May 2018
Beginner to intermediate
364 pages
7h 43m
English
Content preview from Hands-On Data Science with Anaconda
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Summary
In this chapter, we have discussed unsupervised learning. In particular, we have explained hierarchical clustering and k-means clustering. As for R and Python, we have explained several related packages:
- R: rattle, Rmixmod, and randomUniformForest
- Python: scipy.cluster, contrastive, and sklearn
Several real-world examples have also been used to illustrate the applications of these packages in detail.
For the next chapter, we will discuss supervised learning, such as classification, the k-nearest neighbors algorithm, Bayes' classifiers, reinforcement learning, and specific R and Python-related modules, such as RTextTools and sklearn. In addition, we will discuss implementation via R, Python, Julia, and Octave.
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.