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Experienced Ph.D. with a demonstrated history of working in the higher education industry. Skilled in Data Science, AI, Deep Learning, Big Data, & Mathematics.

“What is the Big Data?” When we ask it will come across many definitions, many many data” and many things. I thought I’d better write a little bit about it.

https://restart-project.eu/big-data-matters/

Note: I usually will use some abbreviated words below:

  • Machine Learning — ML
  • Big Data — BD

Lets talk about the BD:

“What is the BD?” When we ask to Google then we will come across many definitions, many “many data” and many things.

I agree with a friend’s comment and share the same quote with you:

“I’ve been dealing with BD for years, but I still don’t understand what…


We watch a movie or listen to music on apps like Netflix and Spotify, and then a movie or music comes in the same round! How do these apps accomplish this or what’s in the background? Let’s try to learn together.

https://morioh.com/p/129aab71f5c2

With the development of technology, our habits are also changing. Instead of shopping from stores, we buy our needs through e-commerce, we watch Netflix instead of watching TV or listen to music on Spotify. One of the reasons why these companies are so popular can be shown that their business structures are based on recommendation systems. If you have…


After explaining the Ensemble Method and its underlying Bagging Vs Boosting methods, it is time to explain( with Question/Answer) the differences between them. This article is the sixth and the last part of the series(Bagging & Boosting Ensemble Methods and What is the Difference Between Them?).

https://dataaspirant.com/ensemble-methods-bagging-vs-boosting-difference/

In this article, I will try to discuss what Random Forests, GBM, XGBoost, LightGBM, and CatBoost, etc. have to do with these approaches.

Here we will ask a general question and try to answer it.

Question:
In tree-based methods, there are the expressions “Bagging” and “Boosting”. …


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the fifth article in this series.

https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/

Now that we’ve learned how the bagging and boosting methods work in the series of articles on this topic, let’s try to understand how there are differences between them.

Split datasets

For splitting the actual train data to multiple datasets, as known as the bootstrap samples both these methods use the bootstrapping statistical method.

In bagging once the bootstrap samples create, there will be no changes for building multiple models. Where as in the boosting based…


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the fourth article in this series. In this part, we will talk about What Is the “Boosting” Ensemble Method?

https://www.pluralsight.com/guides/ensemble-methods:-bagging-versus-boosting

I have written about the bagging issue in my previous article. On top of that, we can see the difference between Boosting and Boosting above, but let’s try to see the system running in the background and finally share the pros and cons.

All the individual models formed by the boosting method in sequentially. …


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the third article in this series. In this part, we will talk about “What is the “Bagging” Ensemble Method”.

https://www.educba.com/bagging-and-boosting/

In fact, we can see that the bagging method, as we have selected above, gives us visually what it tries to explain. But let’s try to explain the idea behind it.

Behind idea in bagging is to combine the results of multiple models (eg all decision trees) to produce a more general result.

The ones that come to mind are:

  • Would…


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the second article in this series. In this part, we will talk about “Weak Learners & Strong Learners for Machine Learning”.

https://livebook.manning.com/book/grokking-machine-learning/chapter-10/v-9/14

Note: I usually will use some abbreviated words below:

  • Machine Learning — ML

Let’s start…

In both, homogeneous and heterogeneous ensemble methods we said that the individual models are called weak learners, in the homogeneous ensemble method these weak learners are built using the same ML algorithms, whereas in the heterogeneous ensemble methods these weak learners are built using…


This series(“Bagging & Boosting Ensemble Methods and What is the Difference Between Them?”) consists of 6 separate articles and is the first article in this series. In this part, we will talk about “What is Ensemble Learning?”.

https://link.springer.com/article/10.1007/s00521-020-04986-5

Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Machine Learning — ML

Let’s start…

In the world of ML, ensemble learning methods are the most popular topics to learn. These ensemble methods have been known as the winner algorithms. …


I tried to bring clarity to this area with the second and last part of the series on this topic. After that, when the data comes in, you will know what to do next!

https://www.dreamstime.com/illustration/data-tsunami.html

Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Exploratory Data Analysis — EDA

Then let’s go…

In the first article you have questions about how to behave when it comes to data.
1. What’s the Purpose?
2. Tidy Data Process
3. Determining and Setting Variable Types
4. Summary Statistics: Showing the Basic Structure of the Data Set

We tried to bring…


I wrote this topic in two articles without boring you. We start with the first part of the series. We got data and we did the reading with the pandas library so what are we going to do? Here we will look for the answer to this question!

http://www.lowtechcartoon.com/?p=934

Note: I usually will use some abbreviated words below:

  • Data Science — DS
  • Exploratory Data Analysis — EDA

Let’s go…

I don’t mean a problem at the beginning of the data preparation phase, but unfortunately it is! If we talk about that subject here, we will deviate from our noble purpose.

Mehmet A.

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