Implementing Perceptrons and Logistic Regression with Python

Understanding the core mathematical principles behind machine learning is the key to building successful AI systems. This text-based course guides you through the fundamental building blocks of neural networks and classification models. You will transition from a theoretical understanding of algorithms to writing clean, functional Python code that implements perceptrons and logistic regression models. By working through clear explanations and structured code snippets, you will gain the confidence to apply these techniques to real-world predictive tasks. What you'll learn: Understand the mathematical foundations of the perceptron and logistic regression; Implement a single-layer perceptron from scratch using standard Python; Build logistic regression models for binary classification tasks; Configure basic neural network components using modern TensorFlow and Keras APIs; Apply clean coding practices, including Python type hints, to machine learning scripts; Evaluate model performance using key classification metrics. The course begins with foundational definitions of supervised learning and classification. You will then progress step-by-step from raw Python implementations to leveraging powerful modern libraries for scalable model training. This course is designed for beginner developers, data enthusiasts, and aspiring machine learning engineers who want to understand how algorithms work under the hood. No prior machine learning experience is required, though basic Python knowledge is helpful. Start your journey into deep learning by mastering the core algorithms that power modern AI.