Comprehensive Data Science and Deep Learning program focused on TensorFlow and practical application. 12 modules: Python for Data Science (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn), statistical analysis and A/B testing, SQL for analysts (BigQuery, PostgreSQL), data visualization (Tableau, Looker, Plotly), ML fundamentals (linear regression, decision trees, Random Forest, XGBoost, Gradient Boosting), TensorFlow Fundamentals (tf.data, tf.keras, eager execution, graph mode), Computer Vision (CNN, ResNet, EfficientNet, YOLO, Detectron2, ImageNet, transfer learning, data augmentation), NLP (Transformers, BERT, GPT, T5, LSTM, attention mechanisms, Hugging Face, spaCy, NLTK), recommendation systems (collaborative filtering, matrix factorization, neural CF, two-tower models, YouTube DNN), production ML (TF Serving, TF Lite, TF.js, MLflow, Kubeflow, Vertex AI, MLOps, feature stores, model monitoring), time series (ARIMA, Prophet, LSTMs, Transformers for time series, anomaly detection), Capstone Project — full ML product from EDA to deployment. Each module includes Jupyter notebooks, hands-on tasks on Kaggle datasets, TensorFlow Developer Certificate preparation.