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What Do We Mean by Machine Learning?

The goal of artificial intelligence is to create a machine that can mimic a human mind and to do that, of course, it needs learning capabilities. However, it is more than just about learning. It’s about reasoning, knowledge representation, and even things like abstract thinking. 

Machine learning, on the other hand, is solely focused on writing software that can learn from past experiences. One thing you might find astounding is that machine learning is more closely related to data mining and statistics than it is to AI. Why is that? First, we need to know what we mean by machine learning.

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Artificial Intelligence in Service Desks

“Flawless customer service facilitates opportunity more than anything else; the opportunity to exceed any and all expectations.” ~ Than Merrill, CEO & Founder, FortuneBuilders

Resolving customer issues at the earliest is as critical as delivering a new product or service to customers. While organizations strive to achieve better customer service by optimizing key metrics such as Mean Time To Resolution (MTTR), Defect Removal Efficiency (DRE), etc., Artificial Intelligence comes in handy in catering to our needs to be faster and accurate in providing resolution.

Knowledge Base

Knowledge Base

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6 Metrics You Need to Optimize for Performance in Machine Learning

6 Metrics to Optimize Performance in Machine Learning

There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved.

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Examples of metrics in Machine Learning

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A Comprehensive Guide to Random Forest in R

Random Forest in R

Random Forest in R

With the demand for more complex computations, we cannot rely on simplistic algorithms. Instead, we must utilize algorithms with higher computational capabilities and one such algorithm is the random forest. In this blog post on random forest In R, you’ll learn the fundamentals of random forest along with its implementation by using the R Language.

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What Is Classification?

Classification is the method of predicting the class of a given input data point. Classification problems are common in machine learning and they fall under the Supervised learning method.

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Anomaly Detection Using the Bag-of-Words Model

I am going to show in detail one use case of unsupervised learning: behavioral-based anomaly detection. Imagine you are collecting daily activity from people. In this example, there are six people (S1-S6). When all the data are sorted and pre-processed, the result may look like this list:

  • S1 = eat, read book, ride bicycle, eat, play computer games, write homework, read book, eat, brush teeth, sleep
  • S2 = read book, eat, walk, eat, play tennis, go shopping, eat snack, write homework, eat, brush teeth, sleep
  • S3 = wake up, walk, eat, sleep, read book, eat, write homework, wash bicycle, eat, listen music, brush teeth, sleep
  • S4 = eat, ride bicycle, read book, eat, play piano, write homework, eat, exercise, sleep
  • S5 = wake up, eat, walk, read book, eat, write homework, watch television, eat, dance, brush teeth, sleep
  • S6 = eat, hang out, date girl, skating, use mother's CC, steal clothes, talk, cheating on taxes, fighting, sleep

S1 is the set of the daily activity of the first person, S2 of the second, and so on. If you look at this list, then you can pretty easily recognize that activity of S6 is somehow different from the others. That's because there are only six people. What if there were six thousand? Or six million? Unfortunately, there is no way you could recognize the anomalies. But machines can. Once a machine can solve a problem on a small scale, it can usually handle the large scale relatively easily. Therefore, the goal here is to build an unsupervised learning model that will identify S6 as an anomaly.