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5 Tips To Get Started With Machine Learning

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Machine Learning, as a subset of Artificial Intelligence, has made significant strides over the past few years. It has affected the way people interact with business, and the way businesses interact with customers. Algorithms in machine learning present to us the shows we watch, the news we read, the music we listen to, and the ads we see. They can even drive cars! Well, some cars.

Despite these advancements, however, there are still obstacles in implementing Machine Learning (ML) in companies. Unfamiliarity is one—many still don’t know how to begin adopting it. Some companies might not fully appreciate what ML can do for them, but that’s easily remedied; what’s more challenging is the process of integrating it into the business.

Innovations may take some time to be universally adopted, but technologies like machine learning are here to stay. Machine learning and artificial intelligence will change society the way electricity did in the 19th century. And the sooner companies adopt machine learning, the better their position will be when further advancements come along. CNVRG can help fast-track a company’s ML adoption.

Below are a few tips to get you started with machine learning.

 Start With Little Steps

Many companies often don’t pay attention to which customers are most likely to cancel their services. They instead focus on getting new customers, not knowing that they possess the data to predict which customers will leave. Keep in mind that the cost of retaining customers is lower than signing up new ones.

Predicting the churn rate, or the number of customers who’ll stop using a company’s products or services, is a good way to start. It’s a small step, but a significant one—a high churn rate indicates dissatisfaction with a product or service. With this data, you can focus on how to keep your customers.

Use Supervised ML First

machine-learning

‘Supervised’ here doesn’t mean a human component is involved; supervised machine learning refers to a component of ML (the other is unsupervised ML), which is a predictive algorithm. Supervised Machine Learning can help you do the following:

  • Detect fraud
  • Predict demand
  • Predict churn rate
  • Predict cancellations
  • Prevent payment defaults

Supervised ML is simpler, can answer particular questions, and can effectively evaluate the quality of algorithms before they’re deployed in an operations environment. Using supervised ML is an excellent beginning for machine learning’s integration into your company.

Put Off Working With Big Data

Big Data can be pricey. Besides, a lot of companies don’t have the right size infrastructure for storing information that size. It would take hours to process, and in any case, with machine learning, it isn’t necessary to have that kind of amount of data. A typical company today has enough information to produce high-value predictive algorithms. Quality always trumps quantity.

Employ Machine Learning In The Cloud

The programming languages used in Machine Learning, which are typically Phyton and R, would require highly-specialized professionals that aren’t familiar with the customers’ specific needs. Besides, the algorithms written in these languages can be too complex to put into production, with codes harder to reuse.

Using a cloud-based ML platform offered by data science companies would be simpler. But perhaps most important is that using ML platforms in the cloud would save your company a pretty penny.

Cloud platforms with systems based on Applications Programming Interface (API) simplify the reuse of algorithms. Even giants like Facebook, Amazon, and others use Machine Learning systems in the cloud, although as separate infrastructure inside their company.

Start Now

It’s because your competitors may now be using machine learning. The advantages you’ll get, whatever industry you might be, are undeniable. ML has been proven to be very useful in banking, retail, tourism, and other industries. Many programmers feel that the business world has barely even scratched the surface when it comes to the advantages that ML could give.

The best time to get on the ML bandwagon is now. If you still haven’t, you’ll know soon if your competitors got there before you.

Conclusion

Modern businesses, if they want to keep up with the market, should start integrating machine learning into their workflow. However, a company need not build its own machine learning platform as that could be prohibitively expensive, not to mention the complications it could bring to the company’s operations. An industry can always use the services of data science companies that can provide a cloud-based ML platform.

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