We all have heard the terminology and a lot of us may even have a brief idea of what machine learning is. In this article, we will try to know the overview of Machine Learning which will help aspiring Data Scientists and enthusiasts to get a kickstart in machine learning.
Field of study that gives computers the ability to learn without being explicitly programmed.
In simple words, machine learning is the way to teach computers to make their own decisions without having to program for each case. There are different machine learning techniques and algorithms that help achieve this. We will talk about some of them in the later part of this article. Let us say you have a dataset of the stock market performance of a specific company, if you have to write conditions or cases to predict the stock price based on available parameters then it would not be possible. This is where machine learning is effective. You can feed the data to machine learning algorithms and make predictions for every real-world case similar to your dataset. That is the power of machine learning.
Why is machine learning taking the center stage?
Machine learning helps businesses simplify, save time, and money and at the same time be effective in their solutions internal or external to their organization. Let me present you with some facts and information points that will help you understand the importance of machine learning in the business setup.
- Netflix uses machine learning to recommend shows and movies to its customers based on their past choices, activity on the website/app, and various other factors. It has helped them in increasing the viewership, and time spent by an individual on their platform and ultimately increasing their revenue through increased subscriptions.
- Amazon uses machine learning in helping its delivery partners optimize the route taken for each delivery which has helped the company in ensuring more deliveries per day.
- Google is using machine learning extensively in its search results, translation and image search products. It has improved their search performance tremendously in the last few years.
These are just a few examples of the top companies. Many other companies have been adopting machine learning extensively to boost their effectiveness both internally and externally. The companies have come to realize that use of machine learning in the right direction can save them time and cost immensely. This adoption is not easy, it has its own challenges, we will learn about those challenges in the next section.
Challenges in machine learning adoption
Let us look at the challenges in adoption of machine learning by businesses.
Software challenges
There are multiple software challenges before adoption of machine learning algorithms. From data collection and structuring to security the companies face an uphill task in organizing their data in the machine readable format. All the machine learning algorithms need data to be organized. Other problems related to data are lack of data and poor quality data.
Hardware challenges
Machine learning algorithms require large amounts of computation power to run. A lot of these algorithms may not run on CPUs and need GPUs for faster turn around. GPUs are expensive and hard to maintain. High initial cost makes it a huge challenge for the organizations to start with their machine learning adoption. The increasing costs of these hardware resources is also posing a challenge to all the organizations.
Black box
Algorithms have to be interpretable and explainable. While a lot of machine learning algorithms are explainable, there are an equal number of algorithms that are black boxes and hard to interpret the results. The algorithms are designed keeping in mind the accuracy and how to boost it. Interpretability takes the next preference as a result of which most algorithms today produce great accuracy results but are not interpretable. For businesses and organizations to adopt machine learning at a higher pace the algorithms have to be both accurate and interpretable.
Talent
Machine learning is gaining momentum in recent times as a result of which it is difficult to find the people with relevant skills in the domain. Finding the best talent in machine learning has become one of the demanding tasks. It takes up a long time to put together the best team of people who can work on machine learning tasks. The process of finding the best talent and putting together a team in achieving the end goal consumes the larger part of time. It also increases the time taken on the ROI for the businesses in a big way.
Conclusion
Machine learning as a new technology opens up a wide range of opportunities for businesses and people alike. It has also proven to be the future of how data can be used to solve complex problems that will have an impact. Besing a new field it comes with its own set of problems in adoption for businesses. As we go further the new research will help in addressing the challenges in machine learning. I hope this article will help in getting an overview on machine learning to those who are new to the field. For more such articles on technology you can always refer to algoexplain. Happy reading!