Datacamp_supervised_learning_with_scikit Learn
This is a memo to share what I have learnt in Supervised Learning With Scikit-Learn
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Imodels | 1,229 |  | 4 | 5 months ago | 44 | October 05, 2023 | 27 | mit | Jupyter Notebook | |
| Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). | ||||||||||
| Shallowlearn | 196 | 7 years ago | 5 | December 30, 2016 | 17 | lgpl-3.0 | Python | |||
| An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn. | ||||||||||
| Iohmm | 140 | a year ago | 5 | February 05, 2021 | 9 | bsd-3-clause | Python | |||
| Input Output Hidden Markov Model (IOHMM) in Python | ||||||||||
| Interactive_machine_learning | 132 | 5 years ago | 1 | mit | Jupyter Notebook | |||||
| IPython widgets, interactive plots, interactive machine learning | ||||||||||
| Python Machine Learning Book 3rd Edition | 93 | 6 months ago | 2 | mit | Jupyter Notebook | |||||
| <머신 러닝 교과서 3판>의 코드 저장소 | ||||||||||
| Machine Learning With Scikit Learn Python 3.x | 27 | 3 years ago | mit | Jupyter Notebook | ||||||
| In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page). | ||||||||||
| Wisconsin Breast Cancer | 27 | 3 years ago | apache-2.0 | Python | ||||||
| [ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset | ||||||||||
| Kaio Machine Learning Human Face Detection | 15 | 6 years ago | mit | Jupyter Notebook | ||||||
| Machine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions | ||||||||||
| Predicting Baseball Statistics | 14 | 5 months ago | Jupyter Notebook | |||||||
| Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn | ||||||||||
| Machine Learning Course | 13 | 4 years ago | ||||||||
| Machine Learning Course @ Santa Clara University | ||||||||||
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