Practical journey into Data Science & AI with real projects, labs, and deployment skills to launch your career
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What you'll learn
- Clean, analyze, and prepare data for real-world machine learning and AI applications.
- Build and evaluate machine learning models for regression, classification, clustering, and recommendation systems.
- Apply deep learning techniques such as neural networks, CNNs, RNNs, and generative AI for advanced use cases.
- Work confidently with Python, Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch to solve end-to-end problems
- Perform feature engineering, model optimization, and hyperparameter tuning to improve accuracy
- Deploy models into production using APIs (FastAPI, Flask), Docker, and Streamlit dashboards.
- Understand the basics of MLOps, including model monitoring, performance tracking, and drift detection.
- Tackle real-world projects and a capstone, gaining the confidence to showcase a complete portfolio to employers.
- Translate technical outputs into business insights and decision-making strategies.
- Prepare for career roles like Data Scientist, Machine Learning Engineer, or AI Specialist.