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.
About The Author
Saimadhu Polamuri is a data science educator and the founder of Data
Aspirant, a Data Science portal for beginners. He has 3 years of
experience in data mining and 5 years of experience in Python. He is
also interested in big data technologies such as Hadoop, Pig, and Spark.
He has a good command of the R programming language and Matlab. He has a
rudimentary understanding of Cpp Computer vision library (opencv) and
big data technologies.
