Advance Technology

7 Common Mistakes in Machine Learning and How to Avoid Them

Data, modelling and process issues can hold your machine learning efforts back—but they don't have to.

Machine Learning Services in India gives organizations the potential to make more accurate data-driven decisions and to solve problems that have stumped traditional analytical approaches.

But most eager businesses jump straight to model building—overlooking the fundamentals—resulting in models that aren’t very helpful. From understanding the data to choosing the best Machine Learning Solutions in India for the problem, there are some common mistakes that businesses often tend to make.

1. Assuming Machine Learning Is Magic

‍One of the biggest mistakes in machine learning is thinking that it's magic. Although you might find some marketing materials claiming that you can use their product to solve any problem, this simply isn't true. You need talent in order to make it work for your business because it requires mathematics and computer science. You might have to hire machine learning services in India for your company.

1. Not Understanding the Data

Understanding the data is a fundamental step in any machine learning project. Without a good understanding of the data you’re working with, you risk making incorrect decisions on pre-processing techniques, feature engineering and selection, and model building. This can result in models that do not perform well and, consequently, not very helpful in solving the problem.

1. Lack of Feature Engineering

Feature engineering is the process of understanding and manipulating existing features and creating new representative features that better capture the underlying relationships between features in the data. But most businesses overlook this super important step.

1. Implementing Machine Learning Solutions in India too soon or without a strategy

Many data-driven organizations have spent years developing successful analytics platforms. Choosing when to incorporate newer, more complex modelling methods into an overall analytics strategy is a difficult task. The transition to machine learning techniques may not even be necessary until IT and business needs evolve. In regulated industries, interpretation, documentation and justification of complex machine learning models adds an additional burden.

1. Only Using One Model Over and Over Again

It might seem like a good idea to train one deep-learning model and then wash, rinse, and repeat. However, it’s actually counterintuitive! It is by training several iterations and variations of deep learning models that we gather statistically significant data that can actually be used in research.

1. Incorporating Only Negative Feedback

‍One common mistake is incorporating only negative feedback into the model to train it.

Keep in mind that you'll need positive feedback for your model as well so that it can understand what "correct" looks like. To do this, include both types of feedback when building your model or add a step after you've trained the model to normalize the data so that it's balanced.

1. Assuming Machine Learning Services in India Will Fix Your Business Problems

Machine learning solutions in India is a complex field and it takes time to get results. It's not something that you can apply instantly, or overnight. You have to have enough patience to let the solution grow from data, instead of trying to force the data into a specific solution.

 So now that the seven common machine learning mistakes are covered, hopefully, you're able to avoid them in your own business.