Machine Learning | Learn for Master - Part 4
  • 8 kills you should learn to be a data scientist

    8 types of data science jobs with a breakdown of the 8 skills you need to get the job

    Data Scientists get assigned different names in different organizations. Contrary to popular belief, data science is not entirely about numbers, though it is a lot about them. A statistician, an astrologer, a survey designer, a biostatistician all play a data scientist’s role at some point without being known as one.

    There are a number of programming languages and software applications that support data analysis functions and they require different levels of programming skills. The following section explores different types of data scientists and corresponding functions performed by them:

    7 Types of Data Scientist
    1) Data Scientist as Statistician

    This is data analysis in the traditional sense.

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  • Data Scientice and Machine Learning Interview Questions

    Here are some Data Science and Machine Learning related Interview Questions asked by big companies such as Facebook, Amazon, Microsoft, Yelp, Pinterest, Square, Google, Glassdoor and Groupon.  I also post an article that briefly describes the popular machine learning interview questions.

    1. Given a coin you don’t know it’s fair or unfair. Throw it 6 times and get 1 tail and 5 head. Determine whether it’s fair or not. What’s your confidence value?

    2. Given Amazon data, how to predict which users are going to be top shoppers in this holiday season.

    3.

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  • Popular Machine Learning Interview Questions

    We list some popular Questions related to Machine Learning. You should prepare them if you are looking for jobs related to Machine Learning Engineers, Data Scientist or Research Scientist related to Machine Learning. 

    I put the questions into three categories: Machine Learning Theories, Machine Learning Algorithms and Machine Learning Tools. 

    Machine Learning Theories

    When we talk about machine learning theories, we often refer to machine learning models such as Support Vector Machines, Decision Trees, Logistic Regression, Topic Models, Bayesian Networks and Deep Learning. 

    Here are some books that must be read:

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