| Time | 07:00 PM - 08:00 PM (IST) | Hands-on 08:00 PM - 09:00 PM (IST) |
|---|---|---|
| Day 1 (Monday): | Introduction to AI, Data Science, and Computer Vision | Setting Up the Environment (Anaconda, Jupyter Notebook, OpenCV) |
| Day 2 (Tuesday): | Introduction to Regression (Linear Regression) | Implementing Linear Regression |
| Day 3 (Wednesday): | Multiple Linear Regression & Polynomial Linear Regression | Implementing Multiple Linear Regression & Polynomial Linear Regression |
| Day 4 (Thursday): | Introduction to k-Nearest Neighbors (k-NN) | Implementing k-Nearest Neighbors |
| Day 5 (Friday): | Introduction to Neural Networks and Backpropagation | Building a Simple Neural Network with Keras/TensorFlow |
| Day 6 (Saturday): | Support Vector Machines (SVM) | Implementing a Basic CNN for Image Classification |
| Time | 07:00 PM - 08:00 PM (IST) | Hands-on 08:00 PM - 09:00 PM (IST) |
|---|---|---|
| Day 1 (Monday): | Decision Trees and Random Forests | Implementing Decision Trees and Random Forests |
| Day 2 (Tuesday): | Classification with Convolutional Neural Networks (CNNs) | Implementing a Basic CNN for Image Classification |
| Day 3 (Wednesday): | Classification Algorithms and Clustering Algorithms | Implementing Classification and Clustering Algorithms |
| Day 4 (Thursday): | Feature Engineering Techniques | Applying Feature Engineering to Datasets |
| Day 5 (Friday): | Loss Functions in Neural Networks | Implementing Different Loss Functions in a Neural Network |
| Day 6 (Saturday): | Computer Vision Applications (OCR, Image Captioning) | Implementing an OCR System |
| Time | 07:00 PM - 09:00 PM (IST) |
|---|---|
| Final Presentation (Monday): | - Each participant or group will present their capstone project, showcasing the application of computer vision and AI/ML techniques learned during the internship. |