Why is the Machine Learning trend emerging so fast?
Machine Learning solves Real-World problems. Unlike the hard coding rule to solve the problem, machine learning algorithms learn from the data.
The learnings can later be used to predict the feature. It is paying off for early adopters.
A full 82% of enterprises adopting machine learning and Artificial Intelligence (AI) have gained a significant financial advantage from their investments.
According to Deloitte, companies have an impressive median ROI of 17%.
Machine Learning Interview Questions For Freshers
1. Why was Machine Learning Introduced?
The simplest answer is to make our lives easier. In the early days of “intelligent” applications, many systems used hardcoded rules of “if” and “else” decisions to process data or adjust the user input. Think of a spam filter whose job is to move the appropriate incoming email messages to a spam folder.
But with the machine learning algorithms, we are given ample information for the data to learn and identify the patterns from the data.
Unlike the normal problems we don’t need to write the new rules for each problem in machine learning, we just need to use the same workflow but with a different dataset.
Let’s talk about Alen Turing, in his 1950 paper, “Computing Machinery and Intelligence”, Alen asked, “Can machines think?”
Full paper here
The paper describes the “Imitation Game”, which includes three participants –
Human acting as a judge,
Another human, and
A computer is an attempt to convince the judge that it is human.
The judge asks the other two participants to talk. While they respond the judge needs to decide which response came from the computer. If the judge could not tell the difference the computer won the game.
The test continues today as an annual competition in artificial intelligence. The aim is simple enough: convince the judge that they are chatting to a human instead of a computer chatbot program.
2. What are Different Types of Machine Learning algorithms?
There are various types of machine learning algorithms. Here is the list of them in a broad category based on:
Whether they are trained with human supervision (Supervised, unsupervised, reinforcement learning)
The criteria in the below diagram are not exclusive, we can combine them any way we like.
3. What is Supervised Learning?
Supervised learning is a machine learning algorithm of inferring a function from labeled training data. The training data consists of a set of training examples.
Knowing the height and weight identifying the gender of the person. Below are the popular supervised learning algorithms.
Support Vector Machines
K-nearest Neighbour Algorithm and Neural Networks.
If you build a T-shirt classifier, the labels will be “this is an S, this is an M and this is L”, based on showing the classifier examples of S, M, and L.
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