Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn

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This Machine Learning tutorial will help you understand why Machine Learning came into the picture, what is Machine Learning, and the types of Machine Learning., Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine, and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm.

Below topics are explained in this Machine Learning tutorial:
00:00 - 00:45 Why Machine Learning?
00:45 - 04:52 What is Machine Learning?
04:52 - 11:34 Types of Machine Learning
11:34 - 16:41 Machine Learning Algorithms
16:41 - 25:43 Linear Regression
25:43 - 34:00 Decision Trees
34:00 - 36:02 Support Vector Machine
36:02 - 01:02:40 Use case: Classify whether a recipe is of a cupcake or a muffin using SVM

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What is Machine Learning?
Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs learn, grow, change, and develop by themselves.

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32:30 Why is "Windy" the 1st branch node of "Outlook"? "Humidity" should be the node since it has better information gain as seen from the previous slide.

cantcurecancer

I made the data set from scratch and it worked just pause the video when he shows the file. Type it in excel and save as CSV, then make sure you upload it to Jupiter notebook. It will work

theemillennial

I have no idea what im doing here, never finished college, failed all my math classes, no idea how to this thing intrigues me

yamiryuu

Thanks for sharing! At 19:40 there will be inverse proportionality. That's why the graph is a hyperbola, and not the line with negative coefficient. You can easily deduce it from s = vt. So, t =s/v. Or t = const/v which is obviously not a line.

Мансур-пы

A - Classification
B - Deep Learning (Neural Networks)
C - Error
D - Supervised Learning (Regression, Naive Bayes)

I know I am 100% correct! :D

sandeepdayananda

At 30:16 shouldn't it be E(9, 5) since the 9 is yes and the 5 is no?

nickromano

For the record at 17:27 c is your Y-intercept and m is your coefficient (or better known as the slope). Set x to 0 and you are left with y = c. At 29:44 it's not Log squared. It's Log Base 2. Log without the subscript is Log Base 10. It's also not Log "root 2".

mswlogo

Can you please explain how it is calculated? 5/14 * I(3, 2) = ?

pratikmondal

Can you please explain these concept ?

Sigmoid activation function
𝜎(𝑥)=11+𝑒−𝑥
Output (prediction) formula
𝑦̂ =𝜎(𝑤1𝑥1+𝑤2𝑥2+𝑏)
Error function
𝐸𝑟𝑟𝑜𝑟(𝑦, 𝑦̂ )=−𝑦log(𝑦̂ )−(1−𝑦)log(1−𝑦̂ )
The function that updates the weights

theinternetcash

This is very interesting. I was thinking it was a lot harder, but it's not. It is hard but easier than I thought.

icakad

when you defined the cupcake_or_muffin, and you tried it, it worked. the definition of mine works, but when I try it, it does not work

mathstats

I know this is a year later but when i type:: model = svm.SVC(kernel = 'linear') model.fit(ingredients, type_label) the output i get is SVC(kernel='linear') and that's it.

dcpt_n

Good content and I am currently attending data science master's program by Simpliilearn and I think I choose the better one

yashwanth

A, classification
B, classification
C, reggression
D, anomaly

WahidAliDataScienceBatch

Thank you for your tutorial, but the sound is poor, and treble high. Please increment bass.

KevinNguyen-ilzf

f you are have understood very basic, to go deeper into the M.L and Deep learning, check Sniffer Search

jamierahman

at 31:10, why is it the I(3, 2) and I(4, 0) and I(2, 3) ? Shouldn't it be I(3/5, 2/5) and I( 4/4, 0/4) and I ( 2/5, 3/5) ?

drcrazzy

truly I can understood a lot of these topics.

rameshlanka

Not to criticize, but to see improvement. There is a mistake in the vedio at 29th minute and 58th second. Please have a look and rectify it.

Mr_Clumsee

I'm new to this thing. So majority of Machine Learning is about solving math problem?

jumbo

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