https://github.com/timkong21/machine-learning-specialization
Contains solutions and notes for the Machine Learning Specialization by Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
https://github.com/timkong21/machine-learning-specialization
artificial-neural-networks decision-trees deep-learning gradient-descent linear-regression logistic-regression machine-learning neural-network python recommender-system regularization supervised-learning tensorflow tree-ensembles xgboost
Last synced: about 1 year ago
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Contains solutions and notes for the Machine Learning Specialization by Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
- Host: GitHub
- URL: https://github.com/timkong21/machine-learning-specialization
- Owner: TimKong21
- Created: 2022-08-05T20:15:59.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-02T16:26:35.000Z (about 3 years ago)
- Last Synced: 2025-02-06T07:21:27.993Z (about 1 year ago)
- Topics: artificial-neural-networks, decision-trees, deep-learning, gradient-descent, linear-regression, logistic-regression, machine-learning, neural-network, python, recommender-system, regularization, supervised-learning, tensorflow, tree-ensembles, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 52.9 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Specialization
A brief description of what this project does and who it's for
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
For more information, pleaese refer to [Coursera](https://www.coursera.org/specializations/machine-learning-introduction?).