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Machine Learning Interview Questions

If you are preparing towards successfully clearing your Machine Learning interview process then we are here to help with it. In this blog post, we have collected the list of most frequently asked interview questions on Machine Learning. This course will be giving you a clear idea about the most frequently asked questions in Machine Learning interviews & how to confidently answer these questions.

  • What is Machine learning?

Machine Learning can be interpreted as an advanced branch in computer science & the subset of AI. It delivers machines the ability to learn & improve from their past experiences without explicitly being programmed. With the application of Machine Learning, machines will get the ability to carry out tasks on their own without explicitly being programmed.

How Many Types Of Algorithm Techniques Exists In Machine Learning?

Different Machine Learning algorithms are

  1. a) Supervised Learning
  2. b) Unsupervised Learning
  3. c) Semi-supervised Learning
  4. d) Reinforcement Learning
  5. e) Transduction
  6. f) Learning to Learn
  • Mention The Difference Between Data Mining & Machine Learning?

The concept of Machine Learning relates with the study, design and development of the algorithms that present machines with the ability to perform tasks on their own without explicitly being programmed. The concept of Data Mining can be defined as the process of extracting insights or finding the patterns from the unstructured data.  Data Mining makes use of Machine Learning models for accurately mining & analysis of the data.

What Are The Advantages & Disadvantages Of Neural Networks?

Advantages-

Neural networks have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Being much flexible Neural Networks are well capable to learn patterns that are almost impossible for the Machine Learning algorithms to learn.

Disadvantages

One of the major drawbacks of Neural Networks is that they require large amount of training data to converge. This makes it highly complex to pick the right architecture, and the internal hidden layers are incomprehensible.

  • What Is Classifier In Machine Learning?

The system that inputs a vector of discrete or continuous feature values and outputs a single discrete value in Machine Learning is nothing but a classifier.

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