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Machine Learning
- 01.ML basic--go through the machine learning process.
- 02.Matrix(derivation & lsm)--performing matrix calculations using NumPy.
- 03.Linear Regression--go through the linear regression process.
- 04.Logistic Regression--go through the logistic regression process.
- 05.Classification model & model evaluation--concepts of classification model decision boundaries & model evaluation.
- 06.Scikit-Learn--use Scikit-Learn to building and evaluating machine learning models.
- 07.Clustering model--clustering models: KMeans and DBSCAN.
- 08.Decision Tree--Decision Trees: ID3 (Iterative Dichotomiser 3), C4.5, & CART.
- 09.bagging & Random Forest--Ensemble Learning, Bagging & Random Forests
- 10.HPO Grid OPT & Bayesian OPT--HPO using Grid Search, Random Search & Bayesian Opt.
- 11.AdaBoost--AdaBoost (Adaptive Boosting)
- 12.GBDT--loss functions used in GBDT & optimizing GBDT using TPE
- 13.XGBoost--using XGBoost for regression and classification, exploring the concepts of three estimators and DART, Structure Score & Gain of Structure Score, and XGBoost hyper-opt using TPE
- 14.LightGBM--LightGBM, including Exclusive Feature Bundling(EFB), Gradient-based One-Side Sampling(GOSS), common hyperparameters, and the process of hyper-opt for LightGBM
- 15.CatBoost--CatBoost, a gradient boosting library designed for categorical feature support
- Practice--Practice
Deep Learning
- 01. NN based onTorch --create a basic neural network using PyTorch：
- 02. CNN --go through the process of building a Convolutional Neural Network
- 03. Training-image-classification-model --image classification model based on classic architecture
- 04. OpenCV
- 05. transformer & resnet
- 06. Classic Project for Object Detection
- 07. NLP Basic
- 08. Sentiment Analysis with LSTM
- 09. BERT 10. HuggingFace
Django Project
