Hey all, recently we had a motivating session about Machine Learning and Artificial Intelligence. Although the session might have been too much for someone with no prior experience in this field, its never too late to get started with something new and exciting. Machine Learning has taken a great leap in terms of research and development since the last decade. This has been made possible due to a thriving community and an abundance of applications of such principles to daily life applications.
In the session I gave some evident examples of how this technology has been creating applications that are both flexible and smart (sometimes to a level that it might get creepy). In this post, I will try delineating some points as to why you can try experimenting with this new and burgeoning field and how it can be a great source for building up your profile as you move through your graduation.
1. Machine Learning has been applied to a great number of areas not only limited to unconventional interests such as Economics, Biology, Sports and others. In a nutshell, the potential for research and development is immense in this field. If you wish to write an academic paper or create projects for showcasing your skills, ML can give you a path that is relatively easy and attractive.
2. If you have a genuine interest in building scalable applications that can take advantage of data being generated around, then learning machine learning and applying its principles into your application can make it way more robust than any other conventional programming techniques.
3. ML is really hot right now! A lot of companies are shifting their product base to take advantage of machine learning. If you see around, it is not hard to come across something that is using machine learning. Be it recommendation systems (Amazon Inc.), Speech Recognition Systems and Smart Assistants (Alexa, Siri, Google Assistant), AI and ML is penetrating its way into the market at a unbelievable scale.
I started ML with zero experience! I just had basic knowledge of a programming language (Python) and wanted to try something new. Nonetheless, it is a great idea to brush up some elementary concepts so that the eventual ride can be a whole lot perceptive!
1. Linear Algebra: A little bit of linear algebra concepts such as Matrix Multiplication and Addition are required. You might have already been through these mathematical fundamentals during senior school. Kudos!
2. Probability Theory: Probability theory might be helpful when trying to get intuition regarding distributions of outputs of machine learning algorithms. I bet the high school mathematics will work just fine!
3. Programming Language (Optional): If you have a prior knowledge of any programming language (Python or Ruby might be great) then you will be able to apply the concepts learned by yourself. I would rather say that one should not focus on the language and more on the algorithm and how it works. So if you don't have any programming language knowledge there is no need to fret. On the contrary maybe you are better off! (*smiles*)
Learning machine learning is more of an experimentation procedure with nothing absolute and that's what makes it beautiful. It would be great if you can also have a little bit of patience when the going gets tough!
Resources
There are no prerequisites for this course and its a must if you wish to get started with Machine Learning. Try doing programming assignments and complete the course for best effects. The programming assignments are in MATLAB (Easy as crap)
If you are already done with Stanford Coursera course then you might want to learn some basic tools of Data Science for Data Visualisation and Data Handling. This course might be a great way to continue the streak!
You know Python + Numpy + Pandas + Machine Learning! Now get your hands dirty with some high level libraries available in Python to create interesting projects!
Kaggle is a platform where competitions for Data Science are held regularly. Participate to get your skills tested!
Hi! I am an Undergrad @ Department Of Computer Engineering, currently pursuing BTech (4th Year). My interests include Machine Learning and Data Analytics.
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