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Cs229-2018-autumn Alternatives
Similar projects and alternatives to cs229-2018-autumn
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cs229-2019-summer
All notes and materials for the CS229: Machine Learning course by Stanford University
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stanford-CS229
Discontinued Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng [UnavailableForLegalReasons - Repository access blocked]
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WorkOS
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stanford-cs229
Discontinued 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford (by zyxue)
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Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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probability
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The official Python client for the Huggingface Hub.
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nn
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Coursera-Machine-Learning-Stanford
Machine learning-Stanford University
cs229-2018-autumn reviews and mentions
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Are there any books I should read to learn machine learning from scratch?
For machine learning (not deep learning), I recommend the lecture notes from Stanford's CS229 course. The reason I really like these notes is because you can find past problem sets that went along with them, and the problem sets are very good: difficult but not impossible, and close to a 50/50 mix of math and programming. I never feel like I've learned a topic just from reading about it, so having good problems to go along with the reading was very important to me.
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I'm a highly motivated undergrad from a 3rd world country who is unable to afford the paid version of new Andrew NG course. What can I do about the labs?
Ng's updated lectures for CS229 are also available on youtube as well as the latest syllabus and notes (from https://cs229.stanford.edu/). Don't worry if you can't access the Coursera version. It's watered down for the mass audience and you can find plenty of top quality material online for free from Stanford, Berkeley, CMU etc.
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Are there any good books or videos for beginners?
I would usually recommend starting with Stanford's lectures and when you reach Linear regression you can switch to previous year's. I find 2018 lectures to be much more accessible but 2019 presents some basic concepts in the first lectures that are useful if you don't have the background. Alternatively, there is Caltech's Machine Learning Course.
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The primary programming language of cs229-2018-autumn is Jupyter Notebook.
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