A 6 weeks Machine Learning crash course using python
Hey Geeks!
Welcome to the machine learning crash course. AI & ML are hot topics and many are trying to get involved in this domain. Attending online courses like Andrew ng's or Emily & Carlos from university of Washington are good thing but it'll take you a very long time to complete it or you may quite in the middle of the way, and that something we don't want it to happen. The online courses are stuffed with many theoretical explanations that is good to understand but you may not need any of it during your work on your model. I tried both ways, the long one where I attended Emily & Carlos's course and it was pretty good. It was made of 6 modules but they reduce it to 4 where most of the people doesn't make it to the end. Also, I did a very short course, only 6 days, and at the end of the course all of the students were able to solve problems using machine learning algorithms.
I'm trying to provide something in the middle. Some theroy will not kill you and we don't need to reinvent the wheel by building the same models from the scratch.
In this course we'll use python as the main programming language. If you don't know python that’s ok you can start with this free online course that introduce you to what you need to know as a data scientist.
If you have background in python, You may want to start seting up your environment. Please setup and install Anaconda. If you run out of space you can work with Conda. If you choose to work with Conda, please make sure to install NumPy, Pandas, Matplotlib, scikit-learn and seaborn
The course is a 6 weeks length. We'll learn the following in each week:
| Week | Content |
|---|---|
| Week 1 | Intro to ML, Pandas and numpy |
| Week 2 | Intro to Scikit-learn ML process |
| Week 3 | Pipelines |
| Week 4 | Features extraction & Time series |
| Week 5 | NLP |
| Week 6 | Neural Networks and computer vision |
One more step before starting, make sure you have an account on Kaggle where we'll use it a lot and you'll submit your work to kaggle.
I'll keep updating the repo content, so please keep watching.
