Build real-world AI systems—from machine learning fundamentals to MLOps deployment pipelines.
← Back to HomepageMaster the basics of Python, NumPy, and pandas.
Linear algebra, calculus, probability, and statistics.
Supervised, unsupervised, and model evaluation.
Neural networks, activation functions, and backpropagation.
Build and train models using industry frameworks.
Image classification, object detection, and CNNs.
Tokenization, embeddings, and attention models.
Accuracy, precision, recall, confusion matrix, AUC.
ETL pipelines, cleaning, and feature engineering.
Build and consume RESTful APIs for model serving.
Flask, FastAPI, Docker, and web integration.
Deploy and monitor models in production environments.
CI/CD pipelines, version control, and automation for ML.
Problem scoping, data pipelines, and metrics.
Bias, fairness, interpretability, and ethics in AI.
End-to-end AI project with deployment.