Description

AI/ML Development

Duration: 45 Days (Excluding Sundays)

Core Focus: Build strong foundations in machine learning, deep learning, MLOps, and generative AI, with hands-on projects and deployment on cloud.

Week 1: Foundations of AI & ML

• Python for ML: NumPy, Pandas, Scikit-learn basics.

• Statistics & probability for data science.

• Data preprocessing, cleaning, feature engineering.

• Exploratory Data Analysis (EDA) & visualization with Matplotlib/Seaborn.

Week 2: Machine Learning Algorithms

• Supervised learning: regression, classification, SVMs, decision trees.

• Unsupervised learning: clustering (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE).

• Ensemble methods: Random Forests, XGBoost, LightGBM.

• Model evaluation: cross-validation, metrics (AUC, F1, RMSE).

Week 3: Deep Learning & Neural Networks

• Fundamentals of neural networks, backpropagation.

• Deep learning with TensorFlow and PyTorch.

• CNNs for computer vision tasks.

• RNNs, LSTMs, GRUs for sequence modeling.

Week 4: Advanced AI Techniques

• Transformers and attention mechanisms.

• Large Language Models (LLMs): BERT, GPT family basics.

• Generative AI: diffusion models, GANs, image synthesis.

• Transfer learning and fine-tuning pre-trained models.

Week 5–6: Capstone Project — AI-Powered Application

• Problem definition, data pipeline setup.

• Model development, training, and hyperparameter tuning.

• Model explainability: SHAP, LIME.

• Deploying the model via REST API/Streamlit/Gradio.

• Observability: model monitoring, drift detection.

Week 7: MLOps & Cloud AI

• ML pipelines with MLflow/Kubeflow.

• CI/CD for ML models.

• Containerization with Docker, orchestration with Kubernetes.

• Deploying to cloud platforms: AWS Sagemaker, Azure ML, GCP Vertex AI.

Week 8: Advanced Topics & Wrap-Up

• Responsible AI: ethics, fairness, and bias in ML.

• Optimization & scaling for large datasets.

• Edge AI: deploying models on mobile & IoT.

• Final demo: AI solution with end-to-end pipeline and cloud deployment.