C-DAC Logo

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 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.

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 Government of India (GOI).

The course fee for PG-DAI has to be paid in two installments as per the schedule.

  • First installment is INR. 10,000/- plus Goods and Service Tax (GST) as applicable by GOI.
  • 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 of 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.

  • First installment is INR. 10,000/- plus Goods and Service Tax (GST) as applicable by GOI.
  • Second installment is INR. 117,500/- plus Goods and Service Tax (GST) as applicable by GOI.

The course fee includes expenses towards 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 seat is allocated during counseling rounds.

The second installment of the course fees is to be paid before the course commencement through NEFT.

NOTE: Candidates may take note that no Demand Draft (DD) or cheque or cash will be accepted at any C-DAC training centre towards payment of any installment of course fees.

   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

  1. 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, vector space, linear combination, linear span, linear independence.
  • Matrices definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, hadamard product, functions, determinant, identity matrix, invertible matrix and inverse, rank, trace, symmetric, diagonal, orthogonal, ortho-normal.
  • Eigen values & eigen vectors, concept, intuition, significance, how to find Principle components , concept, properties, applications
  • Singular value decomposition, concept, properties, applications
  1. 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 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

   JDK, JRE, JVM overview, Working with Data Types, Operators, Arrays, Strings, Constructors, Classes and Objects, Object Oriented Concepts, Exception Handling, Generics & Collections overview, Java APIs (java. Lang, java. util) Functional Programming, Functional Interfaces, Introduction to Streams, Threads, Reflection in Java, Introduction to Node.js, Introduction to Spring Framework.

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


Introduction to Business Analytics using some case studies, Summary Statistics, Making 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; Over fitting 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 KNIME tool.

Python Libraries –Numpy, Scipy, Pandas


Machine Learning in Nut shell, 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, 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 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 with isolation forest, Recommender Systems.


  • Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE)
  • Credit Card Fraud Analysis, Intrusion Detection system


Introduction to Deep Neural Network, RNN, CNN, LSTM, Deep Belief Network, 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 & Technique, Tuning Deep Learning Models, Trends in Deep Learning, Application of Multi Processing in DL, Deep Learning Case Studies


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, spaCy 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/ Auto complete, chat bots, 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,YOLO.


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, Managing containers – Logs / Resources
  • Introduction to Kubernetes, Need for Kubernetes, Introduction to Kubernetes cluster ,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, Administering & Monitoring cloud services, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, 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 distributed platform.

   Aptitude: Percentage, Profit & Loss, Ratio & Proportion, Average, Mixture & Allegation, Simple Interest & Compound Interest, Number Systems, Series, Cyclicity & Remainders, Data Interpretation, Syllogism, Coding & Decoding, Blood Relations, Seating Arrangements (Linear & Circular), Ages, Puzzles, Time, Speed & Distance, Trains, Boats & Streams, Time & Work, Wages (Man days), Pipes & Cisterns, Clocks, Permutations & Combinations, Probability, Calendar.
Effective Communication: Fundamentals of Communication, The Art of Communication, Personality Development, English Grammar, Correct Usage of English, Common Mistakes in English Communication, Listening Skills, Reading Skills, Writing Skills, Public Speaking, Presentation Skills, Group Discussions, Interpersonal Skills, Personal Interviews


Artificial Intelligence in Production (20hrs)

Deployment & Maintenance of AI Applications, AI application testing, 

AI model, interoperability, problem solving approaches.

PG Diploma in Artificial Intelligence (PG-DAI) comprehensive programme 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, Technology Consultants.

C-DACs - Advanced Computing Training School
B-30, Sector 62, Institutional Area, Noida
Uttar Pradesh 201307
9711770748, 0120-2210800, Ext 92
Contact Person
Mr. Ravi Payal

C-DAC's Advanced Computing Training School
C-DAC Innovation Park Sr. No. 34/B/1 Panchvati, Pashan Pune
Maharashtra 411008
Contact Person
Mr. Parimal Wagh

Centre for Development of Advanced Computing
NIT Silchar campus, Silchar
Assam 788010
Contact Person
Mr. Ranjan Singh

Q. What is the Eligibility for PG-Diploma in Artificial Intelligence?
A: The eligibility Criteria for PG-DAI is Candidate holding any one of the following degrees

  • Graduate in any Engineering (10+2+4 or 10+3+3 years) in IT / Computer Science / Electronics / Telecommunications / Electrical / Instrumentation, OR
  • Graduate in any discipline of Engineering
  • MSc/MS (10+2+3+2 years) in Computer Science, IT, Electronics with Mathematics in 10+2, OR
  • Post Graduate Degree in Mathematics/ Statistics/ Physics, OR
  • The candidate must have 60% in the qualifying degree.

Q: What is the selection criterion?

A: The selection process consists of a C-DAC Common Admission Test (C-CAT).

Q: Bank loan assistance for the other centres?

 A: Facility of educational loans is available for the selected candidates, which will be provided by nationalized banks only.

Q: Revision of the course contents, is it every six months?
A: The course contents are revised according to the real world needs and when found relevant to emerging trends

Q: Do you have centralized placement cell?
A: Yes. We do have a Centralized Placement Programme where the respective centre actively participates to organize the campus interviews for all the students.

Q: What is the value of the course in the international market?
A: The course has been a trend-setting course due to its unique curriculum and the opportunities that it generates; hence it gives the edge over for the students and gives an international edge.