Basic Machine Learning Introductory Documents
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Collaborative Filtering Recommendation System update Dec 13, 2017
Ensemble update Dec 30, 2017
FFM update Aug 3, 2018
FM update Dec 30, 2017
Frcwp update Aug 3, 2018
GolVe_Classification add blog path Sep 25, 2018
Knowledge Summary fix bug Mar 20, 2018
Semantic recognition update Dec 3, 2017
Tsnewp update Jan 11, 2018
Youtube update Aug 3, 2018
data update Dec 30, 2017
gradient_descent update Dec 3, 2017
n-gram update Dec 11, 2017
smote update Dec 13, 2017
svd Create svd_rsvd.py Dec 2, 2017
.DS_Store add split line Sep 27, 2018
LICENSE.md MIT Feb 1, 2018
README.md add split line Sep 27, 2018

README.md

Introduction

This project implement classic machine learning algorithms(ML). Motivations for this project includes:

  • Helping machine learning freshman have a better and deeper understanding of the basic algorithms and models in this field.
  • Providing the real-life and commercial executing methods in ML filed.
  • Keeping my Mathematics Theory and Coding ability fresh due to such cases.

Overview

1.FM

1.1 fastfm

Show how to use the package of fast_fm to classify the training data directly.

1.2 Fsfm

@bolg:FM解析

We rewrite fm by ourselves and focus helping people get deeper insights about FM.So we upload it to the pypi named Fsfm,you can downlode it if you're interested in it.


2.N-gram

An interview problem in 'Nlp' solved by n-gram instead of Naive Bayes.


3.Svd

@bolg:SVD解析

3.1 Matrix decomposition in linalg

3.2 Matrix decomposition with RSVD


4.Collaborative Filtering Recommendation System

@bolg:协同推荐解析

4.1 Base of Item

4.2 Base of User


5.Semantic recognition

@bolg:评价文本判断用户流失倾向

5.1 Jieba Process

5.2 Tf-Idf

5.3 Bp Neural Network

5.4 SVM process

5.5 Naive Bayes

5.6 RandomForest


6.Gradient_descent


7.Smote

@bolg:SMOTE解析

7.1 Mean of the weight

7.2 Random scale in connected Vector


8.Frcwp

@bolg:风控方法解析

It means fast risk control with python.It's a lightweight tool that automatic recognize the outliers from a large data pool.


9.Ensemble

@bolg:Kaggle&TianChi分类问题相关算法快速实现

@bolg:Kaggle&TianChi分类问题相关纯算法理论剖析

9.1 Data preprocessing before ensemble

9.2 Case showed by stacking xgboost and logistic regression

9.3 Case showed by stacking gbdt and logistic regression

9.4 Case showed by bagging xgboots or gbdts

9.5 How to use the trained stacking model during the online module


10.Tsnewp

T-distributed stochastic neighbor embedding(t-SNE) rewrite with Python by ourselves, it's a good dimensionality reduction method. Add many explanation among the code.

Package download address.

More test data.


11.Knowledge Summary

Some questions for the new hand to estimate their level of the ML、DL. What's more ,it also contains the key point which i think during my study with Andrew Ng's machine learning lessons(to be continued).

Also, I write some words to the new hand. Read it 写给想转行机器学习深度学习的同学 if you're interested in it .

12.Youtube

Following the paper 'Deep Neural Networks for YouTube Recommendations' , finished with Python.

@bolg:利用DNN做推荐的实现过程中的总结

@bolg:关于'Deep Neural Networks for YouTube Recommendations'的一些思考和实现


13.FFM

See More From:

@bolg:基于Tensorflow实现FFM

More you may follow with interest :FM部分||deepFM部分


14.GolVe_Classification

See More From:

@bolg:GolVe向量化做文本分类

More you may follow with interest :Youtube构造skn Vector||N-Grams

Requirements

Python Environment. More details getting from single project requirement.

More

If you find some incorrect content, i'm so sorry about that. PLS contact me by the following way: