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Data Mining with Python: Classification and Regression

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Data Mining with Python: Classification and Regression. A practical guide that will give you hands-on experience with the popular Python data mining algorithms

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Descripción

The name of this course is Data Mining with Python: Classification and Regression. The knowledge you will get with this indescribable online course is astonishing. A practical guide that will give you hands-on experience with the popular Python data mining algorithms.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Packt Publishing, one of the very best experts in this field.

Description of this course: Data Mining with Python: Classification and Regression

Course Description 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 AuthorSaimadhu 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.

Requirements of this course: Data Mining with Python: Classification and Regression

What are the requirements? A rudimentary knowledge of Python and its libraries would be useful.

What will you learn in this course: Data Mining with Python: Classification and Regression?

What am I going to get from this course? Understand the basic data mining concepts to implement efficient models using Python Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learn Build your first application that makes predictions from data and see how to evaluate the regression model Analyze and implement Logistic Regression and the KNN model Dive into the most effective data cleaning process to get accurate results Master the classification concepts and implement the various classification algorithms

Target audience of this course: Data Mining with Python: Classification and Regression

Who is the target audience? This book is for data analysts or aspiring data scientists who want to learn more about data mining with Python.

Información adicional

Instructor

Packt Publishing

Lectures

21

Length

2

Skill Level

Beginner Level

Languages

English

Includes

Lifetime access <br/> 30 day money back guarantee! <br/> Available on iOS and Android <br/> Certificate of Completion

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