00 Machine Learning Introduction

Machine Learning Introduction

What's the Machine Learning?

Humans learn from past experiences, Whereas computers need to be told what to do, they need to be programmed.

Teaching computers to learn to perform tasks from past experiences--previous data.

1. Decision Tree(Recommending Apps)
2. Naive Bayes Algorithm(Detecting Spam emails)
3. Gradient descent
4. Linear regression(predict house price)
5. Logistic regression(acceptance at a university)
6. Support vector machine
7. Kernel Trick
8. Neural Network
9. L&S&K&N
10. K-Means Clustering
11. Hierarchical Clustering

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Main Algorithm

Decision Tree

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Decision Tree

Recommending Apps

Gender	Age	App
F	15	Pokemon Go
F	25	WhatsApp
M	32	Snapchat
F	40	WhatsApp
M	12	Pokemon Go
M	14	Pokemon Go

Decision Tree

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Naive Bayes Algorithm

Detecting Spam emails

Naive Bayes Algorithm

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Gradient descent

Look for the biggest downward direction

Gradient descent

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Linear regression

Price of a house, the best fitting line

the best fitting line

This general procedure to minimize the error, is know as gradient descent.

Least Squares

least squares
down the mountain

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Logistic regression

acceptance at a university

acceptance at a university

the log loss function, the error function

error function
error function

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Support vector machine

support vector machine

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Kernel Trick

it's very well used in support vector machines.

the curve or the plane the curve the plane

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Neural Network

neural network neural network neural network

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L&S&K&N

ls kn xor

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K-Means CLustering

k-means clustering

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Hierarchical Clustering

hierarchical clustering

块级公式：

行内公式：