Introduction to Machine Learning with TensorFlow

Build powerful machine learning models to make predictions and uncover hidden patterns. Start with foundational supervised learning algorithms including linear regression, decision trees, naive Bayes, support vector machines (SVMs), and perceptrons, then evaluate your model performance with a variety of evaluation metrics. Then you'll advance from perceptrons to deep neural networks in order to perform supervised learning on complex data sources such as images. Finally, you'll dive into unsupervised learning methods, including clustering and dimensionality reduction for customer segmentation. For each technique, you'll start by learning the underlying math, then implement real-world models with Python libraries including TensorFlow and scikit-learn.