Granted, data is everywhere and is being generated at an unprecedented rate. The world is constantly searching for and developing tools and techniques that will handle and process such kind of enormous data fast, effectively, and in almost real-time. The fields of data science, data mining, and machine learning exist to draw value and insight from data to help businesses make informed decisions.
It is the role of data scientists to come up with effective tools and techniques that will be used to make sense of the massive data that businesses hold. Beyond developing tools, data science professionals should possess the skills required to implement and operate such tools. Unlike before, today professionals can take data mining or machine learning online course to hone their skills and be more productive in the workplace.
What is Machine Learning?
In 1959, Arthur Samuel first coined the term machine learning, stating that, “it gives computers the ability to learn without explicitly being programmed”.
Machine learning (ML) is a branch of artificial intelligence that is focused on building algorithms that analyze data to discover hidden patterns for the purpose of predicting future outcomes. The computer is initially programmed with the algorithms and thereafter left to learn and improve from experience, without human intervention, from the data that is continuously fed into it. A good example of a machine learning algorithm is neural networks used for classifying information.
Businesses that have adopted machine learning have become more effective in identifying risks and opportunities in hidden patterns within data sets. For instance, by scanning through other users with the same profile, Netflix can predict which program or series you would want to watch next. Many enterprises have leveraged the power of ML’s cognitive technology with other AI applications to process massive volumes of data more effectively.
There are four machine learning techniques:
- Supervised machine learning. Supervised machine learning uses classified and labeled and labeled data to train algorithms (models) to predict future outcomes.
- Unsupervised machine learning. Unsupervised machine learning uses data that is neither classified nor labeled to train algorithms to predict future outcomes.
- Semi-supervised machine learning. Semi-supervised algorithms use both labeled and unlabeled data to train algorithms and so fall somewhere in between supervised and unsupervised learning.
- Reinforced machine learning. In reinforced machine learning, algorithms learn continuously using feedback from their actions while interacting with their environment.
What is Data Mining?
Data mining is a multidisciplinary field that employs various techniques to extract information from data that was previously not known or understood and using this information to make informed data-driven decisions. It attempts to identify and organize properties of data sets in terms of correlations, patterns, sequences e.t.c, and structure it in a way that is understandable. This way, a data set used in data mining must contain the relationships that are being investigated whose nature is not usually known and so as large a data set as possible should be used.
Data mining encompasses fields like artificial intelligence, pattern recognition, machine learning, data visualization, computational statistics, data room virtual base management, and others. Just in machine learning, data mining has been applied widely in various industries for instance the retail industry to extract insights that are used for making business decisions that drive growth and profitability.
How Machine Learning and Data Mining correlate
As we may already have known, both machine learning and data science have their roots in data science. These two fields intersect in many ways and have often been misconceived.
Machine learning is one of the several techniques employed to mine data. It is basically a data analysis technique in which models are trained to learn and improve from data automatically without being supervised to predict future outcomes on data.
While data mining follows almost the same process as machine learning, it does not focus entirely on algorithms. Data mining broadly defines a business problem and machine learning algorithms become one of the techniques employed to find the solution to the problem using data.
Use cases of Machine Learning and Data Mining
Some applications of machine learning and data mining include:
- Software engineering employs machine learning to predict the cost attached to software development and defects in the software.
- Offering customer support on websites through online chat support
- Creating customized marketing campaigns that target specific customers. Also, product placement and recommendation systems for instance that used in Amazon.
- Detecting spam emails
- Predicting fraudulent transactions in certain specific credit cards.
- Predicting patient attendance on a given day in hospitals and staffing the day accordingly.
Machine learning vs Data mining
While machine learning and data mining are interrelated in that they are both involved with discovering hidden trends and patterns from data to gain insight for decision making, they also have distinct differences.
| Machine learning | Data mining | |
| Goal | Uses trends and patterns discovered in historical data to predict future outcomes. | Applied to discover unknown but already existing trends, relationships, and patterns in various properties of the data set using visualization techniques. |
| Operations | After the defining algorithms and initial programming, a machine automatically learns algorithms over time as data is continuously fed into the system. With time the system learns and improves enough to make accurate predictions of outcomes | Human involvement is required to apply the techniques used to extract information from data. Without human intervention, the process of data mining cannot happen. |
| Method of operation | ML teaches the computer how to learn and understand the flow of data. | Data mining in itself is the technique used to understand the flow of information in data |
| Applications | Machine learning is widely used for discovering correlations and making recommendations out of them, for instance, buyer recommendations in e-commerce sites or fraud detection in the banking industry. ML algorithms self-improve over time. | Data mining is mainly used for research purposes to predict outcomes for instance, for cluster analysis. |
| Accuracy of results | Accuracy in machine learning increases with time as algorithms learn and improve over time. Often, machine learning delivers more accurate predictions compared to other techniques. | Accuracy in data mining greatly depends on how well data is collected, cleaned, and prepared. |
| Implementation | Implementing machine learning techniques requires algorithms, neural networks, and predictive models. | Implementing data mining techniques requires machine learning or other data analysis techniques alongside a database system and a data warehouse server for data management. |
| Foundation for learning | Trained on ‘training data set’ that teaches the computer to learn from data to predict outcomes. | Applied on existing databases with mostly unstructured data |
| Pattern recognition | Machine learning depends on algorithms to learn from and adapt to data. | Data mining depends largely on data itself to reveal patterns through classifications and sequences. Larger data sets produce more reliable insight. |
Conclusion
It is a given that businesses will continue generating even more data. With more data comes the need to extract the most value out of it as well as a higher demand for advanced techniques and professionals to do this. In the future, machine learning and data mining will find more common use cases where one technique will complement the other for even better data analytics outcomes. In data, there lies more valuable insight that machine learning and data mining can unearth, whether applied separately or together.