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*Aim: After reading this post and Part 2, the reader should feel comfortable using PCA and able to explain what these numbers fra PCA mean.*

*Audiences: Part 1 does not require any math background, mostly is logic and intuition. Part 2 will assume that the reader is familiar with matrix, eigenvalue, and eigenvector.*

**Table of content:**

- Introduction
- The intuition of PCA
- A simple example
- Summary

**Principal component analysis (PCA)** is widely used fordata science and machine learning. **PCA can reduce dimension such that it is less heavy for further process.** But how could that be possible? Do we lose information? The answer is that we do lose some information, but not a lot if we do it in a smart way. This post is the convenience that it is possible with visualization and hand-calculation.

There are many resources on the internet to teach PCA. Some of them are too shallow that readers do not feel confident to use it. Some of them are too mathematically in-depth that require the reader to have a good math background. I, personally, very like math and believe there is a way to make sense of these numbers from PCA without going through many mathematic theorems.

In Part I, we will explain what PCA is, when do you need it, and why do you need it. We will show you the very basic idea and intuition of PCA and some simple examples and hand-calculate some numbers to give you some flavor.

In Part II, we will transform our intuition of PCA into mathematic and interpret the data from PCA.

Figure1. Source: Hand drew by the author.

Before we explain what PCA is, let us take a look at image 1. Our data are those green points. They are originally represented in **(x,y) **coordinate. In real-life applications, **XY **pairs can be weight and height. They can be income and health, and etc. Just make sure they are in the **mean-deviation form**, which means the mean of data is zero in all dimensions. And somehow, we have found a new orthogonal coordinate system **(PC1, PC2)**. In our data, we can clearly see that the data spread along with PC1-axis. And PC2-axis does not represent the data very much because most of our data points are very close to zero in PC2-axis.

Two simple questions

Q: Could we drop the PC2-axis when we use (PC1, PC2) coordinate and still able to preserve most of the information?

A: Yes, we can. Remove PC2-axis is the same as projecting all data points on PC1-axis, and it does not do much change in (x,y) after projection on PC1-axis.

Q: Could we see how much contribute does xy-axis have on PC1-axis from the image?

A: Yes, we can. It is clear that PC1-axis has much more projection on y-axis than x-axis does.

So, What is PCA? PCA is all about finding the new coordinate in image 1. More precisely, PCA is all about rotating the coordinate system (frame of reference) for a better representation of your data. In most case, it turns out some of the dimensions does not give much representation of our data, and it can be dropped without losing much information. But what a minute, why do we want to reduce the dimension of the data? In real-life applications, Our data can have more than hundreds of dimensions. And In many cases, it turns out the new coordinate found from PCA, only a few of these hundreds of dimensions is responsible for 90% of data. This is good news because the fewer dimension your data have, the less the computational heavy. Another reason we would like to use PCA is that we can gain insight into our data. It can tell us which few dimensions in our original coordinate dominate the data. OK! How can we find this new coordinate? Let us see an example.

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SAFEMOON UPDATE - ALL YOU NEED TO KNOW ABOUT SAFEMOON AND SAFEMOON PREDICTION

This is all you need to know about safemoon and I provide my safemoon prediction. This is a huge safemoon update so make sure to watch this video until the end.

📺 The video in this post was made by Josh’s Finance

The origin of the article: https://www.youtube.com/watch?v=ZtX7ZIVcXH4

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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AI is available in our lives in numerous spaces, from telephones where we can go without much of stretch access practically any data anyplace on the planet to grocery stores where we can shop with a ‘tick’; from banks where we can undoubtedly handle exchanges online to social stages where we invest the majority of our energy.

Yet, can artificial intelligence make a positive commitment to understanding the issues brought about by environmental change and problems continuously transforming into an emergency?

Indeed, AI can help environment analysts discover arrangements in numerous spaces, for example, air contamination. An illustration of this is IBM’s Green Horizon Project, which predicts contamination by breaking down ecological information and testing what will occur if contamination is decreased.

Similarly, Google has reduced its server farms’ energy by around 15% by utilizing data from AI calculations.

Projects like these can likewise help and urge different firms to diminish their carbon impression.

Astounding advancement has been made to utilize AI calculations dependent on information from other outrageous climate occasions to recognize hurricanes and environmental waterways. What’s more, even though machines are not entirely believed, environment researchers can work with them to gain better headway.

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Crypto Categories You NEED TO KNOW!! 101 Guide 🤓

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0:00 Intro

1:53 Store Of Value Cryptocurrencies

5:53 Smart Contract Cryptocurrencies

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15:13 Privacy Cryptocurrencies

19:00 Exchange Tokens

20:45 Meme Coins

23:48 Conclusion

📺 The video in this post was made by Coin Bureau

️ The origin of the article: https://www.youtube.com/watch?v=7NYmape2ylA

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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#all the basics that you need to know about crypto mining.

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The ever-increasing growth in the production and analytics of Big Data keeps presenting new challenges, and the data scientists and programmers gracefully take it in their stride – by constantly improving the applications developed by them. One such problem was that of real-time streaming. Real-time data holds extremely high value for businesses, but it has a time-window after which it loses its value – an expiry date, if you will. If the value of this real-time data is not realised within the window, no usable information can be extracted from it. This real-time data comes in quickly and continuously, therefore the term “Streaming”.

Analytics of this real-time data can help you stay updated on what’s happening right now, such as the number of people reading your blog post, or the number of people visiting your Facebook page. Although it might sound like just a “nice-to-have” feature, in practice, It is essential. Imagine you’re a part of an Ad Agency performing real-time analytics on your ad-campaigns – that the client paid heavily for. Real-time analytics can keep you posted on how is your Ad performing in the market, how the users are responding to it, and other things of that nature. Quite an essential tool if you think of it this way, right?

Looking at the value that real-time data holds, organisations started coming up with various real-time data analytics tools. In this article, we’ll be talking about one of those – Apache Storm. We’ll look at what it is, the architecture of a typical storm application, it’s core components (also known as abstractions), and its real life-use cases.

Let’s go!

**Table of Contents**

- What is Apache Storm?
- Apache Storm: General Architecture and Important Components
- Master Node (Nimbus Service)
- Worker Node (Supervisor Service)
- Topology
- Stream
- Spout
- Bolt
- Who Uses Storm?
- Spotify
- To Wrap Up…

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