Our rapidly growing digital world has popularized so many new terms and phrases that it’s easy to get overwhelmed or lose track. The onslaught of technobabble is overwhelming. And people are liable to use strange new words interchangeably, unaware that the words mean two different things.
Specifically, that’s the issue facing “data mining” and “machine learning.” The line between the two terms sometimes gets blurred due to some shared characteristics. To bring things into sharper focus, we’re about to explore the notable distinctions between data mining and machine learning, and how it can benefit you.
Enhance your skill set and give a boost to your career with the Post Graduate Program in AI and Machine Learning.
Data mining is considered the process of extracting useful information from a vast amount of data. It’s used to discover new, accurate, and useful patterns in the data, looking for meaning and relevant information for the organization or individual who needs it. It’s a tool used by humans.
On the other hand, machine learning is the process of discovering algorithms that have improved courtesy of experience derived from data. It’s the design, study, and development of algorithms that permit machines to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element (but not eliminating humans themselves; that would be wrong).
Both data mining and machine learning fall under the aegis of Data Science, which makes sense since they both use data. Both processes are used for solving complex problems, so consequently, many people (erroneously) use the two terms interchangeably. This isn’t so surprising, considering that machine learning is sometimes used as a means of conducting useful data mining. While data gathered from data mining can be used to teach machines, so the lines between the two concepts become a bit blurred.
Furthermore, both processes employ the same critical algorithms for discovering data patterns. Although their desired results ultimately differ, something which will become clear as you read on.
So we see that their similarities are few, but it’s still natural to confuse the two terms because of the overlap of data. On the other hand, there’s a considerable number of differences between the two. So for the sake of clarity and organization, we are going to give each one its bullet item.
Let’s dig in to find out some of the differences between data mining and machine learning:
Meanwhile, companies use machine learning for purposes like self-driving cars, credit card fraud detection, online customer service, e-mail spam interception, business intelligence (e.g., managing transactions, gathering sales results, business initiative selection), and personalized marketing. Companies that rely on machine learning include heavy hitters as Yelp, Twitter, Facebook, Pinterest, Salesforce, and a little search engine you may have possibly heard of: Google.
Accelerate your career with the Post Graduate Program in AI and Machine Learning with Purdue University collaborated with IBM.
Every day, a little more of our world turns to digital solutions to handle tasks and solve problems. It’s a big enough digital world out there’s more than sufficient room for both data mining and machine learning to thrive. The continued dominance of Big Data means that there will always be a need for data mining. And the continued drive and demand for smart machines will ensure that machine learning remains a very much in-demand skill.
Which offers the most potential, you may wonder? There is no clear cut answer, but we can make a decent, informed guess. The increased interest in artificial intelligence and smart devices and the continued rise in the use of mobile devices are good signs. Between the two processes, machine learning may offer the best opportunities.
That doesn’t mean that data mining is, by any means, a dead-end career. According to Forbes, the total accumulate data in our digital universe will grow from 2019’s total of 4.4 zettabytes to approximately 44 zettabytes or 44 trillion gigabytes of data. Yes, notice the missing decimal point between those two values!
If you’re looking for an excellent career choice, you can’t miss with a job in the field of machine learning. The demand (and salaries!) for machine learning engineers is on the rise. The average salary of a machine learning engineer is around $146K, with a growth rate last year of 344p percent!
If you want to become a part of this exciting, dynamic world, then Simplilearn has the tools to get you started on your way. The Machine Learning Course will make you an expert in machine learning. You will master machine learning concepts and techniques. The course includes supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms, all to prepare you for assuming the role of Machine Learning Engineer.
Even if you’re not planning on a career in machine learning, it’s an excellent course to take for those who want to upskill and increase their marketability. After all, areas of knowledge such as data mining techniques and machine learning applications will always be in demand. And knowing these disciplines can add to your versatility as a digital professional.
You can choose between self-paced learning, the online classroom Flexi-pass, or as a corporate training solution. You’ll get over 40 hours of instructor-led training, over two dozen hands-on exercises, four real-life industry projects with integrated labs, and 24x7 support with dedicated project mentoring sessions.
Once you’ve passed the criteria, you’ll earn your certification, which is your ticket to this fantastic field. Check it out now, and secure your future digital career!
You can also take-up the AI and Machine Learning courses in partnership with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning with the Tensor flow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.
The comprehensive Post Graduate Program provides you a joint Simplilearn-Purdue certificate, and also, you become entitled to membership at Purdue University Alumni on course completion. IBM is the leading player in AI and Data Science, helping professionals with relevant industry exposure in the field of AI and Data Science, providing a globally recognized certificate, complete access to IBM Watson for hands-on learning and practice. The game-changing PGP program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning and deep learning.
Name | Date | Place | |
---|---|---|---|
Machine Learning | 21 Feb -11 Mar 2021, Weekdays batch | Your City | View Details |
Machine Learning | 8 Mar -26 Mar 2021, Weekdays batch | New York City | View Details |
Machine Learning | 12 Mar -16 Apr 2021, Weekdays batch | San Francisco | View Details |
Shivam Arora is a Senior Product Manager at Simplilearn. Passionate about driving product growth, Shivam has managed key AI and IOT based products across different business functions. He has 6+ years of product experience with a Masters in Marketing and Business Analytics.
Machine Learning
*Lifetime access to high-quality, self-paced e-learning content.
Explore CategoryMachine Learning Career Guide: A complete playbook to becoming a Machine Learning Engineer
Machine Learning vs. Deep Learning: 5 Major Differences You Need to Know
Supervised and Unsupervised Learning in Machine Learning
Machine Learning Interview Guide
What Is Data Mining: Definition, Benefits, Applications, Top Techniques, and More
How to Become a Machine Learning Engineer?