From Foundations to Production - Complete AI/ML Journey
Master Python from scratch with hands-on coding exercises. Build a strong programming foundation essential for AI/ML development.
Build mathematical intuition required for machine learning. Learn Linear Algebra, Calculus, Statistics, and Probability with practical applications.
Master data manipulation with NumPy and Pandas. Create stunning visualizations and perform exploratory data analysis on real datasets.
Learn to query, manipulate and manage data using SQL. Understand database design principles and work with both SQL and NoSQL databases.
Dive into supervised and unsupervised learning. Build and evaluate ML models using scikit-learn. Master feature engineering and model selection.
Explore ensemble methods, time series analysis, and recommender systems. Learn advanced techniques for model optimization and hyperparameter tuning.
Master neural networks from scratch. Learn CNNs for image processing and RNNs for sequential data. Implement deep learning with TensorFlow and PyTorch.
Build end-to-end computer vision applications. Master object detection, image segmentation, face recognition, and video analysis techniques.
Process and analyze text data at scale. Learn word embeddings, transformers, BERT, and build applications like chatbots and sentiment analyzers.
Explore the world of Generative AI. Master GPT architecture, prompt engineering, LLM fine-tuning, and build RAG systems for enterprise applications.
Take your ML models to production. Learn deployment strategies, containerization with Docker, orchestration, CI/CD pipelines, and model monitoring.
Build an end-to-end AI/ML project from scratch. Create your professional portfolio, prepare for technical interviews, and showcase your expertise.