Machine Learning with Scikit-learn, PyTorch, and Hugging Face

Modern artificial intelligence spans everything from simple linear regressions to complex language models. This course bridges the gap between theory and practice, providing a comprehensive introduction to the tools that power today's AI innovations. You will progress from basic data analysis to understanding the architecture of modern neural networks and generative systems. Through clear explanations and code-based examples, you will gain the confidence to handle data and build models that solve real-world problems. By the end of this program, you will have a functional understanding of how to select, train, and refine models for various applications. What you'll learn: - Understand the fundamental concepts of supervised and unsupervised learning with Scikit-learn. - Implement deep learning architectures and manage data tensors using PyTorch. - Apply pre-trained transformer models for natural language tasks using the Hugging Face ecosystem. - Practice essential data cleaning, feature scaling, and model validation techniques. - Explore modern generative AI concepts including vector embeddings and fine-tuning strategies. - Develop a workflow for evaluating model performance and ensuring model reliability. The course starts with foundational definitions and the machine learning lifecycle, moving through classical algorithms into advanced deep learning topics. It is specifically designed for beginners who want a clear, text-based approach to learning without any prerequisites. Begin building your technical expertise in machine learning now.