Data Mining

Learn Data Mining from basics in this free online training. This free Data Mining course is taught hands-on by experts. Learn about Data Description, Data Manipulation, Skewness & a lot more. Best for Beginners. Start now!

4.52
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Intermediate

Level

3.75 Hrs

Learning hours

36.8K+
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About this Course

This Data Mining course will introduce you to prominent Data Mining concepts. The course begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the course. You will then learn to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this course. There is an assessment to evaluate your knowledge at the end of the course. Complete the course for free and avail your certificate. You can also study the attached materials for reference. 
 

After this free, self-paced, intermediate-level guide to Data Mining, you can enroll in the Data Science course and embark on your career with the professional Post Graduate certificate. Learn various concepts in depth with millions of aspirants across the globe!

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Course Outline

Data Description

You will learn mathematics concepts for data mining tasks such as statistics, its types, population, parameter, sample, mean, median, mode, normal distribution, interquartile range of IQR, and its upper and lower limits. This section comprehends a demonstration of the outlier concept at the end of the course for your better understanding.

Basic Data Understanding

You shall understand data and learn to infer insights from the datasets using the diabetes dataset in this section.

Data Manipulation

This section explains how to work with or manipulate the data with different methods in a given set to extract a particular range of values. You will also understand how a dataset not showing accurate data can be recognized and be replaced with the median since it does not get affected by outliers.

Skewness using Histogram/ Density Curves

You shall understand the outlier concept in-depth in this section. You will learn to detect and impute outliers and understand their working later in this section. You will also learn to infer/express data using the histogram. 

 

Visualising outliers using boxplots

You will learn to express missing data and express data in box plots for simple representation and also understand outlier analysis concepts in this section. 

 

Visualising correlation using Scatter Plots/ Heat map

You will learn to represent correlation with different methods and scatter plots or heat maps using automobile dataset to perform exploratory data analysis in this section. 

 

What is Machine Learning?

This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning. 

Introduction to Regression Analysis

This section shall define regression, brief different types of regression, and then explain what regression analysis is in machine learning. You will learn to work with regression analysis to understand the data better. 

Linear Regression Demo

This section shall explain simple linear regression. You will learn to import classes and packages and work with Google Colaboratory to understand linear regression better. 

Multiple Linear Regression Demo

 You will understand the concept of multiple data points, to begin with in this section and then learn to work with multiple linear regression with NumPy.

Salary Prediction Demo

You will learn to work with the dataset by understanding a project on Salary Prediction. You will also learn to work with NumPy, Pandas, and Matplotlib library for the project. 

Introduction to Logistic Regression

You will learn a supervised learning technique to classify the data based on the classifying points and logistic regression in this section. 

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Data Mining

3.75 Learning Hours . Intermediate

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