Description

From Python data science fundamentals to advanced deep learning, this course covers NumPy, Pandas, Scikit-learn, TensorFlow, and NLP. Build real-world ML models, deploy them with Flask, and learn MLOps best practices.

Course Requirements

• Intermediate Python programming knowledge
• Basic understanding of statistics and linear algebra is helpful
• A computer with at least 8GB RAM (GPU optional)
• Curiosity and willingness to experiment with data

What You Will Learn

• Perform EDA and data wrangling with NumPy and Pandas
• Build and evaluate classification, regression, and clustering models
• Apply ensemble methods: XGBoost, Random Forest, LightGBM
• Design deep learning models with TensorFlow and Keras
• Build CNNs for image tasks and LSTMs for sequence data
• Implement NLP pipelines including BERT-based models
• Deploy ML models as REST APIs using Flask and FastAPI
• Track experiments and manage model lifecycle with MLflow