Build practical machine learning skills with Python and TensorFlow.

Progress from data preparation and visualization to predictive models and neural networks.

This Specialization provides a structured pathway for developing practical machine learning skills, beginning with core concepts, Python development environments, and numerical computing using NumPy. You will learn how machine learning systems use data to identify patterns and solve predictive problems.

You will then work with Pandas, Matplotlib, Seaborn, and Scikit-learn to clean, organize, analyze, visualize, and preprocess datasets. Through hands-on workflows, you will handle missing values, combine data, explore statistical patterns, and prepare reliable model-ready information.

As you progress, you will apply feature engineering, build and fine-tune regression models, and develop TensorFlow skills using tensors, variables, matrix operations, cost functions, and optimizers. You will then build classification models and neural networks while exploring practical applications such as housing prediction and MNIST handwritten-digit classification.

By the end, you will be able to move from raw data to trained machine learning and neural network models using an integrated Python and TensorFlow workflow.

Applied Learning Project

Learners will complete hands-on projects involving data cleaning, visualization, feature engineering, regression, classification, and neural network development. They will apply Python, Scikit-learn, and TensorFlow to prepare real-world datasets, optimize models, and solve practical predictive problems.