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Read the tutorials and lecture notes entirety available online online at https://tejshahi.github.io/beginner-machine-learning-course/
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Run the code using the Jupyter notebooks available in this repository's notebooks directory.
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Launch executable versions of these notebooks using Google Colab: [
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Regester here (www.mountech.com.np) for live training class.
All notebooks are written and tested with Python 3.8, though other python version should work fine but not guranted.
The course covers the core packages that includes: Python, NumPy, Pandas, Matplotlib, Scikit-Learn, and [seaborn] (https://seaborn.pydata.org/) packages. There is no pre-assumption for this course. Anyone can start with this. However if you need a introduction to the python itself, see the free companion project, A Whirlwind Tour of Python: it's a fast-paced introduction to the Python language aimed at researchers and scientists.
The packages I used to run the code in the book are listed in requirements.txt (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use). To install the requirements using conda, run the following at the command-line:
$ conda install --file requirements.txt
To create a stand-alone environment named PDSH with Python 3.5 and all the required package versions, run the following:
$ conda create -n PDSH python=3.5 --file requirements.txt
You can read more about using conda environments in the Managing Environments section of the conda documentation.
The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.
The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.