[Ipython Notebook]Introduction to Machine Learning with scikit-learn

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Learning from data is close to what we do, as humans. From our experience, we intuitively learn general facts and relations about the world, even if we don't fully understand its complexity. The increasing computational power of computers makes them able to learn from data, too. That's the heart of machine learning, a modern and fascinating branch of artificial intelligence, computer science, statistics, and applied mathematics.

This featured recipe is a hands-on introduction to the most fundamental concepts in machine learning. These concepts are routinely used by data scientists. We will illustrate them withscikit-learn, a popular and user-friendly Python package for machine learning.

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2016-8-19 02:30:20 上传

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关键词：scikit-learn introduction troduction Notebook Learning understand complexity experience computers learning

1. 1.First, let's make all the necessary imports.
   
2. import numpy as np
   
3. import scipy.stats as st
   
4. import sklearn.linear_model as lm
   
5. import matplotlib.pyplot as plt
   
6. %matplotlib inline
   
7. 2.We now define the deterministic function underlying our generative model.
   
8. f = lambda x: np.exp(3 * x)
   
9. 3.We generate the values along the curve on [0,2][0,2].
   
10. x_tr = np.linspace(0., 2, 200)
    
11. y_tr = f(x_tr)
    
12. 4.Now, let's generate our data points within [0,1][0,1]. We use the function ff and we add some Gaussian noise.
    
13. x = np.array([0, .1, .2, .5, .8, .9, 1])
    
14. y = f(x) + np.random.randn(len(x))
    
15. 5.Let's plot our data points on [0,1][0,1].
    
16. plt.figure(figsize=(6,3));
    
17. plt.plot(x_tr[:100], y_tr[:100], '--k');
    
18. plt.plot(x, y, 'ok', ms=10);

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kankan

kankan

期待期待。。。。。。。。。。。。

check it out and thanks, buddy

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thanks for sharing

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