Course	Foundations of Artificial Intelligence Technology
Code	NS-3.5-IT-FAI
Starting Date	21-Jan-2026
Last Date To Apply	18-Jan-2026
Course Coordinator	R.C.Bhanusree Mob:8500422223

Course Preamble

The rapid advancement of Artificial Intelligence (AI) has transformed industries, revolutionized decision-making, and created new career opportunities across the globe. To prepare students for this evolving landscape, the Foundations of AI Technology course is designed to introduce learners to the fundamental principles, tools, and applications of AI with a strong focus on practical implementation using Python.

This course emphasizes both theoretical knowledge and hands-on practice, ensuring that participants not only understand AI concepts such as intelligent agents, problem-solving, and machine learning but also gain the technical skills required to build and deploy AI solutions. Students will explore key domains of AI, including natural language processing, computer vision, and deep learning, while working with industry-standard frameworks like TensorFlow, PyTorch, and Keras.

By the end of this course, learners will have developed the confidence to independently design, experiment, and implement AI models, preparing them to contribute meaningfully to the IT and computer science sectors and enhancing their employability in a technology-driven world.

Course Objective

The purpose of this qualification is to train the students in AI implementation using Python to upskill them and increase their employability in the field of IT/Computer Science. The purpose is to demystify AI and equip the future workforce with the confidence to learn and apply skills independently. The participants will get initial exposure for developing AI models

Course Outcome

i.Foundational Knowledge:

● Understanding Artificial Intelligence Principles: Participants will gain a comprehensive understanding of core AI concepts, including knowledge representation, reasoning, problem-solving, and intelligent agents.
● Knowledge of AI Techniques: Participants will become familiar with AI techniques such as machine learning algorithms, neural networks and predictive models.

ii.Technical Skills:

● Natural Language Processing (NLP): Participants will learn techniques for processing and understanding human language using AI models for tasks like sentiment analysis, text classification, and machine translation.
● Computer Vision: Participants will develop skills in AI-powered image and video analysis, including object detection, image recognition, and image segmentation.

iii.Tools and Technologies:

● AI Development Tools: Participants will gain hands-on experience with tools like TensorFlow, PyTorch, and Keras for building AI models. AI Platforms: Participants will utilize platforms like IBM Watson, Google AI, and Microsoft Azure AI for AI model deployment and experimentation.
● Data Annotation and Preprocessing: Participants will learn to preprocess and annotate data for AI training, including handling large datasets for image and text processing.

iv.Deployment and Integration:

● AI Model Deployment: Participants will understand how to deploy AI models in production environments, including integration with cloud platforms.
● Real-time AI Systems: Participants will learn how to implement real-time AI systems, using APIs and cloud services for tasks like real-time predictions and anomaly detection

Course Structure

S.No	Elements	Theory Hours	Practical Hours	Total Hours
1	Introduction to AI Concepts	5	2.5	7.5
2	Programming with Python for AI	5	10	15
3	Machine Learning Fundamentals	6	9	15
4	Introduction to Deep Learning	5	10	15
5	Natural Language Processing (NLP) Fundamentals	6	9	15
6	Computer Vision Basics	5	10	15
7	AI Model Deployment	3	4.5	7.5
Total Duration		35	55	90

 

Course Contents

Introduction to AI Concepts

● Overview of Artificial Intelligence (AI) and its role in modern applications
● Core concepts: intelligence, cognitive modelling
● Understanding AI agents and environment interactions
● AI issues, concerns and Ethical AI
● Generative AI basics

Programmi ng with Python for AI

● Importance of Python in AI
● Python setup, installation, and Integrated Development Environment (IDE)
● Basic Python programming: syntax, data types, control structures, function
● Python libraries for ML NumPy, Matplotlib, and Pandas.

Machine Learning Fundament als

● Core machine learning principles: supervised vs. unsupervised learning
● Foundational ML algorithms: linear regression, decision trees, clustering
● Model evaluation techniques and accuracy metrics

Introduction to Deep Learning

● Introduction to Deep Learning: Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN)
● Fundamentals of image classification using ANN
● Building basic deep learning models with TensorFlow or PyTorch

Natural Language Processing (NLP) Fundament als

● Introduction to Natural Language Processing (NLP): tokenization, stemming, lemmatization
● Natural Language Understanding (NLU) concepts and applications
● Using NLTK for basic NLP tasks

Computer Vision Basics

● Introduction to computer vision and its applications
● Key image processing techniques: analysis, preprocessing, and edge detection
● Simple object detection (e.g., face/eye detection)

AI Model Deployment

● Fundamentals of model deployment
● Introduction to MLOps (Machine Learning Operations)
● Basics of deploying AI models on cloud platforms (AWS, Google AI, Open source)
● Introduction to Docker for containerization

Course Fees

Course fee: Rs.4,680 + all taxes as applicable
SC/ST: Free

Registration Fee: Rs.1,000/- (non-refundable, adjusted if student joins)

Payment Verification & Registration Information

General & OBC - Candidates / Online Courses for all: After completion of the fee payment, please submit the form available under Apply Now. Our team will review and verify your payment details. Once the verification is successfully completed, we will contact you with further guidance to complete the remaining steps of the registration process.For any queries or assistance, please feel free to contact or message the course coordinator.

SC / ST – Offline Courses: SC/ST offline courses are free of charge. Instead of uploading a payment acknowledgement, please upload your Government-issued Caste Certificate during form submission.

Eligibility Criteria

Criteria 1	Criteria 2	Experience	Training Qualification
10th	Pursuing Continuous Schooling	No Experience	None
8th	Passed	No Experience	2 year NTC
8th	Passed	3 years	None
Previous NSQF qualification	of Level 3	1.5 Years	None

 

Important Dates

Next update dates

Month	Starting Date of Registration	Last Date of Registration	Welcome Mail Sending Date(Befor 6PM)	Course Starting Date
Jan,2026	11-Nov-2025	18-Jan-2026	19-Jan-2026	21-Jan-2026
Mar,2026	22-Jan-2026	15-Mar-2026	16-Mar-2026	18-Mar-2026

Contact

For further information if any, you may Contact the Course Coordinator : R.C.Bhanusree, Mob:8500422223