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Essential Machine Learning Interview Questions and Concepts

 


Essential Machine Learning Interview Questions and Concepts

Machine Learning Interview Questions: Algorithms/Theory. Q2: What is the difference between supervised and unsupervised machine learning? Q3: How is KNN different from k-means clustering? Q4: Explain how a ROC curve works. Q5: Define precision and recall. Q10: What's the difference between Type I and Type II error?

Instructor : Dr Ashish Dikshit, PHD

An ex-Cisco, GE, HP& JP Morgan Chase.(Data scientist)

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What you'll learn

  • Concepts related to machine learning and algorithms, to prepare you the best for interviews

Requirements

  • Basic understanding of terms related to Data Sciences

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Description

This course will help you to answer some of the questions asked in Interviews related to Machine Learning. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

Evolution:

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.

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