Things I Wish I Knew Before I Started With Machine Learning

Looking back at my journey so far, here are few things that would have made my life easier.

It is not easy
You’re gonna spend a lot of time searching through resources. Just running an algorithm from caret library isn’t enough. You have to understand the maths behind it and it takes a lot of careful reading and mental efforts. Any resource that tells you “you don’t need to learn maths” is probably not worth the efforts.

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Moving From Python to R

Its been 3 months since I started with machine learning. Coming from a web development background, my first choice of tool for performing data analysis was python. Primary reason why I chose python was the familiarity with language. But soon enough I started to realize its not an optimal environment for data analytics. Because my programming background heavily influenced my workflow, I kept shuffling across multiple tools; terminal for running the script, sublime for writing python code and matplotlib chart viewers for visualizing the data. I wanted a better workflow to work with data. A workflow that will allow me to easily try out different visualizations, navigate through data without writing much code and keep a history of operations I performed.

Since I knew R is the most used programming language for data science, I decided to give it a try. I downloaded R distribution from the website. I started by executing some commands on the terminal, initial impressions were good. However, I could not find more efficient way to explore the data than earlier. Then I came across RStudio. RStudio is awesome. It allowed me pin down all points of interest within a single window. It shows me plots, help for R functions, data explorer and simplitstic editor for writing code. I loved the way I could execute commands on RConsole and see the output immediately on the plots tab. This allowed me to keep iterating quickly. Later, I started learning more about the language. RStudio also shows the help for functions in R, it has been the most useful feature for me. I’m discovering more and more as I learn about the language and ecosystem of packages. But as of now, R is definitely a way to go for my machine learning explorations.