Ai Engineering By Yusuf Didighar
 

AI Engineering with Machine Learning, NLP, and Generative AI
What you'll learn
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 ethical and societal implications of AI systems.
Requirements
Python programming basics, numpy, pandas, matplotlib
Description
This comprehensive course is designed to equip learners with the technical skills and conceptual understanding required to engineer modern AI solutions. Covering the full AI development lifecycle, the course blends core machine learning (ML) techniques with advanced natural language processing (NLP) and cutting-edge generative AI (GenAI) models.Students will begin with foundational concepts in supervised and unsupervised learning, progressing through real-world model development, deployment, and optimization. They will then explore NLP techniques such as text preprocessing, embeddings, sentiment analysis, and language modeling. The course culminates with hands-on experience in building and deploying generative AI applications using models like GPT, diffusion models, and multimodal transformers.Key Topics Covered:Foundations of Machine Learning (regression, classification, clustering)Model evaluation, tuning, and pipeline designNatural Language Processing (tokenization, word embeddings, transformers)Deep learning for NLP (RNNs, LSTMs, attention, BERT, GPT)Generative AI concepts and applications (text, image, and code generation)Responsible AI, model interpretability, and bias mitigationEnd-to-end AI engineering: MLOps, APIs, and deploymentLearning Outcomes:By the end of this course, learners will be able to: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 ethical and societal implications of AI systems.
Who this course is for