If you are new to Machine learning please check out the reference section to dive deeper in to this field.
Now a days, Machine Learning is been every where from Autonomous Driving Car to reccommendation system used by Amazon. Everybody knowingly or unknowingly is using Machine Learning. Have you ever wonder":
- How Facebook recommend you new friends? -How Siri reconize your voice?
Using machine learning softwares is fun but implementing them is more fun. It's like training a child and when you see it doing the work you become more curious about it.
This repository is dedicated to Machine learning, in this you can find implementation of various machine learning algorithms. This repository is actually a result of various online tutorial. I tried to implement algorithm as easy as possible with full detail in Read me and working code. It is suitable for beginners who want to find clear and concise example of machine learning algorithm. For readability, the tutorial includes both notebook and code with explanations. From simple numpy to sklearn and all the way through TensorFlow, you can find example in each area.
- Linear Regression(article | git | notebook)
- CO2 Emission(notebook)
- Neural Network(notebook)
- TensorFlow(notebook)
- Twitter Sentiment Analysis(notebook)
- Exercise#1: Machine Learning in Life Scicence WS 2016 - 2017
- Exercise#2: Machine Learning in Life Scicence WS 2016 - 2017
This code is written in python using Jupyter
- First setting up and how to use jupyther you can use this Link
- Introduction to Machine Learning, University Of Freiburg by Dr. Joschka Bödecker, Manuel Blum
- ML Recipies by Josh Gordon, Google Developers
- Machine learning, Andrew Ng
- Intro to Deep Learning (Udacity Nanodegree)
I would be happy to talk to you about this project and if you are interested then we can further enhance this project to.
- Amanullah Tariq
- Email: [email protected]