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Data Mining with Python: Implementing Classification and Regression

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Data Mining with Python: Implementing Classification and Regression

Data Mining with Python: Implementing Classification and Regression

HDRips | MP4/AVC, ~58 kb/s | 1280x720 | Duration: 02:03:25 | English: AAC, 128 kb/s (2 ch) | 297 MB

Genre: Development / Programming

A practical guide that will give you hands-on experience with the popular Python data mining algorithms.
Python is a dynamic programming language used in a wide range of domains by programmers who find it simple yet powerful. In today’s world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning.

In this course, you will discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data. You will also tackle some notorious data mining problems to get a concrete understanding of these techniques.

We begin by introducing you to the important data mining concepts and the Python libraries used for data mining. You will understand the process of cleaning data and the steps involved in filtering out noise and ensuring that the data available can be used for accurate analysis. You will also build your first intelligent application that makes predictions from data. Then you will learn about the classification and regression techniques such as logistic regression, k-NN classifier, and SVM, and implement them in real-world scenarios such as predicting house prices and the number of TV show viewers.

By the end of this course, you will be able to apply the concepts of classification and regression using Python and implement them in a real-world setting.

Data Mining with Python: Implementing Classification and Regression

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