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