1 Month / 90 Hrs

Recommended 4 hrs / day

26 May 2025

Start Date

Course Fee

 No Tuition Fee  

21 May 2025

Apply By

Sr

No

Elements

Contents

Theory

Practical

Total

1

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 EthicalAI
● Generative AI basics

5

2.5

7.5

2

Programming 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.

5

10

15

3

Machine Learning Fundamentals

Core machine learning principles: supervised vs.
unsupervised learning

● Foundational ML algorithms: linear regression, decision trees,clustering
● Model evaluation techniques and accuracy Metrics

6

9

15

4

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

5

10

15

5

Natural Language Processing (NLP) Fundame-ntals

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

6

9

15

6

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)

5

10

15

7

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

3

4.5

7.5

Total Duration

35

55

90