Intro to Machine Learning in Python: Part I

Intro to Machine Learning in Python: Part I

In this post I mainly talked about my first day in Machine Learning primarily working with Linear Regression and analyzing your data for getting ready to fit it.

After messing around with really getting to know the in’s and outs of data frame management and other sides of data science in Python I have been reluctant to get into Machine Learning with the worry of not having the time I would like to commit to it and get as good as I would like. Like everything sometimes you just gotta do it. So here we go. Starting Point Where I am starting is Supervised learning, which basically means there is known input and outputs and you are just modifying the parameters of your model to predict future outcomes. -An example of this would be Positive vs. Negative movie reviews I am doing doing this work in Jupyter with the library scikit-learn in Python which has algorithms already in it, which makes it much easier to fit models, split test and training data etc.

linear-regression python data-science machine-learning

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