AI Engineering

Unlock the power of Artificial Intelligence with this comprehensive course! Gain the technical skills and conceptual understanding needed to engineer modern AI solutions. We'll guide you through the entire AI development lifecycle, blending fundamental machine learning (ML) techniques with advanced natural language processing (NLP) and the latest generative AI (GenAI) models.

You'll start with the foundations of machine learning, exploring regression, classification, and clustering algorithms. Learn how to effectively evaluate, tune, and design robust ML pipelines.

Next, dive deep into natural language processing (NLP). Master techniques like tokenization, word embeddings, and transformers. Explore deep learning for NLP, including RNNs, LSTMs, attention mechanisms, BERT, and GPT for powerful text analysis.

The course culminates in hands-on experience with generative AI. Discover text, image, and code generation using cutting-edge models like GPT, diffusion models, and multimodal transformers. We'll also cover crucial aspects of responsible AI, including model interpretability and bias mitigation.

Finally, learn end-to-end AI engineering, covering MLOps, APIs, and deployment strategies to get your AI systems into production.

Key Topics Covered:

• Foundations of Machine Learning: Regression, classification, clustering.
• Model Evaluation & Tuning: Learn best practices for optimizing your models.
• Pipeline Design: Build efficient and scalable AI workflows.
• Natural Language Processing (NLP): Tokenization, word embeddings, transformers, and more.
• Deep Learning for NLP: RNNs, LSTMs, attention mechanisms, BERT, GPT.
• Generative AI: Text, image, and code generation with GPT, diffusion models, and multimodal transformers.
• Responsible AI: Model interpretability, bias mitigation, and ethical considerations.
• End-to-End AI Engineering: MLOps, API development, and deployment strategies.

Learning Outcomes:

• Design and train ML and NLP models using modern frameworks.
• Build and fine-tune generative models for creative AI applications.
• Develop and deploy AI systems in production environments.
• Understand the ethical and societal implications of AI systems.