Introduction to Machine Learning with Python, Early Release, Mueller A.C., Guido S., 2016

Introduction to Machine Learning with Python, Early Release, Mueller A.C., Guido S., 2016.

   Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence and computer science, which is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become ubiquitous in everyday life. From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core.




Summary.
Lets summarize what we learned in this chapter. We started off formulating a task of predicting which species of iris a particular flower belongs to by using physical measurements of the flower. We used a dataset of measurements that was annotated by an expert with the correct species to build our model, making this a supervised learning task. There were three possible species, Setosa, Versicolor or Virginica, which made the task a three-class classification problem. The possible species are called classes in the classification problem, and the species of a single iris is called its label

The dataset consists of two numpy arrays, one containing the data, which is referred to as X in scikit-learn, and one containing the correct or desired outputs, which is called y. The array X is a two-dimensional array of features, with one row per data point, and one column per feature. The array у is a one-dimensional array, which here contained one class label from 0 to 2 for each of the samples.

ОГЛАВЛЕНИЕ.
1. Introduction.
2. Supervised Learning.
3. Unsupervised Learning and Preprocessing.
4. Summary of scikit-learn methods and usage.
5. Representing Data and Engineering Features.
6. Model evaluation and improvement.
7. Algorithm Chains and Pipelines.
8. Working with Text Data.



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