Data Mining Vs Machine Learning

The Growing Importance of Digitalization

For the past couple of years, digital transformation or digitalization has been quite the buzzword among businesses and organizations. However, digitization in the most real sense transcends a mere technological up-gradation and entails a reorganization of the entire company, its workforce, and its day-to-day operations. 

The process of digital transformation has been fuelled with advancements in Artificial Intelligence (AI) techniques like Machine Learning, and thus, demands for machine learning online courses have skyrocketed. A recent survey conducted by Infosys has shown that organizations that use AI-enabled activities to drive digital transformation have generated substantial profits. Machine learning, specifically, has had the maximum impact in spurring the digital revolution by making the business processes more efficient, saving time and effort, boosting productivity, improving customer experience, reducing costs, driving innovation, enriching the staff’s knowledge and skillset and simplifying daily organizational work.

Data Mining and Machine Learning have revolutionized the digital space and popularized the machine learning online course. Let’s see why.

What is Data Mining?

Data Mining is the process of investigation and analysis of large sets of data to find meaningful correlations, patterns, and anomalies for the prediction of future trends and outcomes. In the last ten years or so, advancements in speed and processing power have replaced manual, time-consuming, and tedious data analysis practices to move on to automated, easy, and quick methods of analyzing data. From bankers and manufacturers to insurers and telecom providers, data mining is used by everyone to understand the relationships among demographics, pricing, and promotions and how various economic and social factors affect business operations, revenue generation, and customer relationships. Data mining enables:

• Screening and filtering the noise and redundancy in the data
• Understanding which data is crucial and relevant for the assessment of likely future outcomes
• Accelerating the rate of making quick and well-informed business decisions

The process of data mining involves the following steps:

1. Understanding the business and its needs
2. Understanding the data collected
3. Preparation of relevant data to be mined
4. Modeling the data using mathematical tools and models
5. Evaluation of the findings of data analysis and their relevance across the business or organization
6. Deployment of the conclusions of the data mining procedure to daily operations

What is Machine Learning?

As business landscapes are undergoing a rapid digital transformation, machine learning online courses and certifications are gaining quite the hype. Machine learning is the method by which a computer can be taught to make precise predictions when fed with data. Say, a system trained on machine learning models has to determine whether a flower in a photo is a rose or a tulip.

The system would conveniently discriminate between the flowers based on the large number of images it has been trained on and labeled as containing a rose or a tulip. The difference between traditional computer software and machine learning is that in the former, a human developer writes the codes instructing the system to differentiate between the given objects, and in the latter, the system is trained to detect the differences based on data observations and instructions automatically.

Classification of machine learning algorithms: 

• Supervised learning: In supervised learning, the algorithm is exposed to data consisting of labeled examples so that it can reliably single out specific data types based on any given input. 
• Unsupervised learning: In this learning method, the algorithm learns to identify patterns in unlabelled data and sorts it based on similarities or anomalies.
• Semi-supervised learning: This technique utilizes labeled data for partial training of a machine learning model and subsequently uses the latter for labeling unlabelled data.
• Reinforcement learning: This learning technique is based on the system’s interaction with the environment and taking appropriate action to reward maximization in a particular situation.

Data Mining Vs Machine Learning

Very often, people get confused between the concepts of machine learning and data mining for apparent reasons:

• Both involve data analysis
• Pattern recognition is a characteristic of both the processes
• Both entail decision making based on data-driven insights
• Both data mining and machine  learning require vast amounts of accurate data

So, before you sign up for a machine learning online course, you must understand that even though data mining and machine learning are similar on many levels, the two concepts differ significantly from each other. Let us discuss some of the aspects that set apart data mining from machine learning:

• While data mining means the extraction of knowledge from an extensive repertoire of data, machine learning entails building algorithms based on data as well as previous experience.
• Data mining methods look for patterns already existing in the data. But, machine learning goes beyond the scope of past events to predict future likelihoods based on pre-existing data.
• At the start of data mining processes, the patterns and rules are mostly unknown. But with machine learning, the machine understands and learns the data based on the standards and variables given to it.
• Traditional databases consisting of unstructured data call for data mining operations. However, machine learning owes its origin from algorithms as well as existing data.
• Data mining relies on manual human interference and decision making. But once self-automated, machine learning models can run without human maneuver.
• Data mining techniques are used for building machine learning models. Whereas, machine learning algorithms are implemented in neural networks, decision trees, and other areas of AI.
• Data mining is most frequently used in research fields such as text mining, web mining, recognizing sales patterns, and fraud detection. Machine learning finds applications in cases such as making product recommendations and estimating delivery time. 
• Data collection and human efficiency influence the accuracy of data mining procedures. Machine learning algorithms are more accurate than data mining methods.

Conclusion

Be it data mining or machine learning, data remains the most crucial factor. While data mining looks for trends and patterns in the existing data to project future outcomes, machine learning enables the quick and automated processing of data to generate results in the form of models. As global trends shift towards increased digitalization, these technologies have the potential for immense growth.