Advanced Hugging Face

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

Transformer Neural Networks - EXPLAINED! (Attention is all you need)

Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.

State-of-the-Art Machine Learning Papers Implementation

This course equips you with the skills to build real-world NLP applications using transformer models from the Hugging Face ecosystem. You will gain hands-on experience with speech-to-text pipelines, sentiment analysis, and text generation.

This course teaches you how to use open-source models from the Hugging Face Hub for various tasks like NLP, audio, and image processing. You will learn to use the transformers library to perform these tasks with just a few lines of code and deploy your applications using Gradio and Hugging Face Spaces.

This course, developed in collaboration with Hugging Face, teaches the fundamentals of model quantization. You will learn to compress large models, making them more accessible and efficient, using the Hugging Face Transformers library and Quanto.

You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?Do you want an automated process for developing deep learning solutions?This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namely Deep Learning in Practice I: TensorFlow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects. Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNNs algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition. The student will learn how to build a face recognition app in TensorFlow and Keras.Deep Learning in Practice I: Basics and Dataset Design There are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.I have

Master Machine Learning & AI Engineering — From Data Analytics to Agentic AI Solutions Launch your career in AI with a comprehensive, hands-on course that takes you from beginner to advanced. Learn Python, data science, classical machine learning, and the latest in AI engineering—including generative AI, transformers, and LLM agents / agentic AI.Why This Course?Learn by Doing With over 145 lectures and 21+ hours of video content, this course is built around practical Python projects and real-world use cases—not just theory.Built for the Real World Learn how companies like Google, Amazon, and OpenAI use AI to drive innovation. Our curriculum is based on skills in demand from leading tech employers.No Experience? No Problem Start from scratch with beginner-friendly lessons in Python and statistics. By the end, you’ll be building intelligent systems with cutting-edge AI tools.A Structured Path from Beginner to AI Engineer1. Programming Foundations Start with a crash course in Python, designed for beginners. You’ll learn the language fundamentals needed for data science and AI work.2. Data Science and Statistics Build a solid foundation in data analysis, visualization, descriptive and inferential statistics, and feature engineering—essential skills for working with real-world datasets.3. Classical Machine Learning Explore supervised and unsupervised learning, including linear regression, decision trees, SV Ms, clustering, ensemble models, and reinforcement learning.4. Deep Learning with TensorFlow and Keras Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), using real code examples and exercises.5. Advanced AI Engineering and Generative AI Go beyond traditional ML to learn the latest AI tools and techniques:Transform