How is machine learning related to big data?

Machine learning and big data are two terms that get used together a lot. This post will show you what big data is and how it is related to machine learning.

So, how is big data related to machine learning? Big data refers to the very large amount of data that organizations are acquiring on a day to day basis. Machine learning can make use of this data to make predictions and to divide the data into related categories.

There are actually a number of different ways that big data gets used in machine learning and there are a number of challenges being created by machine learning and big data.

What big data is

Big data refers to the very large amount of data that is being gathered by organizations. The amount of data is so large, in fact, that traditional storage methods are not sufficient to store the data and it is necessary for large organizations to build data centers to store the data in a centralized location.

Examples of big data:

  • The viewing history of Youtube users
  • The purchase history of people on Amazon
  • The characteristics and behavior of people on Facebook
  • The history of patients at hospitals

The data collected by companies can be used to put groups of people or things, that share similar characteristics, into categories. The companies will then make use of these categories by doing things such as advertising to these groups or allowing advertisers to market to certain groups of people. Examples of ways that people or things can be put into categories are by age, location, political views, weight or gender.

You can watch the video below to see a good overview of what big data is and how it is used.

What machine learning is

Machine learning is where statistical algorithms are used to make predictions by learning from data. The algorithms have the ability to modify the predictions that they make when new data is fed into them and they make predictions without being explicitly told how to do so.

Machine learning has gained a lot of popularity, in recent years, since computers have only recently become powerful enough to make use of the more powerful algorithms such as neural networks.

There are three different types of machine learning which are supervised learning, unsupervised learning and reinforcement learning.

Supervised learning algorithms work by predicting an output value based on a number of input values. For example, a supervised learning algorithm might try to predict the price of a house based on its location, size, number of rooms and how close it is to local schools. The data that these algorithms are trained on will include the output values.

Unsupervised learning algorithms are trained using data that does not come with the corresponding output values. The role of these algorithms is to learn more about the data and to group the data into related groups. An example of how an unsupervised learning algorithm might be used would be to segment a set of bird pictures based on their species. Another example would be to use one to detect anomalies such as to find fraudulent bank transactions by detecting behavior that is very rare.

Reinforcement learning is where the algorithm interacts with an environment and modifies its behavior based on a reward. The goal of a reinforcement learning algorithm might be to maximize the score of a game by playing the game many times. When it gets a high score it will remember what it did and do it more in the future. When it gets a low score it will avoid doing it again in the future.

How machine learning and big data are related

Big data can also refer to the analysis and use of the data that companies have available to them. This is where machine learning comes in.

Machine learning is related to big data because machine learning models are what are used to make predictions with the data.

It has become possible to use machine learning with big data, in recent years, since our ability to store the data has improved. Additionally, computers have become powerful enough to run machine learning algorithms, which can be very computationally expensive, on the data.

Examples of how machine learning makes use of big data:

  • Amazon recommends products that it thinks you would be likely to buy based on your browsing and purchasing history.
  • Netflix recommends shows that it thinks that you would be likely to watch based on your watch history
  • Youtube recommends videos that you would be likely to watch based on your watch history and the history of people that watch similar videos to you
  • Political campaigns advertising to you on Facebook based on your political views which are determined based on things such as the posts and pages you have liked
  • Recommending certain treatments based on past medical results

The data that is available to us has value in a number of different ways since it can tell you a lot about the origin of the data.

Machine learning can also make use of big data by sorting the data into related clusters. For example, Facebook users could be clustered into related groups which could then be made use of by advertisers.

Problems with big data

There are a number of challenges that are being caused by the emergence of big data.

There are some ethical issues that it could cause. For example, big data could be used to change people’s views on things by advertising to certain groups of people that are likely to change their minds when being shown a certain piece of advertising.

If there is a security flaw then the data could be stolen. This could cause people’s data to fall into the wrong hands which could then be used to do things such as change their political views or to make purchases using their bank details.

The algorithms might be predicting things based on the wrong predictors. As an example, if a machine learning algorithm is being used to predict a country’s life satisfaction score and all the countries with the letter s in their name had a similar score then the algorithm might predict a new country with an s in its name to have a similar score.

Is it better to get a job in machine learning or big data?

If you are interested in entering either the field of big data or machine learning then the good news for you is that they are both very in-demand professions and they both pay well.

The best option for you will depend largely on where your interests are and your current skills.

Big data jobs include Data analysts, data engineers, data scientists, or database managers. Only data scientists work with machine learning directly.

If you want a job that is more related to the processing of big data then it will be necessary for you to have very strong programming skills, a computer science bachelors or masters will often be required (or a very related degree) and you’ll need to be good with database software.

Machine learning jobs are those such as data science, machine learning researcher scientists and machine learning engineers.

If you want a job in machine learning then it will be necessary for you to have good programming skills, knowledge of statistics, calculus, linear algebra, the machine learning algorithms, SQL, and data analysis skills.

Machine learning jobs will also often require at least a masters degree. Sometimes, a bachelors degree will be enough if it is in a quantitative field and you can show a lot of relevant experience.

If you would like to learn more about how to implement machine learning algorithms, consider taking a look at DataCamp which teaches you data science and how to implement machine learning algorithms.

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