Computer Vision Tutorial — Lesson 6

Computer Vision Tutorial — Lesson 6

This tutorial is the foundation of computer vision delivered as “Lesson 6” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the complete syllabus and table of content here

Note from author :

_This tutorial is the foundation of computer vision delivered as “Lesson 6” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the [complete syllabus and table of content here_](https://medium.com/@Rakesh.thoppaen/free-computer-vision-full-syllabus-65059d21ea2a?source=friends_link&sk=519548ce05dc078146506fe30dd0edbc)

Target Audience_ : Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career ._

Takeaway_ : Main takeaway from this article :_

  1. Machine Learning Introduction

2. Machine Learning Algorithms

a) K — Nearest neighbor Algorithm

b) KNN Exercise : K-Nearest Neighbor classifier to classify hand written digits images from the MNIST datasets

Machine Learning Introduction

Machine Learning Is Not the Same as Human Learning

As human,you can learn how to deal with almost any situation without explicit instructions. If you sell houses for a long time, you will instinctively have a“feel”for the right price for a house,the best way to market that house and the kind of client who would be interested in it. You have the ability to extrapolate understanding from very little data and you can project your understanding on to new situations. The goal of strong AI research is to be able to replicate this ability with computers,but current machine learning algorithms aren’t that good yet. They only work on very specific,limited problems. Essentially,they are just statistical models that come up with the most likely answer based on all the data they have seen so far. May be a better definition of“learning”in this case is “figuring out an equation to solve a specific problem based on lots of example data.” Unfortunately “machine figuring out an equation to solve a specific problem based on lots of example data”isn’t really a great name. So we ended up with“machine learning”instead. But don’t be mistaken;today’s machine learning has nothing in common with the artificial intelligence portrayed in science fiction. It’s fundamentally a much more limited thing. We have no idea how to build truly intelligent machines. Of course,if you are reading this 50 years in the future and we’ve figured out the algorithm for strong AI,then things might be different. May be stop reading this article and go tell your robot servant to go make you a briyani, future human. And if we’ve figured out time travel too,please send me back some stock tips.

There are 3 types of Machine Learning Algorithms

  • *Supervised Learning: *we have both the image data (in either raw image format or the extracted feature vectors) along with the label/category associated with each image, so we can “teach” our algorithm what each image category looks like.

machine-learning artificial-intelligence python computer-vision programming

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