AI/ML Placement Bootcamp

AI/ML Placement Bootcamp


Week 1: Foundations of AI and Machine Learning

Day 1: Introduction to AI and ML

Day 2: Setting up Python for Data Science

Day 3: Basic Python for Data Science

Day 4: Introduction to Basic Statistics for ML

(asymptotic vs practical)

Day 5: Introduction to Mathematics for ML

Week 2: Supervised Learning with Python

Day 1-2: Linear Regression with scikit-learn

Day 3: Polynomial Regression

Day 4: Decision Trees and Random Forests

Day 5: Model Evaluation and Cross-Validation with Python

Week 3: Foundations of AI and Machine Learning

Day 1: Introduction to Supervised Learning

Day 2: Linear Regression

Day 3: Logistic Regression

Day 4: Unsupervised Learning Overview

Day 5: Clustering Algorithms

Week 4: Core Machine Learning

Day 1: Feedforward Neural Networks

Day 2: Backpropagation and Training Neural Networks

Day 3: Practical Implementation of Neural Networks

Day 4: Convolutional Neural Networks (CNNs)

Day 5: Training CNNs for Image Processing

Week 5: Advanced Machine Learning and Deep Learning

Day 1: Advanced Deep Learning Techniques

Day 2: Transfer Learning

Day 3: Generative Adversarial Networks (GANs)

Day 4: Sequence-to-Sequence Models

Day 5: Natural Language Processing

Week 6: Reinforcement Learning and Applied AI

Day 1: Introduction to Reinforcement Learning (RL)

Day 2: Markov Decision Processes (MDPs)

Day 3: Q-Learning

Day 4: Deep Reinforcement Learning

Day 5: Applied AI in Real-world Projects

Week 7: Final Projects and Deployment

Day 1: Final Project Introduction and Team Formation

Day 2: Final Project Planning and Development

Day 3-4: Final Project Development

Day 5: Final Project Development and Testing

Week 8: Career Development and Finalization

Day 1: Building an Effective Resume

Day 2: Creating a Strong LinkedIn Profile

Day 3: Job Search Strategies and Networking

Day 4: Mock Interviews and Feedback

Day 5: Course Conclusion, Graduation, and Next Steps