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.
Show how to use the package of
fast_fm to classify the training data directly.
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.
An interview problem in 'Nlp' solved by n-gram instead of Naive Bayes.
3.1 Matrix decomposition in linalg
3.2 Matrix decomposition with RSVD
4.Collaborative Filtering Recommendation System
4.1 Base of Item
4.2 Base of User
5.1 Jieba Process
5.3 Bp Neural Network
5.4 SVM process
5.5 Naive Bayes
7.1 Mean of the weight
7.2 Random scale in connected Vector
It means fast risk control with python.It's a lightweight tool that automatic recognize the outliers from a large data pool.
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
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.
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 .
Following the paper 'Deep Neural Networks for YouTube Recommendations' , finished with Python.
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Python Environment. More details getting from single project requirement.
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