Introduction to Machine Learning with TensorFlow

The Introduction to Machine Learning with TensorFlow program covers supervised and unsupervised learning methods for machine learning. Course 1 introduces regression, perceptron algorithms, decision trees, naive Bayes, support vector machines, and evaluation metrics. Course 2 focuses on neural networks and creating an image classifier. Course 3 covers unsupervised learning methods, including clustering and dimensionality reduction for customer segmentation. Key skills include data preprocessing, model selection, and evaluation using TensorFlow and Python.

• Introduction to Machine Learning
• Supervised Learning
• In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.
• Introduction to Neural Networks with TensorFlow
• Learn the fundamentals of neural networks with Python and TensorFlow, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.
• Unsupervised Learning
• In this course, you'll learn how to apply unsupervised learning to solve real-world problems.
• Congratulations!
• Prerequisite: Python for Data Analysis
• Prerequisite: SQL for Data Analysis
• Prerequisite: Command Line Essentials
• Prerequisite: Git & Github
• Additional Material: Python for Data Visualization
• Additional Material: Statistics for Data Analysis
• Additional Material: Linear Algebra
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