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Machine Learning Algorithm category
| Category | Explain | Scenarios |
|---|---|---|
| Supervised Learning | Training data is labeled | Auto Driving Image Recognization |
| UnSupervised Learning | Training Data is not labeled | Clustering Anomaly detection Dimensionality Reduction |
| Semi-Supervised Learning | Trained on labeled and unlabeled data | self-training co-training |
| Reinforcement Learning | Adjust according to feedback to gain maximum reword | game playing Reward such as Finance natural language process |
Machine Learning Algorithms
| Name | Category | Detail | Scenarios |
|---|---|---|---|
| Linear Regression | Supervised | Continous Regression Line | |
| Logistic Regression | Supervised | Yes - No | Binary Classification Medication Dignosis Political Forecasting |
| Naive Bayes | Supervised | Based on Bayes-theory | |
| Decision Tree | Supervised | ||
| Random Forest | Supervised | Use a set of sub-forest to vote | |
| KNN - K-Nearest Neighbor | Supervised | Use the K nearst neighbor to decide where it belongs | classification |
| K-means | Unsupervised | Use a ‘center’ to define each cluster | Clustering |
| SVM - Supported Vector Machine | Supervised | Classification and regression | |
| XGBoost | Supervised | Large dataset, complex problems | classification Regression Feature selecion abnormal detection natural language processing feature selection |
| CNN Convolutional Neural Networks | Supervised, Deep Learning | Image Classification Object Detection Image Segmentation |
|
| RNN - Recurrent Neural Networks | Supervised, Deep Learning | Sequential Data Processing Time series preication Speech Recognition |
|
| GAN - Generative Adversarial Network | Supervised, unsupervised, Deep Learning | Image Generation Image to image translation abormal detection |
|
| Deep Belief Network | UnSupervised, Deep Learning | ||
| Autoencoders | Unsupervised, Deep Learning | data denoising dimensionality reduction anomaly detection |
|
| DRL - Deep Reinforcement Learning | Reinforcement, Deep Learning | Combining deep learning with reinforcement learning | Feature Learning |
| Transformer Network | Semi-supervised, Deep Learning | Natural Language Processing, including BERT, GPT | |
| Yolo - You only look once | Supervised, Deep Learning | Real time object detection Traffic Monitoring Retail Analysis |
Popular Machine Learning library
| Library | Short Description |
|---|---|
| Scikit-Learn | Traditional Machine Learning Algorithms, such as XGBoost |
| TensorFlow | Deep Learning Framework, Google backed |
| Pytorch | Deep Learning Framework, Facebook backed |
| Keras | Deep Learning Library, Popular choice and supported multiple platform |
| Numpy | Numeric computing |
| Matplotlib | Visualize 2-D data |
| Pandas | Read and Analysis structured data |
Linear Regression
SVM
KNN
K-means
Decision Tree
Random Forest
XGBoost
Importance of data versus algorithms
Popular Encoder - from text to numeric
| Name | Detail | Scenarios |
|---|---|---|
| LabelEncoder | Convert category data into a number, like 2 | Quick Preserver the order |
| OneHotEncoder | Convert category data into a binary vector such as [0,0,1] | Doesn’t assume the order Can handle unseen label |
| Binary Encoder | Convert category data into a binary vector such as [0,0] [0,1], [1,1] | has less dimensionality compared to one-hot-code |
| Target Encoder (Mean-Encoder) | Encode category data by the mean value of each category | Useful when there is strong relation between category and target variable |
| Frequency Encoder | Encodes each category by its frequency | Useful when frequency is a valuable feature |
Real Datasets
- OpenML.org - openml.org
- Kaggle.com - kaggle.com/datasets
- PapersWithCode.com - paterswithcode.com/datasets
- UC Irvine Machine Learning Repository - archive.ics.uci.edu/ml
- Amazon’s AWS datasets - registry.opendata.aws
- TensorFlow datasets - tensorflow.org/datasets
Keras Model
Sequential Model
Functional Model
ChatGPT
- How does chatGPT works - https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/