https://github.com/madonahs/machine-learning

🌎 I created this repository for educational purposes. It will host a number of projects as part of the process .
https://github.com/madonahs/machine-learning

java linear-programming linear-regression machine-learning matrices matrix-multiplication neural-network neural-networks python r tensorflow tensorflow-experiments tensorflow-models

Last synced: 2 months ago
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🌎 I created this repository for educational purposes. It will host a number of projects as part of the process .

• Host: GitHub
• URL: https://github.com/madonahs/machine-learning
• Owner: Madonahs
• License: apache-2.0
• Created: 2018-03-18T14:20:12.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-01-22T01:28:48.000Z (almost 5 years ago)
• Last Synced: 2024-11-12T17:04:00.781Z (2 months ago)
• Topics: java, linear-programming, linear-regression, machine-learning, matrices, matrix-multiplication, neural-network, neural-networks, python, r, tensorflow, tensorflow-experiments, tensorflow-models
• Language: Python
• Homepage: https://www.madonahsyombua.com/
• Size: 2.3 MB
• Stars: 37
• Watchers: 9
• Forks: 22
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
Machine Learning 


 


       

        

        



**NOTE:

I created this repository for educational purposes. It will host a number of projects as part of the process and some exercises that we created, just purely a learning process. Not perfectly done.**



  


    

    

 

 



  *  [**Machine Learning Cheat Sheet**](https://drive.google.com/open?id=1lxb-7FWiY5KwTYehcPhn67tFu5ZHvJDH)

   *  [**Machine Learning PlayGround**](https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=3,2&seed=0.88257&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=true&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)

 *  [**Machine Learning in Laymens Terms**](https://drive.google.com/open?id=1xyBNBgOW6uR9Wzf5SiJrMHZK0alvrr3s)

 

 





## Contributing

Read  [Contributing](https://gist.github.com/PurpleBooth/b24679402957c63ec426)

## Machine learning algorithms:

#### Linear Algorithms:

* **Algorithm 1: Linear Regression**

* **Algorithm 2: Logistic Regression**

* **Algorithm 3: Linear Discriminant Analysis**

**Nonlinear Algorithms:**

* **Algorithm 4: Classification and Regression Trees**

* **Algorithm 5: Naive Bayes**

* **Algorithm 6: K-Nearest Neighbors**

* **Algorithm 7: Learning Vector Quantization**

* **Algorithm 8: Support Vector Machines**

**Ensemble Algorithms:**

* **Algorithm 9: Bagged Decision Trees and Random Forest**

* **Algorithm 10: Boosting and AdaBoost**

## License

 Copyright [2019] [Madona Syombua]

   Licensed under the Apache License, Version 2.0 (the "License");

   you may not use this file except in compliance with the License.

   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software

   distributed under the License is distributed on an "AS IS" BASIS,

   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

   See the License for the specific language governing permissions and

   limitations under the License.