Visualizing Data and Building Regression Models in TensorFlow and Keras

Understanding how to generate, visualize, and model data is the cornerstone of successful machine learning. Before diving into complex deep learning architectures, you must master the foundations of data patterns and simple regression. This text-based course guides you through the process of working with uniform data, visualizing relationships with Matplotlib, and training your first linear regression models. You will gain a solid intuitive understanding of how neural networks learn to fit data points, preparing you for more advanced machine learning challenges. What you'll learn: Understand the core concepts of uniform data generation and basic statistical distributions; Create clear data visualizations using Matplotlib to inspect your datasets; Configure a foundational linear regression model using TensorFlow and Keras; Train and evaluate your neural network using modern optimization techniques; Analyze model performance by plotting loss curves and prediction lines; Apply best practices for structured data preparation and modern Keras API workflows. You will start with key terminology and foundational concepts of data generation and visualization. From there, you will progress to building, compiling, training, and testing a linear regression model step-by-step through clear written explanations and structured code examples. Designed for beginners, this course requires no prior machine learning experience, though basic familiarity with Python is helpful. Start building and visualizing your first machine learning models today.