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Machine Learning is not just glorified Statistics

People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world. — Pedro Domingos

What is Machine Learning

Machine Learning(ML) is a method of data analysis that allows a system to learn without being explicitly programmed. Or in the words of Tom Mitchell, “A computer program is said to learn…


How to use descriptive statistics to see what the data shows

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

In statistics, what we see in our data is interesting not to ourselves, but to…


Machine Learning is not just glorified Statistics

People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world. — Pedro Domingos

What is Machine Learning

Machine Learning(ML) is a method of data analysis that allows a system to learn without being explicitly programmed. Or in the words of Tom Mitchell, “A computer program is said to learn…


Machine Learning is not just glorified Statistics

Image by Author

People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world. — Pedro Domingos

What is Machine Learning

Machine Learning(ML) is a method of data analysis that allows a system to learn without being explicitly programmed. Or in the words of Tom Mitchell, “A computer program is said to learn…


How to use descriptive statistics to see what the data shows

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

In statistics, what we see in our data is interesting not to ourselves, but to…


How to use descriptive statistics to see what the data shows

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

In statistics, what we see in our data is interesting not to ourselves, but to…


A quick guide on the differences between quantitative and qualitative data

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

Defining the type of variable you are working with is always the first step in…


A quick guide on the basic concepts used to describe data

“Facts are stubborn things, but statistics are pliable”

― Mark Twain

The job of a data analyst is not to come up with a lot of fancy reports containing tons of data as it may first seem. He needs to understand what the data can tell the business or help it solve existing problems.

Okay, we have data what’s next?

The next step is to get insights from them.

What are insights?

Insights are valuable knowledge obtained with the help of data analytics. It’s a very general definition because under the category of insides can fall a lot of things…


A quick guide on the basic concepts used to describe data

“Facts are stubborn things, but statistics are pliable”

― Mark Twain

The job of a data analyst is not to come up with a lot of fancy reports containing tons of data as it may first seem. He needs to understand what the data can tell the business or help it solve existing problems.

Okay, we have data what’s next?

The next step is to get insights from them.

What are insights?

Insights are valuable knowledge obtained with the help of data analytics. It’s a very general definition because under the category of insides can fall a lot of things…


A quick guide on the differences between quantitative and qualitative data

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

The whole series:

Defining the type of variable you are working with is always the…

Kirill Bobrov

helping robots conquer the earth and trying not to increase entropy using Python, Big Data, ML. Linkedin @luminousmen. Check out my blog — luminousmen.com

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