Introduction to Machine Learning with Pytorch

The Intro to Machine Learning with Pytorch program covers machine learning concepts and techniques, with a focus on supervised and unsupervised learning. The program includes three courses and covers topics such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. The courses include projects that allow learners to apply these techniques to real-world problems, such as identifying potential donors for a charity and clustering customers based on their spending habits. The program uses Python and PyTorch for implementation and includes lessons on model evaluation and tuning.

• Introduction to Machine Learning
• Welcome to Machine learning with Pytorch
• 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 PyTorch
• Learn the fundamentals of neural networks with Python and PyTorch, 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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