Best Free Data Mining Courses

Find the best online Free Data Mining Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings.

Introduction to Data Science using Python (Module 1/3)

Learn Data science / Machine Learning using Python (Scikit Learn)

Created by Rakesh Gopalakrishnan - Over 260,000 Students

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Students: 120642, Price: Free

Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts. 

Introduction to R

Learn the core fundamentals of the R language for interactive use as well as programming

Created by Jagannath Rajagopal - Entrepreneur and Data Scientist

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Students: 34525, Price: Free

UPDATE: As of Nov 22, 2018, this course is now free! Many thanks to all my existing students who made it possible for the wider audience to benefit from the course material :-)

With "Introduction to R", you will gain a solid grounding of the fundamentals of the R language! 

This course has about 90 videos and 140+ exercise questions, over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session. 

From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations, Importing into R, and Loops and Conditions

Next, you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations. 

After that, you will learn the use of R in Statistics, where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees. 

Following that, the next topic will be Graphics, where you will learn to create 2-dimensional Univariate and Multi-variate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on. 

At that point, the course finishes off with two topics: Exporting out of R, and Creating Functions

Each chapter is designed to teach you several concepts, and these have been grouped into sub-sections. A sub-section usually has the following: 

  • A Concept Video

  • An Exercise Sheet

  • An Exercise Video (with answers)

 
 
 

Why take a course to learn R? 

When I look to advancing my R knowledge today, I still face the same sort of situation as when I originally started to use R. Back when I was learning R, my approach was learn by doing. There was a lot of free material out there (and I refer to that early in the course) that gave me a framework, but the wording was highly technical in nature. Even with the R help and the free material, it took me up to a couple of months of experimentation to gain a certain level of proficiency. What I would have liked at that time was a way to learn the fundamentals quicker. I have designed this course with exactly that in mind. 

Why my course? 

For those of you that are new to R, this course will cover enough breadth/depth in R to give you a solid grounding. I use simple language to explain the concepts. Also, I give you 140+ exercise questions many of which are based on real world data for practice to get you up and running quickly, all in a single package. This course is designed to get you functional with R in little over a week

For those beginners with some experience that have learnt R through experimentation, this course is designed to complement what you know, and round out your understanding of the same. 

Essentials of Data Science

Discover what Data Science is all about

Created by Maximilian Schallwig - Data Scientist

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Students: 25288, Price: Free

Data Science is growing ever faster as Big Data become an increasingly important part of our lives.
Because data is universal, the applications of Data Science are pretty much endless, all you need is access to the data of the system that you want to study.

Since it's such a new field, there are a lot of questions about what is Data Science, what do Data Scientists do, and what do you need to succeed as a Data Scientist? 

This course is designed to give you an overview of the three essential areas of Data Science, the areas that every good data scientist should know, and being proficient in these areas can be the key to your success. After this course you will have a clear understanding of what Data Science is all about, and can make a clear decision of if it's the right field for you. You will also know what areas are important in Data Science, and hence can make informed decisions on what areas to focus on learning.

When you really get into Data Science there are other areas that start coming in too, but all of these can be traced back to one, or multiple, of these three basic foundations.

What is Data Science ?

Fundamental Concepts for Beginners

Created by Gopinath Ramakrishnan - Data Science & Machine Learning Enthusiast, Agile Coach

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Students: 19599, Price: Free

If you have absolutely no idea what Data Science is and are looking for a very quick non-technical introduction to Data Science , this course will help you get started on fundamental concepts underlying Data Science.

If you are an experienced Data Science professional, attending this course will give you some idea of how to explain your profession to an absolute lay person.

There are lots of very good  technical and programming focused courses available on Data  Science in Udemy and elsewhere.

This short  course will lay a firm foundation for better understanding and appreciation of what is being taught in advanced Data Science courses. 

Bootcamp for KNIME Analytics Platform

For users new to KNIME and data science, or experienced users of other data science tools.

Created by KNIME Inc - Data Science and Evangelism

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Students: 19142, Price: Free

If you've never used KNIME Analytics Platform before, this is the course for you. You can use KNIME Analytics Platform to create visual workflows with an intuitive, drag and drop style graphical interface, without the need for coding.

We'll start with installation and setup of the software, and present detailed materials on its features. We'll move on to some practical application of data blending from different sources, and use real datasets to show you all the different way you can transform, clean, and aggregate information. Finally, we'll introduce some machine learning algorithms for classification, and show you how to build your own models.

More than 50 videos are provided, along with some exercises for you to work on independently. By the end of the course, we want you to feel comfortable with the interface of KNIME Analytics Platform, be able to perform common processing tasks with your own data, and start putting predictive analytics into practice.

Data Science with Analogies, Algorithms and Solved Problems

Machine learning, Data Mining, Data Science, Deep Learning, Data analysis, Data analytics, Python, Visualization

Created by Ajay Dhruv - Assistant Professor at VIT Mumbai, India

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Students: 9523, Price: Free

Interested to know about the field of Machine Learning?

Then this course is for you! This course has been designed such that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into machine learning.

Here is a list of few of the topics we will be learning:

• Difference between Data Mining and Deep Learning

• Data and 5 Vs of Big Data

• Types of Attributes

• Outliers

• Supervised learning, Unsupervised learning, Reinforcement learning

• Python Libraries

• CNN, RNN, LSTM

• K - means Clustering Algorithm

• Bayesian Algorithm, ID3 Algorithm

• Simple Linear Regression

• Anaconda

• Visualization