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1
Phase 1
Python & Math Foundations
NumPy, Pandas, Matplotlib, linear algebra, statistics, probability & Calculus basics.
NumPyPandasStatistics
2
Phase 2
Machine Learning
Scikit-learn, regression, classification, clustering, model evaluation, feature engineering.
Scikit-learnXGBoostML Pipelines
3
Phase 3
Deep Learning
Neural networks, backpropagation, CNNs, RNNs, LSTMs — PyTorch & TensorFlow.
PyTorchTensorFlowCNNs
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Phase 4
NLP & Computer Vision
Transformers, BERT, text classification, OpenCV, image detection, Hugging Face.
TransformersOpenCVHuggingFace
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Phase 5
LLMs & Prompt Engineering
GPT, Claude, LangChain, RAG pipelines, vector databases (FAISS, Pinecone), agents.
LangChainRAGPinecone
6
Phase 6
Generative AI
Stable Diffusion, fine-tuning LLMs, PEFT & LoRA, multimodal models, AI agents.
Fine-tuningLoRAMultimodal
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Phase 7
MLOps & Production
MLflow, DVC, FastAPI model serving, Docker, AWS/GCP deployment, monitoring.
MLflowDockerFastAPI
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Phase 8
Capstone & Career Launch
End-to-end AI product build, GitHub portfolio, mock interviews, resume & LinkedIn.
PortfolioInterviewsPlacement