Machine Learning Foundations with Scikit-Learn and PyTorch

Machine learning is reshaping how we solve complex problems, but getting started with the right tools can feel overwhelming. This text-based course simplifies the journey by introducing you to the two most powerful libraries in Python: Scikit-Learn for classical machine learning and PyTorch for deep learning. You will transition from understanding core data concepts to building, evaluating, and tracking your own predictive models. By reading through clear, step-by-step explanations and structured code examples, you will gain the practical skills needed to tackle real-world data challenges. What you'll learn: Understand fundamental machine learning concepts, terminology, and data preprocessing workflows; Build and evaluate classical models like regression, decision trees, and classifiers using Scikit-Learn; Design, train, and fine-tune basic neural networks from scratch using PyTorch; Apply modern model evaluation techniques and basic experiment tracking to monitor performance; Implement best practices in data pipeline design for clean, reproducible code. The course begins with essential definitions and data preparation techniques, ensuring you have a solid foundation. From there, you will progress from classical statistical models to deep learning architectures, learning how to structure your code efficiently at every step. This course is designed for beginners who are new to machine learning and want a clear, conceptual introduction. No prior experience with PyTorch or Scikit-Learn is required, though a basic familiarity with Python is helpful. Start reading today to build your foundation in machine learning and deep learning.