Machine Learning Tutroial - iami

1 .Machine Learning Tutorial Basic introduction

2 . Computer Version Nature Language Processing Robotic/Automatic Biology

3 .

4 .Basic Concept Cross Validation Over Fitting Activation Function Supervised Learning UnSupervised Learning Batch Epoch Layer Train Set Test Set Feature Dimensionality Generalization Maxpooling L1 /L2 Benchmark Baseline

5 .Basic Concept

6 .Basic Concept

7 .Supervised/Unsupervised Learning

8 .Supervised Learning

9 .Supervised Learning

10 .Unsupervised Learning

11 .Basic Algorithm K NN K Means Naïve Bayes Decision Tree Gradient Boosting algorithms SVM Random Forest Linear Regression Logistic Regression Dimensionality Reduction Algorithms

12 .How do you choose a machine learning algorithm ?

13 .How do you choose a machine learning algorithm ?

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15 .Output Input Feature Engineering Data PreProcessing

16 .With Python Or R Python and Pythonic ML library Common library

17 .With Python Or R Python and Pythonic ML library Common library Don’t fighting language, Pythonic

18 .With Python Or R Python and Pythonic ML library Common library Tensorflow Keras Scikit -Learn Pytorch

19 .With Python Or R Python and Pythonic ML library Common library Pandas Numpy Scipy Matplotlib Pyplot Ndarray Scikit -image Image

20 .R: I forget it ... Easily And Powerful

21 .Examples: DGA/XSS/ Webshell MNIST

22 .MNIST DGA/XSS/ Webshell

23 .Mnist And Fashion- Mnist Pro Beginners Online Demo

24 .Algorithm 1 : KNN

25 .Principle Wikipedia

26 .Principle

27 .Code in Python

28 .Code in Python

29 .Algorithm 2 : SVM