Predicting Data with TensorFlow and Keras: A Practical Guide

Want to understand how machine learning models make accurate numerical predictions? This course teaches you how to harness TensorFlow and Keras to analyze data trends, build predictive models, and calculate prediction errors. You will transition from a complete beginner to confidently writing code that trains neural networks to predict data points, evaluate model performance, and minimize loss. What you'll learn: Understand the foundational math and concepts behind machine learning and data prediction; Configure neural networks using Keras sequential models to handle regression tasks; Analyze and minimize loss functions to improve model accuracy over training epochs; Prepare and preprocess structured data using modern Python libraries; Evaluate model predictions against actual solutions using clear metrics; Implement best practices for training, validation, and preventing overfitting. You will start with essential terminology, learning how machine learning models define predictions and errors. From there, you will read through step-by-step code implementations to build, compile, train, and test your own predictive models. This course is designed for absolute beginners to machine learning and TensorFlow. No prior data science experience is required, though a basic familiarity with Python is helpful. Start reading today to unlock the power of data prediction with TensorFlow.