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 with Pytorch
Kaggle , Amazon Web Services and Amazon via Udacity Nanodegree
Overview
Syllabus
- 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
- Career Services
Taught by
Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard , Jay Alammar and Andrew Paster
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Reviews
4.0 rating, based on 5 Class Central reviews
4.7 rating at Udacity based on 248 ratings
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"This program so far has exceeded my expectations. First, access to mentors that readily answers questions is so valuable. When learning a new topic it is so easy to get stuck and frustrated, but when you can reach out to someone that can answer you…
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very poor content don't worth the money and the time
it even redirect you to Khan Academy Videos and YouTube videos to cover the important parts -
It is exceeding my expectations so far. My projects have been promptly reviewed and the feedbacks are extremely thorough and constructive.
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I really enjoyed the lesson as they stroke a balance of not going too technical but technically sufficient. However, there have been many bugs/mistakes on quizzes that I have made feedback over which certainly made me annoyed.
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For now, I have finished the machine learning part, which is the first part of the course. First of all, the language of expression was very explanatory, simple and well designed for teaching. Secondly, I will talk about the narrative structure of the subjects again, because explaining the subject and then asking questions or exercises about it and sharing different resources provide better learning. Third, the feedback given in the project evaluation was helpful.
