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manning

Repository for the book Grokking Machine Learning, by Manning Editors

TutorialsML/AI fundamentalsJupyter Notebook
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创建于 2019-03-25 · 更新于 2026-10-15 · 今日第 6065 名
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luisguiserrano/manning 排名第 6,065,有 900 个星标。

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luisguiserrano/manning 排名第 6,065,此处有 8 个同类条目;vndee/local-talking-llm 排名第 6,077。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别ML/AI fundamentals
星标数900
分叉数379
贡献者2
创建日期2019-03-25
更新日期2026-10-15
1 天星标数1
7 天星标数1
排名6,065
上次排名12,695
排名变化6,630
7 天星标排名10,472
上次 7 天星标排名13,624
7 天星标排名变化3,152
总星标排名12,085
上次总星标排名12,073
总星标排名变化-12
国家Canada
README 摘录

Grokking Machine Learning Book Repository This is the repo for the book "Grokking Machine Learning", available here. Get it with a 40% discount code: **serranopc** Chapters: 1. Drawing a line close to our points: **Linear regression** (code) 4. Optimizing the training process: **Underfitting**, **overfitting**, **testing**, and **regularization** (code) 5. Using lines to split our points: **The perceptron algorithm** (code) 6. A continuous approach to splitting points: **Logistic classifiers** (code) 7. How do you measure classification models? Using probability to its

maximum: The **Naive Bayes** model (code) 9. Splitting data by asking questions: **Decision trees** (code) 10. Combining building blocks to gain more power: **Neural networks** (code) 11. Finding boundaries with style: **Support vector machines** and the **kernel method** (code) 12. Combining models to maximize results: **Ensemble learning** (code) 13. Putting it all in practice: A real life example of **data engineering** and **machine learning** (code)

常见问题

记录了多少星标和分叉?

luisguiserrano/manning 有 900 个星标和 379 个分叉。

仓库排名是多少?

luisguiserrano/manning 排名第 6,065。

记录的语言和类别是什么?

luisguiserrano/manning 有 900 个星标,语言为 Jupyter Notebook,类别为 Tutorials。

哪个仓库排名最接近?

luisguiserrano/manning 与 vndee/local-talking-llm 比较,后者排名第 6,077,有 894 个星标。

README

Grokking Machine Learning Book Repository

This is the repo for the book "Grokking Machine Learning", available here.

Get it with a 40% discount code: serranopc

Chapters:

  1. What is machine learning?
  2. Types of machine learning
  3. Drawing a line close to our points: Linear regression (code)
  4. Optimizing the training process: Underfitting, overfitting, testing, and regularization (code)
  5. Using lines to split our points: The perceptron algorithm (code)
  6. A continuous approach to splitting points: Logistic classifiers (code)
  7. How do you measure classification models?: Accuracy and its friends
  8. Using probability to its maximum: The Naive Bayes model (code)
  9. Splitting data by asking questions: Decision trees (code)
  10. Combining building blocks to gain more power: Neural networks (code)
  11. Finding boundaries with style: Support vector machines and the kernel method (code)
  12. Combining models to maximize results: Ensemble learning (code)
  13. Putting it all in practice: A real life example of data engineering and machine learning (code)
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