & eigenvectors, concept, intuition, significance, how to find Principle component analysis, concept, properties, applications
• Singular value decomposition, concept, properties, applications
ii. Calculus (20 Hrs)
• Function scalar derivative, definition, intuition, common rules of differentiation, chain rule, partial derivatives, Gradient, concept, intuition, properties, directional derivative
• Vector and matrix calculus, how to find derivative of scalar-valued, vector-valued function with respect to a scalar, vector} four combinations- Jacobian
• Gradient algorithms, local/global maxima, and minima, saddle point, convex functions, gradient descent algorithms-batch, mini-batch, stochastic, their performance comparison
Natural Language Processing & Computer Vision - 100 Hours
Natural Language Processing: Understanding Language, NLP Overview, Introduction to Language Computing, Language in Cognitive Science, Definitions of language, Language as a rule-governed dynamic system, Language and symbolic systems: Artificial language (Logical language/programming language) vs. Natural Language, Linguistics as a scientific study, Language Analysis and Computational Linguistics, Semantics, Discourse, Pragmatics, Lexicology, Shallow Parsing and Tools for NLP, Deep Parsing and Tools for NLP, Statistical Approaches, NLP with Machine Learning and Deep Learning, Pre-processing, Need of Pre-processing Data, Introduction to NLTK, Using Python Scripts, Word2Vec models (Skip-gram, CBOW, Glove, one hot Encoding), NLP Transformers, Bert in NLP Speech Processing, NLP Model Deployment Techniques using Flask, NLP Applications- Language identification, Auto-suggest/ Autocomplete, chatbots, Robotics, Building NLP Application from scratch
Computer Vision: Introduction to Computer Vision, Computer Vision and Natural Language Processing, The Three R's of Computer Vision, Basics of Image Processing, Low-, Mid- & High-Level Vision, Edge Detection, Interest Points and Corners, Image Classification, Recognition, Bag of Features, and Large-scale Instance Recognition, Object Detection & Transfer Learning, AlexNet, ResNet, Image Net, Gender Prediction, Face / Object Recognition, Introduction to object detection Algorithms - RCNN, Fast RCNN, Faster RCNN, Mask RCNN.
Practical Machine Learning - 100 Hours
Machine Learning in Nutshell, is Supervised Learning, Unsupervised Learning, ML applications in the real world.
Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA, t-SNE
ML Algorithms: ML Algorithms, Linear and Nonlinear classification, Regression Techniques, Support vector Machines, KNN, K-means, Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naive Bayes classifier, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Clustering, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System
Self-Study:
• Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, score, Precision, Recall, MSE, MAE)
• Credit Card Fraud Analysis, the Intrusion Detection system
Project - 120 Hours
Artificial Intelligence in Production (20hrs)
Deployment & Maintenance of AI Applications, AI application testing,
AI model, interoperability, problem-solving approaches.
Aptitude & Effective Communication - 80 Hours
Course Outcome and Job Role
PG Diploma in Artificial Intelligence (PG-DAI) comprehensive program that combines Data Science, Machine Learning, and Deep Learning to prepare candidates for the roles of Applied AI Scientists, Applied AI engineers, AI architects, Technology architects, Solution Engineers, and Technology Consultants.
CDAC Center for PG DAI
Pune - Cutoff 2000 to 4000 approx
Noida - Cutoff 3000 to 7000 approx
Patna - Cutoff 4000 to 11000 approx
Silchar - Cutoff 5000 to 11000 approx
Course Fees Structure
The Post Graduate Diploma in Artificial Intelligence course will be delivered in fully ONLINE or fully PHYSICAL mode. The total course fee and payment details for the fully PHYSICAL or fully ONLINE mode of delivery is as detailed herein below:
1. PHYSICAL Mode of Delivery:
The course fee for the fully PHYSICAL mode of delivery is INR. 1,50,000/- plus Goods and Service Tax (GST) as applicable by the Government of India (GOI).
The course fee for PG-DAI has to be paid in two installments as per the schedule.
The first installment is INR. 10,000/- plus Goods and Service Tax (GST) as applicable by GOI.
The second installment is INR. 1,40,000/- plus Goods and Service Tax (GST) as applicable by GOI.
2. ONLINE Mode of Delivery:
The course fee for the fully ONLINE mode of delivery is INR. 127,500/- plus Goods and Service Tax (GST) as applicable by GOI.
The course fee for PG-DAI has to be paid in two installments as per the schedule.
The first installment is INR. 10,000/- plus Goods and Service Tax (GST) as applicable by GOI.
The second installment is INR. 117,500/- plus Goods and Service Tax (GST) as applicable by GOI.
The course fee includes expenses for delivering classes, conducting examinations, final mark list and certificate, and placement assistance provided.
The first installment course fee of Rs 10,000/- + GST on it as applicable at the time of payment is to be paid online as per the schedule. It can be paid using credit/debit cards through the payment gateway. The first installment of the course fees is to be paid after the seat is allocated during counseling rounds.
The second installment of the course fees is to be paid before the course commencement through NEFT.