The process of learning Machine Learning (ML) can often be challenging and confusing. A good majority of ML enthusiast start off energetic only to give up at the start and trust me, I know and I’ve been there. I have personally gone through that daunting process not once but thrice.
I started learning Machine Learning 5 years ago. Back then, good Machine Learning resources were very limited and the best places to find quality ML resources were on sites like Udacity during the days when the term “ML” was mostly coined with the term “Data Science”. After 2 or 3 ML videos, I gave up and started learning something else. I tried again 3 years later and again, I gave up.
But what changed in 2021 ? Well for starters, I gained more experience in engineering and continued to improve upon myself and incase you were wondering, 5 years ago I was 17 years old. The second and most important reason as to how I was able to dive into Machine Learning was that I had a good roadmap and today I’ll be laying out to you my roadmap for getting started with Machine Learning.
Python is the leading programming language in Machine Learning development.
ML Developers love python because of its accessibility and simple uncomplicated syntax.
Here is a great tutorial to begin with if you don’t already know Python.
Google’s Machine Learning Crash Course is by far the best crash course on ML that I have seen till date. It gets you started with basic ML principles and as well as give you a good understanding of the math involved in some of MLs core algorithms.
The crash course also teaches NumPy, Pandas and most importantly TensorFlow which is an amazing library for handling most Machine Learning related problems and packs into it all the math and algorithms needed to build a ML Model.
Optionally, after taking Google’s Machine Learning Crash Course, you can also take Coursera’s ML Course by Andrew Ng.
Although as mentioned above that math is not required to learn Machine Learning, a good understanding of how various math calculations work in ML will give you an edge in your journey learning Machine Learning.
However, you don’t need to know all the major math topics as to only a mindful of math topics is used in Machine Learning and a few examples are :
Probability and Statistics make up a huge chunk of the Machine Learning math in most ML libraries that are used for creating models, so those topics are where you would be focusing most of your time on.
Khan Academy has a great YouTube tutorial playlist for these particular math topics.
Notebooks like the Jupyter Notebook are interactive multi-purpose tools that not only let you write and execute code but, at the same time, analyze intermediate results to gain insights while working on a project. Simply put, they are amazing and they speed up development.
Google Collaboratory (COLABS) is Google’s version of Jupyter’s Notebook. It creates a virtual environment in which you can write and test your code and the best part is that it is fully online and you don’t have to worry about GPU’s and Hardware specs.
Learning how to use Notebooks is a great asset to have in your journey as you learn ML.
Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. It was created and is maintained by Google and is a great platform for learning Machine Learning by practice.
Kaggle is home to various and popular real world scenario projects that you can work on to improve your ML skills and equip you with enough knowledge to be able to build and understand real world datasets and models on your own.
Learning Machine Learning has never been more easier in any other period in time except for now. There are hundreds if not thousands of learning materials out there which often just clouds your head and can make you give up prematurely but with a good roadmap, you can learn ML faster and most importantly, you can learn ML the right way!
Thank you for reading, and I hope this roadmap I have set out for you helps you navigate the cloudy path of learning Machine Learning.
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