A Comparison Guide: Big Data Vs Machine Learning

Big data and machine learning are the two most common terms that we hear nowadays. These technologies are allowing organizations to make better decisions and improve (Read: automate) several functions of the business. However, both the technological advancements are misunderstood and often utilized interchangeably. To reduce the confusion of individuals who are willing to pursue a machine learning course or big data certification course, we have created a comparison guide.

Read more to understand the importance of both the technologies and acknowledge differences.

There are a few key variations in machine learning course and big data certification course, which an individual need to understand before undertaking a related task. The following sections will discuss these key variations.

Understanding Big Data

Big data includes large amounts of data which is created by organizations and entities on a daily basis. Here, we are talking about extraordinarily large data volumes, velocity, and variety.

This data after collecting, handling, and processing become a part of big data, and big data analytics is used to analyze unstructured and structured data.

The main aim behind completing this processing is to find patterns and trends in the data which can help organizations make consumer-related effective decisions.

Hence, this is how we can summarize it in simple words:

o   First, we collect, manage, and process a huge amount of data according to targeting population, trends, etc.

o   Then, this data is analyzed to find out patterns that are used by stakeholders and decision-makers.

Understanding Machine Learning

If big data analyze a huge amount of data, machine learning finds one way to process it. For example, the recommendation tab on Amazon or user recommendation on Netflix.

This technology uses an algorithm to learn certain tasks and ways to do it better. Improving accuracy in the Netflix example. Using this analysis, Netflix can justify the production of one type of genre more.

But, how does machine learning train an algorithm to be better and learn?

It does this with data. An algorithm is prepared and large amounts of quality data are fed to it to extract information and insights. However, it is necessary to note that the bigger your dataset is, the better will be your results. The quality and size of the data impact the accuracy of your machine learning algorithm. You will learn this in your machine learning course.

Comparison Between Machine Learning and Big Data

Now, let’s compare the machine learning course and big data certification course

1.     Data Use

Since big data contains ingestion, processing, and analysis of data, it is used in various fields including sales, financial, and research data collection.

Machine learning allows the algorithm to learn and act in a certain way based on data offered. So, it is used in inventions like self-driving cars, recommendation engines, etc.

2.     Foundation

Big data extract data which is already there and processes it to find if there are any patterns in the data. This helps in making decisions that are important for the organization. For example, analyzing a huge set of data to know whether users prefer buying a certain product online or offline.

However, machine learning uses this existing information to teach a system. This algorithm or system learns from the existing data and then acts accordingly in the future.

3.     Pattern Recognition

Big data uses methods such as sequence analysis and classification to find out patterns and insights in the system.

Machine learning also uses the same methods but it teaches the algorithm to use these methods and learn, which offers results as per user expectations. These steps are automated in machine learning, as the algorithm does the work.

4.     Purpose

The purpose of big data is clear. Its analysis data and handles a large amount of data which is hard for human interpreters to interpret in a small time. So, big data analytics are used to process and extract information out of this data.

For machine learning, the purpose is to make an algorithm work in a certain manner and improve over time. The aim is to learn and become better at a function without human supervision.

5.     Data Volume

In big data, data is more and datasets are large. Using this, information is extracted.

But, machine learning requires more concise, quality data for exact functioning and behavior. Hence, the data utilized in machine learning is often less than the size of the data used in big data.

Conclusion

When completing a machine learning certification or big data certification course, it is necessary to understand that both machine learning and big data are intertwined. If machine learning offers us an impeccable algorithm that reduces our work, big data offers quality and concise data to this algorithm.

Keeping this in mind, move forward with your education and co-relate both the technologies whenever necessary.