Let’s assume that computers can learn something new without explicitly programmed or without any human interference. Isn’t this sound interesting? So, let’s talk about how this could be possible. This is where the concept of machine learning comes into the frame.
It is the application of Artificial Intelligence(AI) which gives computers the ability to learn itself by data stored, observations made, and examples. The computer gets the idea of how to react by using this data. Machine learning aims to make computers more self-dependent so that they can learn themselves.
Now what you have to learn to make your computer smart enough to learn itself.so, the top 10 languages for machine learning are
- Java Script
ML is a growing area of AI and there are a lot of languages which support the ML libraries and frameworks, but still, python is one of the most chosen and learned language for ML followed by C++, Java, and others.
This is all about which language you should use or prefer to learn for this purpose. Now if you are a beginner then one of the most important questions is how to learn this concept? You don’t have to pay a large sum of money for this, it’s is not mandatory that you have to have a good and prior knowledge of any above-mentioned programming language. You can simply learn them anytime so if you are a fresher and an enthusiast of learning ML, let’s begin.
First of all, don’t confuse this with data science, AI, predictive analysis, etc. although many concepts may overlap they are not the same.
And trust me guys the self-starter way of learning this is doing this. The companies don’t care about the proofs all they want to know how you can turn their data into gold. So instead of spending a lot of time in textbooks and theory and ultimately get frustrated and start considering this a very hard to learn the topic. Start switching between theory and practical, make projects, do experiments. You will surely have more fun and have something good for presenting on your portfolio.
In a nutshell, the self-starter way is better, practical, and faster.
The four steps to learn machine learning are:
- Prerequisites -Build a foundation of statistics, programming, and a bit of math.
- Sponge mode-Immerse yourself in the essential theory behind ML.
- Targeted Practice-Use ML packages to practice the 9 essential topics.
- ML projects-Dive deeper into interesting domains with larger projects.
You should definitely forge these aspects to start your learning journey but here it is just a brief way of how to learn and from where so I have not encompassed these topics as a whole here but once you start exploring you would surely get to know about them.
Now being a beginner it’s very easy to distract from your goals and you might think to drop the idea to learn in this lockdown so the tip which I would like to share is to nip the idea of giving up in the bud and be keen as mustard to explore this.
Please learn to walk before you run. Try to get focused on the core concepts first so don’t get fascinated by the advanced concepts. The advanced topics will get much easier to learn once you master the core ones.
Seek different perspectives. The way a statistician explains an algorithm will be different from the way a computer scientist explains it. Seek different explanations of the same topic.
And the most important try to alternate between practice and theory. And Don’t believe the hype. Machine learning is not what the movies portray as artificial intelligence. It’s a powerful tool, but you should approach problems with rationality and an open mind. ML should just be one tool in your arsenal!
Here is a rundown of some resources from where you can learn ML:
- CS50’s Introduction to Artificial Intelligence with Python.
- Python programming tutorials by Socratica.
- Google’s machine learning crash course.
- ML and Big Data Analytics course.
- Machine learning course from Stanford.
- Elements of AI.
- Machine learning with Python
So, all the best for your learning journey guys. Hope you guys enjoyed it!