AI 300: Certified Artificial Intelligence (AI) Associate

Course Name: Certified Artificial Intelligence (AI) Associate

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Course Name Certified Artificial Intelligence (AI) Associate
Course Code AI 300
NSQF Level Level-4
Duration 240 Hours
Last Date of Registration 04-12-2022
Course Start Date 12-12-2022
Fee Details
  • Registration Fee- Rs. 100/- + GST applicable (Exempted for SC/ST candidates)
  • Tuition Fee - Rs.10, 080/- + GST applicable (Exempted for SC/ST candidates) 
  • NSQF Examination Fee: Rs 2300 + GST as applicable (Exempted for SC/ST candidates) 

                  Artificial Intelligence has grown to be very popular in today’s world. Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. It’s but natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or pursuing a career in Artificial Intelligence. 

               AI has emerged as a leading technology used in the Booming areas like Machine Learning (ML), Internet of Things (IoT) and Data analytics. Currently available academic curriculum is not much enough to fulfil the requirement of Skills needed for AI in Industry. This course will offer required skills and hands-on experience in AI to candidate and professionals; this will increase the employability opportunity for candidate and bridge the gap of Skilled Human requirement for Industry.

Objective of the Course:

            The Objective is to develop the skills in Machine Learning and Deep Learning technologies for development AI applications using Python Programing and Data Analytics packages.

Outcome of the Course:

After successful completion of this Course, students will be able to:

  • Develop Python programming language skills  required for Data  Analytics, Machine Learning and Deep Learning
  • Able to use Descriptive & Inferential Statistics concepts in Data Analysis And Predictive Modelling
  • Able to Analyse and Process the data  using  Numpy and Pandas Libraries
  • Able to do Data Visualization using Pandas, Matplotlib, Seaborne, Plotly and Cufflinks 
  • Able to develop predictive models using Machine Learning Algorithms & Scikit tool Framework
  • Able to develop predictive models using DL models using Keras, TensorFlow tool

Expected Job Roles:

  • ML Associate / AI Associate
  • Data Analyst
  • Machine Learning Engineer
  • AI  Engineer

Full Flow of Course:

Course Structure:

This course contains totally five modules as given in the table below:

Module Code Module Name Duration(in Hours)
AI301 Python Programming 30
AI302 Statistical Concepts 20
AI303 Data Science and Analytics 30
AI304 Machine Learning 80
AI305 Deep Learning 80
                                                                           Total 240

Course Fee:

NSQF Registration Fee - Rs. 100/- + GST as application (Exempted for SC/ST candidates) 
Tuition Fee - Rs. 10,080/- + GST as application (Exempted for SC/ST candidates)
Course fee of  Rs. 11,900/- Including GST can be  paid as a single  instalment on or before 04-12-2022

Apart from above fee, following examination fee to be paid by all selected candidates (excluding SC-ST candidate#) directly while applying for admission:
  1. NSQF Examination fee of Rs. 2300/-  + as GST applicable while registering for examination

Registration Fee- Refund Policy:

(Non-Refundable if candidate is selected for admission but did not join and if a candidate has applied but not eligible.)
However, the registration fee shall be refunded on few special cases as given below:

  • Candidates are eligible but not selected for admission.
  • Course postponed and new date is not convenient for the student.
  • Course cancelled.


  • Pursuing final year BE/BTech/MCA in any discipline Or 
  • BCA/ B.Sc. IT/ B.Sc. Electronics Or 
  • Diploma in Electronics/ IT/ Electrical with 1 Years of Experience in IT Sector

Number of Seats: 20 (Twenty) – Total

How to Apply?

Candidates can apply online in our website Payment towards non-refundable registration fee can be paid online on the Student Portal   

Last date of Registration: 04th December, 2022

Registration Procedure:

All interested candidates are required to fill the Registration form online with registration fees before 04th December, 2022 with all the necessary information.

Selection Criteria of Candidates:

The selection to the course shall be based on the following criteria:

  • Selection of candidates will be based on the merit of their marks in the last qualifying examination subject to meeting the eligibility and availability of seats.
  • The list of Provisionally Selected Candidates will be published on NIELIT website ( ) on 06-12- 2022 by 5:00 PM.
  • Provisionally selected candidate has to forward their document for online verification.
  • For SC/ST:
    • Original Copies of Proof of Age, Qualifying Degree (Consolidated Mark sheet & Degree Certificate/ Course Completion Certificate), 10th and 12th mark sheets.
    • Self-attested copy of community certificate.
    • AADHAR Identity proof must for SC/ST Candidates (For availing concession).
    • One passport size photograph.
  • For Others:
    • Original Copies of Proof of Age, Qualifying Degree (Consolidated Mark sheet & Degree Certificate/ Course Completion Certificate), 10th and 12th mark sheet.
    • One passport size photograph.
    • Self-attested copy of Govt. issued photo ID card

After successful completion of registration alongwith registration fee payment, candidates are required to send their credential documents as single pdf file to "" and ""  for further processing for admission.


All provisionally selected candidates whose documents are verified and paid the fees (full) and verified by accounts section of NIELIT will get admission confirmation message through WhatsApp or email.

Discontinuing the course:

  • No fees (including the security deposit) under any circumstances, shall be refunded in the event of a student who have completed the process of admission or discontinuing the course in between. No certificate shall be issued for the classes attended.
  • If candidates are not uploading assignments / excercise within assigned time their candidature will be cancelled without any notice and all fees paid will be forfeited.
  • If candidates are not appearing for any internal examinations/practical their candidature will be cancelled without any notice and all fees paid will be forfeited

Course Timings: 01:30 PM to 05:30 PM (2 Hours Theory with Hands on Practical’s and 2 Hours for Self-practice) (Monday to Friday)

Course enquiries:

Students can enquire about the various courses either on telephone or by personal contact between 9.15 A.M. to 5.15 P.M. (Lunch time 1.00 pm to 1.30 pm) Monday to Friday.

E-mail: 011-2530 8300
Contact Person: Mr. Ripunjay Singh, Contact No: 011-25308400 (Call @ 9 AM to 6 PM)

Important Dates:

Last Date of Registration: 04-12-2022
Display of Provisional Selection List: 06-12-2022 
Payment fee: 06-12-2022 to 09-12-2022
Course Start Date: 12-12-2022

Examination & Certification:

  • For getting Certified Artificial Intelligence (AI) Associate, a candidate has to pass each module individually with minimum required marks of 50%.
  • Certificates will be issued after successful completion of all the modules including assignment, seminar, examination, praticals and project. Online Digital e-Certificate will be issued to the successful candidates and the same should be downloaded from . Certificates issued by NIELIT are also accessible by the candidate through Digilocker.

Examination Scheme:

Examination scheme for each module is as follows:

Module Name Total Marks Written Practical / Assignment
Python Programming 50 20 30
Statistical Concepts 25 10 15
Data Science and Analytics 75 30 45
Machine Learning 100 40 60
Deep Learning 100 40 60
Total 350 140 210
Grading Scheme:
Following Grading Scheme (on the basis of total marks) will be followed:
Grade S A B C D E
Marks Range (in %)  >=85%

>=75% and <85%

>=65% and <75% >=55% and <65% >=50% and <55%  Below 50%

Final Grading as per above grading scheme will be given on the basis of total marks obtained in all modules.

NSQF Examination Pattern:

(Each Question will carry 1 mark)
Duration (in Min): 90
Practical Internal Assessment (Marks) Project/ Presentation/ Assignment Major Project/ Dissertation Total
Papers Marks / Paper Papers Duration (in Min) Marks/ Paper     No. Of Proj ects Marks  
2 100 1 180 90 30 30 0 0 350


Detailed Syllabus of the Course:

  1. Python Programming
  • An Introduction to Python
  • Beginning Python Basics
  • Python Program Flow
  • Functions& Modules
  • Exceptions Handling
  • File Handling
  • Classes in Python
  1. Statistical Concepts
  • Descriptive & Inferential Statistics,
  • Probability Concept: Marginal, Joint & Conditional Probability, Bayes Theorem
  • Probability Distributions,
  • Hypothesis Test
  • Entropy &Information Gain,
  • Regression & Correlation,
  • Confusion Matrix, Bias & Variance
  1. Data Science and Analytics
  • An Introduction to Data Science and Analytics
  • Data Analysis Using NumPy
  • Data Analysis Using Pandas
  • Data Visualization – Pandas, Matplotlib, Seaborne, Plotly and Cufflinks
  1. Machine Learning
  • Introduction to Machine Learning
  • Linear Regression
  • Logistic Regression
  • K-Means Clustering
  • Decision Tree
  • Random Forest
  • K-Nearest Neighbours
  • Support Vector Machine
  • Naive Bayes
  • Principal Component Analysis(PCA)
  • Artificial Neural Networks(ANN)
  1. Deep Learning
  • Introduction to Deep Learning
  • Artificial Neural Network -ANN
  • Loss Function
  • Bias & Gradient Descent
  • Stochastic Gradient Descent
  • Convolution Neural Networks -CNN
  • Recurrent Neural Networks – RNNs
  • Natural Language Processing-NLP
  • Computer Vision using Opencv
  • Deployment
Case Studies / Project Covered:
  • Covid-19 data Analysis
  • Data Pre-processing and Data Analysis for Banking Application
  • Predictive Analysis for Housing Prices
  • Kaggle’s Titanic Survival
  • Numerical Digit Image Classification using Regression Alogrithim
  • Medical Diagnosis using ML (Diabetic and Cancer)
  • Implementation of Spam filtering messages for Mails
  • Hand Written Number Image Classification Using CNN
  • Complex image recognition (CIFAR) using DL
  • Creating Sine wave Signal using RNN
  • Use Deep Learning for medical imaging