Build practical machine learning and deep learning skills with TensorFlow through a structured path from data preparation to neural network development. You’ll begin by transforming raw data through feature encoding, custom preprocessing, and feature engineering, then apply linear regression techniques to improve predictive performance.

Next, you’ll explore TensorFlow fundamentals, including tensors, operations, variables, core concepts, and eager execution. You’ll construct, train, optimize, and evaluate linear and logistic regression models using cost functions and optimizers. Finally, you’ll build a neural network that uses activation and classification functions to recognize handwritten digits from the MNIST dataset. This course is designed for beginners entering machine learning and professionals who want to strengthen their TensorFlow knowledge. Its focused progression connects classical machine learning preparation directly to TensorFlow model development through clear explanations, coding practice, case studies, and assessments. Enroll to gain hands-on experience with feature engineering, regression models, model optimization, neural networks, and image classification in TensorFlow.