Introduction to Machine Learning with Pytorch
Kaggle , Amazon Web Services and Amazon via Udacity Nanodegree
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Overview
Syllabus
- Introduction to Machine Learning
- Welcome to Machine learning with Pytorch
- Supervised Learning
- From theory to application, this course guides you through supervised learning essentials. Learn to select, implement, and refine models that solve complex classification and regression tasks.
- 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
- Learn to apply unsupervised learning methods like K-means and Gaussian mixtures to extract value from raw data. Develop skills in feature extraction and cluster validation to enhance data analysis.
- 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
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 249 ratings
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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 -
"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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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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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.
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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.