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About 100 Days of ML Code
The document provides summaries of the content covered each day in a 100 day machine learning coding challenge. It discusses various machine learning algorithms and concepts covered each day including simple and multiple linear regression, logistic regression, support vector machines, k-nearest neighbors, naive bayes, decision trees, random forests, and neural networks. It also discusses brushing up on linear algebra, calculus, k-means clustering, and diving deeper into Python data science topics like NumPy, Pandas, and Matplotlib.
Detailed Information
| Author: | ['Duy Linh'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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