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Top 10 Problems of Machine Learning

top 10 problems of machine learning

Despite the effectiveness of Machine Learning, there are still problems of Machine Learning present till today.

Below are top 10 Problems of Machine Learning.

1. Churn Prediction

Churn prediction is one of the most popular use cases for people who want to leverage machine learning. It has a large business value and benefits attached to itself especially in industries like telecom and banking. Several challenges such as the skewed nature of the data set available and the ability to decide which models to use are going to be under a lot of debate.

2. Sentiment Analysis 

Many judgments are made these days based on the opinions of others. We are more inclined to purchase a product if it has gotten great feedback, and we are more likely to visit a hotel if it has obtained the highest internet rating. Sentiment analysis has its own set of issues, such as determining how detailed the sentiment may be, how subjective the sentiment is, and so on, and as a result, sentiment analysis will be a suitable area to start attacking machine learning.

3. Truth and Veracity

There’s a lot stated online these days, and it’s difficult to tell what’s real and what’s not. We now have bots capable of publishing information in the same way that humans do, and there are social components to the evaluations of various organizations on the internet. I feel machine learning will be leveraged as a big challenge to determine the veracity/truth of information online.

4. Recommendations

With so many options accessible online, picking a book, restaurant, or even a basic product is getting increasingly challenging. Because it takes a lot of effort to understand the user’s context and not simply the preferences of the crowd, making effective suggestions based on the user’s context will be a huge problem.

5. Online Advertisement

There is a lot of work and many start ups around the space of intelligent online advertisements, but to be able to push the right advertisement at the right time in the right way to the user needs a lot of understanding of the when to target a particular customer. Machine learning exhibits a great challenge in this space in my opinion for determining the user’s behavior online to push the correct advertisement instantly when the user really needs it.

6. News Aggregation

Plenty of news is being generated around us from various different places about a variety of topics. Yet we all have a constant thirst to consume all the news relevant to us as much as possible. How are we going to aggregate news according to the user’s preference? Does his taste vary with time? How do we learn this variation? All this is going to be a challenge for machine learning and it involves a great deal of making sense of news and articles.

7. Scalability

Data is constantly expanding in variety, velocity, and volumes. Can the traditional machine learning algorithms that were developed a decade back be applied to big data? I feel they will all undergo some kind of refurbishment to be able to operate on data at a large scale. Can SVMs train faster? Can it be made parallel? This is going to be a good problem to focus on the rise of big data.

There are millions of people around the world on various social networks and within enterprises. How can you discover people who share similar interests as yours and what parameters are you going to consider to measure this similarity? How do we measure similarity and can we quantify this? I feel this is a nice problem for machine learning where we will face the challenge of trying to find the needle in a haystack.

9. Intelligent Learning

For example, identifying behavior in a video sequence is still challenging, despite a lot of study in this area. One of the most difficult difficulties with current learning algorithms is to enable robots to see, hear, and recognize in the same way that the human brain does. This means a good problem would be to leverage machine learning algorithms to use different modes of learning to achieve a particular task, be it recognition or anything similar.

10. Machine Learning for Medicine

The most interesting machine learning problem now and the coming future. There are so many diseases that need our attention and a lot of human time is spent researching for their cure by analyzing a lot of symptoms. Yet, two patients with similar health problems receive different kinds of treatment and eventually different extents of cure. Can we use machine learning to understand how a patient is feeling at a particular moment and appropriately recommend the right treatment for him? I feel this will change how we are going to live and will help doctors discover a lot of new medical methodologies.

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