What is Machine Learning?

Machine learning is a form of artificial intelligence that enables software applications to accurately predict certain outcomes without having to be directly programmed to do so. Historical data is input into machine learning algorithms to predict new output values. As data is fed into algorithms, they are able to construct their own logic, and ultimately create solutions.

The field of machine learning has immense value, in that it provides companies or organizations with a snapshot of customer behavior trends as well as operational patterns. It is currently used by many small and large companies, such as Uber, Google, and Facebook, and provides them with a substantial advantage over competitors. For example, Netflix saved an estimated $1 billion by using its machine learning algorithm to study the effect of content recommendations and personalization.

Machine learning has a wide range of applications across fields. This powerful form of AI is used in fraud detection, price prediction, and medical diagnostics. It is also helpful in government agencies that focus on public safety and utilities. The retail world uses machine learning to analyze customers’ buying history and even personalize shopping experiences. In addition, the transportation sector relies on it to increase the efficiency of routes and to forecast potential problems.

How Machine Learning is Used in Data Analytics

There are a variety of techniques for incorporating machine learning into the process of data analysis:

  • Clustering: The commonalities between data are determined to provide insights into such topics as how customers are similar to one another. This allows customers to then be grouped in manners that might not be overtly apparent.
  • Natural language: Phrases such as “sales'' are mapped by the machine to their coding language counterparts. This enables those who may not have a background with Python or R to perform deep analysis simply by asking straightforward questions that can then be translated by the machine.
  • Elasticity: This process involves a machine pinpointing the cause for a given result. This is especially useful in situations where factors are in constant flux, as it can isolate which factor led to a specific outcome.

These machine learning analytic techniques isolate the underlying driving factors beneath data and indicate the best opportunities for growth.

Benefits & Challenges of Using Machine Learning in Data Analytics

New uses for machine learning are constantly being developed. The benefits this technology offers to businesses and companies are part of the reason machine learning is being increasingly used at both small and large organizations.

Here are a few of the benefits of using machine learning in data analytics:

  • It can automate the complete data analysis workflow to offer deeper, faster, and more robust insights.
  • It tests hypotheses in order to answer important business questions and can test them in seconds rather than weeks.
  • The story that machine learning tells about the data it has analyzed is accurate, timely, and relevant.
  • As the amount of data at hand grows, machine learning algorithms continue to improve on their efficiency and accuracy, and evolve to make better decisions.
  • Customer acquisition can be increased at an organization by using machine learning algorithms. They help organizations personalize data, predict which product is most relevant to offer, and decide which messaging app or channel is most suited to capturing the attention of new customers.

Yet as the amount of data that must be analyzed increases, challenges also arise with regard to harnessing its power:

  • Businesses who wish to use machine learning to process raw data must invest in essentials, such as data cleaning, structuring, and maintenance to guarantee that there is proper support for the pipelines.
  • Machine learning demands time and resources.
  • In some instances, those who make important decisions about data don’t work directly with the data itself. Those who hold business-driven roles, like CMOs and brand managers, don’t always have the necessary training or resources to glean insights from data unless they have the help of user-friendly tools or Data Scientists and Data Analysts.
  • It’s possible to get partial results if a machine system model is biased or has been tampered with. If the accuracy of training information is compromised, biased or even false results can be generated, which will negatively impact the decision-making process.
  • More companies and businesses are realizing the tremendous value of data. As they invest in syndicated data sources, how can they gain an advantage against competitors who have access to the same data?

Despite the drawbacks of incorporating machine learning into the data analytics process, most organizations cite the many benefits this technology provides as a reason to continue using it. This is why over three-quarters of enterprises prioritized ML and AI initiatives in their 2021 IT budgets.

Hands-On Data Analytics Classes

Do you want to learn more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.

In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500. Students can study from the comfort of their own home or office space and still receive industry-relevant data analytics training.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.

For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500. This tool can also help prospective students search for the best machine learning courses nearby.