Here are some notes of my personal record and practice in machine learning field.

1. Exploratory Data Analysis On Iris Dataset
   Summarize exploratory data analysis methods in iris dataset

2. Linear Model For Regression
   Training and comparing different linear regression models' performance and some strategies of finding the best regression model

3. Taiwan ETF Prediction Use gradient boosting regressor model to predict the ETF price

4. Kaggle House Prediction
   The code for kaggle 'House Prices: Advanced Regression Techniques

5. Compare Classification Model Performance
   Compare the training time and f1 score between 5 basic classifcation model

6. Image Classification
   Use fast ai library to classify the where am I dataset

Collect some template for machine learning and deep learning.

1. Logistic Regression - use logistic regression to classify data
2. Decision Tree - use decision tree to classigy data
3. K means - use k means method to cluster the data
4. K Nearest Neighbors - classification data
5. Support Vector Machine - classification data
6. One class SVM(Anomaly detection) - sample of anomaly detection
7. Linear regression model - regression model with tunning

1. CNN for fruit recognition
2. CNN for Cifar10 classification
3. RNN for classify trash SMS
4. LSTM for text binary analysis

1. DNN Regressor
2. Neural Network Classification
3. DNN Classifier
4. ResNet Transfer Learning
5. Linear Regression Sample

1. Cifar10_CNN_turorial
2. Custom NN function
3. Dynamic Graph & Control Flow in NN
4. Pytorch NN model
5. Neural Network Tutorial
6. Transfer_learning_Tutorial
7. Word Classification Example

1. FastAI CNN Transfer Learning

1. Linear regression
2. Recommender system sample

To be updated...