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PG Certificate Programme in Artificial Intelligence (PGCP-AI)

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(File Type: PDF, File Size: 953 KB, Date: 27/11/2025)
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The objective of the PGCP-AI 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 PGCP-AI course is

  • Graduate in Engineering or Technology (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 Certificate Programme 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 PGCP-AI has to be paid in two installments as per the schedule.

  • First installment is INR. 15,000/- plus Goods and Service Tax (GST) as applicable by GOI.
  • Second installment is INR. 1,35,000/- 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 PGCP-AI has to be paid in two installments as per the schedule.

  • First installment is INR. 15,000/- plus Goods and Service Tax (GST) as applicable by GOI.
  • Second installment is INR. 112,500/- 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 15,000/- + GST on it as applicable at the time of payment is to be paid online as per the schedule. Payments may be made using any of the available payment modes provided 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 using netbanking, UPI, and credit/debit cards through the payment gateway.

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.

i. Fundamentals of Artificial Intelligence

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

ii. Mathematics for Artificial Intelligence 
a. Linear Algebra
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, positive definite matrix, Eigen values &Eigen vectors, concept, intuition, significance, how to find principle components, concept, properties, applications, Singular value decomposition

b. Calculus
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

i. Java Programming 
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.

ii. Advanced Programming using Python
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.

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
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 & Modeling, Feature selection Techniques, Dimensionality reduction, PCA, t-SNE

ML Algorithms: Linear and Nonlinear classification, Regression Techniques, Support vector Machines, KNN, K-means, Decision Trees, Random forest, Bayesian analysis and Naive Bayes classifier, Gradient boosting, Ensemble methods, Bagging , Boosting & Stacking, 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.

Self-Study:

  • 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
Next-Gen AI: Deep Learning, NLP and Computer Vision

Introduction to Deep Neural Network, RNN, CNN, LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, Tensor flow 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, Model Interpretability Tools, Retrieval-Augmented Generation (RAG), Trends in Deep Learning, Application of Multi Processing in DL, GenAI techniques, 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, LLM, Prompt Engineering, 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, Vision Transformers, 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 and YOLO.
AI Compute Platforms, Applications and Trends

Apache Spark
Apache Spark APIs for large-scale data processing: Basics of Spark, deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, setting up Kafka Producer and Consumer, Kafka Connect API, FastAPI for AI/ML model deployment and optimized API serving Connecting DB’s with Spark, Spark Session, Spark Context, Spark Data Frames, ETL jobs using spark.

DevOps for AI/ML
1. Git/GitHub: Introduction to Version control systems, Creating GitHub repository, Using Git – Introduction to get commands.
2. Introduction to containers: Introduction to DevOps, Introduction to Containers, Advantages of using container-based applications, Managing containers – Logs / Resources
3. 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.
4. CI/CD with Jenkins: Introduction to CI/CD, Using Jenkins to build a CI/CD pipeline.
5. 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

Employability Skills

Number System, Ratio and Proportion, Partnership, Percentage, Profit and Loss, Simple Interest & Compound Interest, Time, Speed and Distance, Trains, Time and Work, Wages, Pipes and Cisterns, Boats and Stream, Averages, Mixtures and Allegation, Probability, Permutations and Combinations, Series, Blood Relations, Coding- Decoding, Seating Arrangement, Syllogism, Venn Diagram, Data Interpretation & Sufficiency, Problems on Ages, Clock & Calendar, Alphabetical Reasoning, Ranking & Order, Direction, Puzzles, Statements & Assumptions



Personality Development, English Grammar, Correct Usage of English, Listening Skills, Reading Skills, Writing Skills, Formal Application Writing, Public Speaking, Presentation Skills, Group Discussions, Personal Interviews

PG Certificate Programme in Artificial Intelligence (PGCP-AI) is a 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
Address
:
No 87-A, 6th Cross, Opp. KFC, Wipro Gate, Electronic City, 1st Phase, Bengaluru
Karnataka 560100
Telephone
:
080-28523300
Contact Person
:
Course Enquiries - Mr. R. Guru Prasad
Hostel Enquiries - Arun Shankar
Fax
:
e-Mail
:
Course Enquiries - actsb[at]cdac[dot]in
Hostel Enquiries - arun[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-ESD, PGCP-ITISS, PGCP-AI, PGCP-BDA

C-DACs - Advanced Computing Training School
Address
:
IIT Guwahati Research Park , 5th Floor, Amingaon, Guwahati
Assam 781039
Telephone
:
7002750884, 7002701941
Contact Person
:
Mr. David Ray, Ms. Ruchika Nath
Fax
:
e-Mail
:
davidr[at]cdac[dot]in, nruchika[at]cdac[dot]in
Courses
:
PGCP-AI

C-DACs - Advanced Computing Training School
Address
:
Maithrivihar Building, Satyam Theatre Road, Opposite Bank of India, Near Ameerpet Metro Station, Ameerpet, Hyderabad
Telangana 500016
Telephone
:
7382053731 / 32
Contact Person
:
Mr. BSRK Varaprasad
Fax
:
e-Mail
:
bsrkvprasad[at]cdac[dot]in/ training-hyd[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-VLSI, PGCP-ESD, PGCP-AI, PGCP-ASSD, PGCP-CSF, PGCP-BDA

C-DACs - Advanced Computing Training School
Address
:
A-34 Industrial Area, Phase VIII S.A.S. Nagar, Mohali
Punjab 160059
Telephone
:
0172 2237052-55,6619000
Contact Person
:
Ms Amritpal Kaur, Mr Ajay Mudgil
Fax
:
0172-2237050-51
e-Mail
:
enquiry-mohali[at]cdac[dot]in, ajay[at]cdac[dot]in
Courses
:
PGCP-AI

Centre for Development of Advanced Computing
Address
:
Sector 7, Rain Tree Marg, Next to Bharati Vidyapeeth, Near Kharghar Rly Stn, CBD Belapur, Navi Mumbai
Maharashtra 400614
Telephone
:
022-27565308/04
Contact Person
:
Ms. Rekha Sivasankaran (Admissions & Hostel)
Fax
:
e-Mail
:
course_kh[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-AI, PGCP-BDA

C-DACs - Advanced Computing Training School
Address
:
Plot No. 20, FC-33, Institutional Area, Jasola, New Delhi 110025 New Delhi
New Delhi 110025
Telephone
:
9811558878 , 9166781294, 011-29879518
Contact Person
:
Mr. Apoorva Kohli
Fax
:
e-Mail
:
abhinavd[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-AI, PGCP-BDA

C-DACs - Advanced Computing Training School
Address
:
B-30, Sector 62, Institutional Area, Noida
Uttar Pradesh 201307
Telephone
:
9711770748, 0120-2210800, Ext 92
Contact Person
:
Mr. Ravi Payal
Fax
:
0120-3063374
e-Mail
:
recpacad-noida[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-VLSI, PGCP-ITISS, PGCP-AI, PGCP-BDA

C-DAC Patna
Address
:
14th Floor, Biscomaun Tower,West Gandhi Maidan Patna
Bihar 800001
Telephone
:
0612-2219021, 8757570233
Contact Person
:
Ms. Shreya Chakraborty
Fax
:
e-Mail
:
infocdacpatna[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-AI, PGCP-FBD

C-DAC's Advanced Computing Training School
Address
:
C-DAC Innovation Park Sr. No. 34/B/1 Panchvati, Pashan Pune
Maharashtra 411008
Telephone
:
020-25503134/136/107, 9373731598
Contact Person
:
Ms. Heera Mohanan
Fax
:
NA
e-Mail
:
acts[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-VLSI, PGCP-ESD, PGCP-ITISS, PGCP-AI, PGCP-CSF, PGCP-BDA, PGCP-HPCSA, CCST

Centre for Development of Advanced Computing
Address
:
IIPC Building, NIT Silchar campus, Silchar
Assam 788010
Telephone
:
8447130305, 03842-242009
Contact Person
:
Mr. Alok Dey
Fax
:
e-Mail
:
alokdey[at]cdac[dot]in
Courses
:
PGCP-AI

C-DACs - Advanced Computing Training School, Software Training and Development Centre (STDC)
Address
:
C-DAC STDC, Technopark Campus, Kariyavattom Thiruvananthapuram
Kerala 695581
Telephone
:
0471-2781500, 8547882754
Contact Person
:
Mr. Hiron Bose
Fax
:
e-Mail
:
stdc[at]cdac[dot]in
Courses
:
PGCP-AC, PGCP-AI, PGCP-CSF, PGCP-BDA

Q. Why is nomenclature of Post Graduate Diploma in Artificial Intelligence changed to Post Graduate Certificate Programme Artificial Intelligence ?

A. C-DAC’s Post Graduate Diploma in Artificial Intelligence (PG-DAI) Course nomenclature is enhanced as Post Graduate Certificate Programme Artificial Intelligence  (PGCP-AI) to bring PG-DAI course in line with NCVET standards and guidelines. C-DAC’s 900-hour Post Graduate Diploma in Artificial Intelligence is being upgraded to 1200-hour (24-week), 40-credits. NSQF alignment and NCVET approval are under process.

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

  • Graduate in any Engineering or Technology (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
  • MCA, MCM
  • The candidate must have 60% in the qualifying degree.
For any specific engineering or postgraduate courses that are not mentioned above, candidates may check their course eligibility by emailing their certificates and mark-sheets to actssupport@cdac.in before the last date of C-CAT application.

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.

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