ML NLP

Project Introduction

• This project is a knowledge point and code implementation that is often tested in machine learning and NLP interviews , and is also a theoretical basic knowledge that an algorithm engineer must know.
• Since the interview is the main purpose, it cannot be summarized. Please understand and ask questions.
• This project takes each module as the entry point, allowing everyone to have a clear knowledge system.
• This project can also be used for frequent reading, regular recording and review during interviews.
• The questions in each chapter are knowledge points that may be asked during the interview. If there are any omissions, please contact me to add them. There are practical code cases of algorithms at the end.
• For mind maps, please follow the AIArea official account and reply: NLP mind maps can download high-definition large images.
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Table of contents

• The project is being updated continuously...

Module	chapter	GitHub)	Contact QQ
Machine Learning	1. Linear Regression	@mantchs	448966528
Machine Learning	2. Logistics Regression	@mantchs	448966528
Machine Learning	3. Decision Tree	@mantchs	448966528
Machine Learning	3.1 Random Forest	@mantchs	448966528
Machine Learning	3.2 Gradient Improvement Decision Tree (GBDT)	@mantchs	448966528
Machine Learning	3.3 XGBoost	@mantchs	448966528
Machine Learning	3.4 LightGBM	@mantchs	448966528
Machine Learning	4. Support Vector Machine (SVM)	@mantchs	448966528
Machine Learning	5. Probabilistic Graphical Model
Machine Learning	5.1 Bayesian Network	@mantchs	448966528
Machine Learning	5.2 Markov	@mantchs	448966528
Machine Learning	5.3 Topic Model	@mantchs	448966528
Machine Learning	6. Maximum Expected Algorithm (EM)	@mantchs	448966528
Machine Learning	7.Clustering	@mantchs	448966528
Machine Learning	8.ML feature engineering and optimization methods	@mantchs	448966528
Machine Learning	9.KNN algorithm (KNN)	@mantchs	448966528
Deep Learning	10. Neural Network	@mantchs	448966528
Deep Learning	11. Convolutional Neural Network (CNN)	@mantchs	448966528
Deep Learning	12. Recurrent Neural Network (RNN)	@mantchs	448966528
Deep Learning	12.1 Gated Circulation Unit (GRU)	@mantchs	448966528
Deep Learning	12.2 Long and short-term memory (LSTM)	@mantchs	448966528
Deep Learning	13. Transfer Learning	@mantchs	448966528
Deep Learning	14. Reinforcement & multitasking	@mantchs	448966528
Deep Learning	15. Optimization method for deep learning	@mantchs	448966528
NLP	16. Natural Language Processing (NLP)	@mantchs	448966528
NLP	16.1 Word Embedding (Word2Vec)	@mantchs	448966528
NLP	16.2 Subword embedding (fastText)	@mantchs	448966528
NLP	16.3 Global vector word embedding (GloVe)	@mantchs	448966528
NLP	16.4 textRNN & textCNN	@mantchs	448966528
NLP	16.5 Sequence to sequence model (seq2seq)	@mantchs	448966528
NLP	16.6 Attention Mechanism	@mantchs	448966528
NLP	16.7 Transformer Model	@mantchs	448966528
NLP	16.8 BERT Model	@mantchs	448966528
NLP	16.9 XLNet Model	@mantchs	448966528
project	17. Recommendation System	@mantchs	448966528
project	18. Intelligent Customer Service	@mantchs	448966528
project	19. Knowledge Graph
project	20. Comment analysis

Everyone is welcome to join! Together, improve this project! NLP learning QQ2 group [207576902]