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Exploring my first Machine Learning algorithm — Gradient descent.
Explaining the basics of Gradient Descent and how we can apply it to create a best fit line.
Over the last couple of weeks I have been teaching myself Python along with it’s computational (NumPy) and data analysis/visualization (Pandas/Matplotlib) modules to ultimately become a data scientist. I’ve been blogging this journey to provide a collection of notes I can look back on and to show that you can learn anything in this day and age. The amount of information on the internet is HUGE.
Now I approached this just as many people would — I went straight to Google and typed in “How to become a data scientist”. I found so many articles, videos and blog posts I was overwhelmed. It took a bit but I narrowed down similar features between many sources and made my own curriculum but there was one thing that puzzled me:
Why do I need machine learning to become data scientist?
I didn’t understand the importance of machine learning because I couldn’t visualize what these algorithms were doing to my data and what the computer was learning from my data.
Nonetheless I went on with my syllabus. I learned a little NumPy, Pandas, Matplotlib and then I started tackling the project I had planned which was to…
