Machine Learning & AI Track : 4 – 5 months
Week 1-2
- Class 1-2 Python for ML (NumPy, Pandas, Matplotlib)
- Class 3-4 Linear Algebra Basics (Vectors, Matrices, Operations)
- Class 5-6 Probability & Statistics Basics
- Class 7-8 Data Preprocessing (Cleaning, Normalization, Encoding)
Week 3-4
- Class 9-10 Intro to ML, Supervised vs Unsupervised Learning
- Class 11-12 Linear Regression (Theory + Implementation)
- Class 13-14 Logistic Regression & Evaluation Metrics
- Class 15-16 Decision Trees & Random Forests
Week 5-6
- Class 17-18 KNN, Naïve Bayes
- Class 19-20 Support Vector Machines (SVM)
- Class 21-22 Clustering (K-Means, Hierarchical)
- Class 23-24 Dimensionality Reduction (PCA, t-SNE)
Week 7-8
Class 25-26 Neural Networks Basics, Activation Functions
Class 27-28 Backpropagation & Gradient Descent
Class 29-30 Building Neural Networks with TensorFlow/Keras
Class 31-32 CNN Basics for Image Recognition
Week 9-10
Class 33-34 RNN & LSTMs for Sequence Data
Class 35-36 Transformers & Attention Mechanisms (Intro)
Class 37-38 GANs (Generative Adversarial Networks)
Class 39-40 Transfer Learning & Pre-trained Models
Week 11-12
Class 41-42 NLP Fundamentals (Text Preprocessing, Embeddings)
Class 43-44 Sentiment Analysis Project
Class 45-46 Computer Vision Project (Image Classifier)
Class 47-48 Reinforcement Learning Basics
Week 13-15
Class 49-52 End-to-End ML Project (Data ® Model ® Deployment)
Class 53-56 AI Mini Projects (Chatbot / Recommendation System)
Class 57-60 Hackathon Preparation & Case Studies
Week 16-18
Class 61-64 Model Deployment (Flask, FastAPI, Streamlit)
Class 65-68 Cloud ML (AWS/GCP basics)
Class 69-71 Portfolio Building, Resume Review
Class 72 Internship & Placement Guidance
