Data Mining with Python: Implementing Classification and Regression

Data Mining with Python: Implementing Classification and Regression

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Saimadhu Polamuri

3 customer reviews
A practical guide that will give you hands-on experience with the popular Python data mining algorithms
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Video Details

ISBN 139781785885716
Course Length2 hours and 3 minutes

Video 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.

Table of Contents

Introduction to Data Mining
The Course Overview
A Brief Introduction to Data Mining
Data Mining Basic Concepts and Applications
Setting Up the Data Mining Python Packages Environment
Why Python?
Basics of Python
Installing IPython
Installing the Numpy Library
Installing the pandas Library
Installing Matplotlib
Installing scikit-learn
Cleaning Data and Preprocessing Techniques
Data Cleaning
Data Preprocessing Techniques
Linear Regression Model
Linear Regression Basic Model Approach
Evaluating Regression Models
Basic Regression Model Implementation to Predict House Prices
Regression Model Implementation to Predict Television Show Viewers
Classification Concepts
Logistic Regression
K – Nearest Neighbors Classifier
Support Vector Machine
Logistic Regression Model Implementation
K – Nearest Neighbor Classifier Implementation

What You Will Learn

  • 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

Authors

Table of Contents

Introduction to Data Mining
The Course Overview
A Brief Introduction to Data Mining
Data Mining Basic Concepts and Applications
Setting Up the Data Mining Python Packages Environment
Why Python?
Basics of Python
Installing IPython
Installing the Numpy Library
Installing the pandas Library
Installing Matplotlib
Installing scikit-learn
Cleaning Data and Preprocessing Techniques
Data Cleaning
Data Preprocessing Techniques
Linear Regression Model
Linear Regression Basic Model Approach
Evaluating Regression Models
Basic Regression Model Implementation to Predict House Prices
Regression Model Implementation to Predict Television Show Viewers
Classification Concepts
Logistic Regression
K – Nearest Neighbors Classifier
Support Vector Machine
Logistic Regression Model Implementation
K – Nearest Neighbor Classifier Implementation

Video Details

ISBN 139781785885716
Course Length2 hours and 3 minutes
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