Unsupervised LearningYouTube↗  IA↗(long shot)
Education  21.6K views 28 likes 4 dislikes   23 Feb 2015
Udacity 651K subscribers United States YouTube↗


Please select the problem
You must specify your email and describe the problem in detail
Describe your problem in detail if it's not obvious

Click to Play Video Click to Play Video
إذن Katie، ستتناول هذه الوحدة التعلم غير الخاضع للإشراف.
So Katie, this is going to be a unit on unsupervised learning.
>> التعلم غير الخاضع للإشراف هو أمر بالغ الأهمية،
>> Unsupervised learning is something that's very important,
ففي أغلب الوقت تكون البيانات التي تحصل عليها في الواقع ليس لها علامات
because most of the time, the data that you get in the real world doesn't have
تدلك على الإجابة الصحيحة.
little flags attached that tell you the correct answer.
إذن فما الذي ينبغي عليك فعله في هذه الحالة باعتبار أنك تمارس التعلم الآلي؟
So what are you to do as a machine learner in that case?
تلجأ إلى تقنيات التعلم غير الخاضع للإشراف كي يظل بإمكانك اكتشاف شيء
You turn to unsupervised techniques to still figure something out
عن البيانات.
about that data.
>> حسنًا، لنتحدث عن هذه التقنيات.
>> Okay, let's talk about them.
لنفترض أن لدينا مجموعة بيانات دون تسميات، وأن جميع نقاط البيانات من الفئة نفسها.
Given a dataset without labels over all the data points are of the same class.
أحيانًا تظل هناك أمور يمكنكِ فعلها لاستخراج معلومات مفيدة.
There are sometimes still things you can do to extract useful information.
على سبيل المثال، مجموعة البيانات الموجودة هنا، حيث يمكنني أن أقول إن مجموعة البيانات هذه مبنية
Like this dataset over here, where I would say this dataset is structured in
بطريقة من المفيد لأي خوارزمية تعلم آلي أن تتعرف عليها.
a way that is useful to recognize for a machine learning algorithm.
>> عند النظر إلي ذلك بالعين، يبدو وكأن هناك كتلًا أو
>> When we look at this by eye, it looks like there's clumps or
مجموعات داخل البيانات.
clusters in the data.
وإذا استطعنا التعرف على هذه الكتل أو المجموعات، فقد نتمكن من معرفة شيءٍ ما عن
And if we could identify those clumps or clusters, we could maybe say something
نقطة بيانات جديدة وغير معروفة وكذلك معرفة ما قد يكون عليه شكل جيرانها.
about a new, unknown data point and what its neighbors might be like.
>> أو، إليكِ مثالًا آخر على البيانات.
>> Or here's a second example of data.
قد تبدو البيانات كذلك بالضبط.
Maybe the data looks just like this.
هناك ما يمكن معرفته في هذه الحالة أيضًا.
There's something we can say here as well.
>> صحيح.
>> Right.
تبدو جميع البيانات في هذا المثال وكأنها تقع على خط من نوع ما
So all the data in this example looks like it lives on some kind of line or
أو على شكل معقد نوعًا ما كالذي يبدو أنك ترسمه الآن.
some complicated shape that you seem to be drawing in there right now.
>> أجل. وقد كان ذلك فضاءً ثنائي الأبعاد، حيث توجد x
>> Yeah. And it's, it's, it's used to be a two-dimensional space, with x and
وy هنا.
y over here.
ولكن يمكننا تقليل جزء منه ليصبح خطًا أحادي البعد.
But some of it we can reduce it to a one-dimensional line.
وما اسم هذا الأسلوب؟
So that's called what?
>> عادةً ما يسمى تقليل الأبعاد.
>> That's called dimensionality reduction, usually.
>> تقليل الأبعاد.
>> Dimensionality reduction.
إذن فقد عرفنا بعض الأمور عن تكوين أنظمة المجموعات.
So we learned about, a little bit about clustering.
>> وتكوين أنظمة المجموعات هو ما سنتعلمه في هذا الدرس.
>> Clustering is what we'll learn in this lesson.
>> ويمكنك هنا رؤية مثال على ما يسمى كذلك بالتعلم غير الخاضع للإشراف
>> And you can see here an example of something also called unsupervised
لتقليل الأبعاد.
learning of dimensionality reduction.
>> وهو ما سنتعلمه في درس قادم.
>> Which we will get in a future lesson.
>> إذن، تسمى الأشياء التي تجد فيها بنية في البيانات دون تسميات،
>> So these kind of things where you find structure in the data without labels,
تعلمًا غير خاضعٍ للإشراف.
they're called unsupervised learning.
وسوف ندخل الآن إلى العالم الرائع
And we're now gong to dive into the wonderful,
للتعلم غير الخاضع للإشراف.
wonderful magical land of unsupervised learning.
>> يبدو هذا رائعًا، فلندخل في صلب الموضوع.
>> Sounds great, let's dive in.
Similar videos:

Prerequisites
Udacity
80.5K views 158 likes 10 dislikes 23 Feb 2015100% match

Introduction Part II
Udacity
90.9K views 164 likes 12 dislikes 23 Feb 2015100% match

Separating with the New Feature - Intro to Machine Learning
Udacity
32.3K views 6 likes 0 dislikes 23 Feb 2015100% match

Normalizing 1 - Intro to Machine Learning
Udacity
54.7K views 4 likes 0 dislikes 23 Feb 2015100% match

Unsupervised Learning
Udacity
4K views 4 likes 0 dislikes 06 Jun 2016100% match

Information Gain Calculation Part 1 - Intro to Machine Learning
Udacity
32.3K views 8 likes 25 dislikes 23 Feb 2015100% match

Cancer Test - Intro to Machine Learning
Udacity
51.2K views 28 likes 6 dislikes 23 Feb 2015100% match

Scatterplots to Decision Surface - Intro to Machine Learning
Udacity
104.4K views 16 likes 0 dislikes 23 Feb 2015100% match

What is Unsupervised Learning?
Matthew Voke
59 views 6 likes 0 dislikes 03 Apr 2020100% match

Speed Scatterplot 2 - Intro to Machine Learning
Udacity
38.6K views 11 likes 1 dislikes 23 Feb 2015100% match

Speed Scatterplot: Grade and Bumpiness - Intro to Machine Learning
Udacity
94.6K views 37 likes 3 dislikes 23 Feb 2015100% match

Decision Tree Parameters - Intro to Machine Learning
Udacity
45.7K views 61 likes 3 dislikes 23 Feb 2015100% match

Classification By Eye Solution - Intro to Machine Learning
Udacity
55.7K views 28 likes 0 dislikes 23 Feb 2015100% match

Playing Around with Kernel Choices - Intro to Machine Learning
Udacity
47.2K views 17 likes 0 dislikes 23 Feb 2015100% match

SVM Gamma Parameter
Udacity
34K views 195 likes 6 dislikes 17 Mar 2015100% match

Features Visualization Quiz - Intro to Machine Learning
Udacity
111.3K views 31 likes 3 dislikes 23 Feb 2015100% match

Multiple Linear Questions - Intro to Machine Learning
Udacity
31.4K views 10 likes 1 dislikes 23 Feb 2015100% match

Quiz - Intro to Machine Learning
Udacity
132.5K views 107 likes 5 dislikes 23 Feb 2015100% match

Intro To Stanley Terrain Classification
Udacity
48.9K views 42 likes 0 dislikes 23 Feb 2015100% match

SVMs and Tricky Data Distributions - Intro to Machine Learning
Udacity
38.4K views 9 likes 1 dislikes 23 Feb 2015100% match

Training and Testing Data
Udacity
30.1K views 39 likes 1 dislikes 23 Feb 2015100% match

Overfitting - Intro to Machine Learning
Udacity
43.6K views 46 likes 10 dislikes 23 Feb 201569% match

Normalizing 2 - Intro to Machine Learning
Udacity
48.4K views 7 likes 4 dislikes 23 Feb 201569% match

Normalizing 3 - Intro to Machine Learning
Udacity
44.6K views 2 likes 8 dislikes 23 Feb 201569% match

Solution - Intro to Machine Learning
Udacity
74.3K views 146 likes 0 dislikes 23 Feb 201569% match

Introduction Pt. III
Udacity
81.5K views 130 likes 4 dislikes 23 Feb 201567% match

Kernel Trick
Udacity
64.7K views 161 likes 102 dislikes 23 Feb 201567% match

Gaussian NB Example
Udacity
50.3K views 80 likes 19 dislikes 23 Feb 201567% match

Scatterplots to Predictions - Intro to Machine Learning
Udacity
82K views 22 likes 1 dislikes 23 Feb 201564% match

Prior and Posterior - Intro to Machine Learning
Udacity
68.8K views 44 likes 19 dislikes 23 Feb 201564% match


Settings Privacy About Contact Donate