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.
- Decision Tree(Recommending Apps)
- Naive Bayes Algorithm(Detecting Spam emails)
- Gradient descent
- Linear regression(predict house price)
- Logistic regression(acceptance at a university)
- Support vector machine
- Kernel Trick
- Neural Network
- L&S&K&N
- K-Means Clustering
- Hierarchical Clustering
Main Algorithm
Decision Tree
Recommending Apps
Gender | Age | App |
---|---|---|
F | 15 | Pokemon Go |
F | 25 | |
M | 32 | Snapchat |
F | 40 | |
M | 12 | Pokemon Go |
M | 14 | Pokemon Go |
Naive Bayes Algorithm
Detecting Spam emails
Gradient descent
Look for the biggest downward direction
Linear regression
Price of a house, the best fitting line
This general procedure to minimize the error, is know as gradient descent.
Least Squares
Logistic regression
acceptance at a university
the log loss function, the error function
Support vector machine
Kernel Trick
it’s very well used in support vector machines.
Neural Network
L&S&K&N
K-Means CLustering
Hierarchical Clustering
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