Everything That You Know About Machine Learning

A newly emerged branch of artificial intelligence or AI, machine learning Is an invention of computer science that is solely focus upon the use of data along with its algorithms to copy the way that humans learn new things, gradually improving Itself with time. The term “machine learning” was coin in 1962 By Arthur Samuel. Over the next span of decades, there have been major technological developments in the field of machine learning, making major developments possible such as our beloved Netflix’s recommendations engine or self-driving Cars.

This has been made possible through the help of statistical methods and algorithms which come together to uncover key insights within data sets, In order to come up with classifications and predictions. As the fields of big data and machine learning continue to expand with every passing day, the need for machine learning technicians and scientists is bound to increase. If you are interest in the field of machine learning and would like to establish a career in it, gear yourself up, as in this article, we will share with you everything that you need to know about machine!

1.   How Does Machine Learning Actually Work?

What is machine learning? Machine learning is a development of computer science that allows computer systems to learn and adapt to human language, and thus follow instructions seamlessly without the need of human interference. Machine works in 3 steps –

  • A Decision Process –  The sole work of a machine algorithm is to come up with a classification or a prediction. So if you start by inputting some data, whether it is labile or unlabelled, the algorithm automatically comes up with an estimate that is based on the pattern of the data.
  • Error function  – The role of the error function is to evaluate the Algorithm model’s prediction. With the help of some already known examples, the error function makes comparisons to assess how accurate the prediction is.
  • Process of Model Optimisation – If the model feels that the data points fit better to a different training set, The algorithm will adjust the weight to decrease the amount of discrepancy between the model estimate and the known examples. The algorithm will continue to repeat this optimization process until the weights have reached a level of accuracy.

2.  What Are The Use Cases Of Machine Learning?

  • Speech recognition- Also known as ASR or automatic speech recognition, This process utilizes natural language processing to process natural human language into a written format. Eg. Siri or Amazon’s Alexa.
  • Recommendation engines- Through the help of past consumer behavior of the user, The algorithm comes up with data Trends that can further be utilize to create more effective selling strategies.  Example- Netflix’s recommendation engine or Amazon’s “users also liked” panel.
  • Online chatbots- Chatbots are quickly replacing human agents for improved customer service. They are useful to answer frequently asked questions surrounding topics such as shipping policies, For suggesting sizes to the user, or cross-selling products.

We hope that this article helps you out!