How newcomers can break into Machine Learning and succeed to understand the question of how to become machine learning expert. This goes something like this:
- Enroll in a combination of math classes. Typically, Probability and Statistics, Calculus, Linear Algebra, and Logic are used.
- Identify one or two personal projects that pique your interest. A good piece of advice will suggest creating an end-to-end pipeline that includes everything from data collection to analysis and report generation.
- Look for online tutorials/documentation to learn about the implementations.
- Take advantage of your machine learning expertise.
What should you do if you have little experience?
Reading papers will not be immediately useful. They will have a lot of keywords that you are unfamiliar with. You won’t be familiar with half of the techniques they employ. However, it will get you in the habit of looking things up and learning new things. It will teach you how to become acquainted with complex problems. Paper by paper, you will notice that your knowledge is expanding. And this will assist you in dealing with the issue that would arise if you concentrated on projects.
What are the flaws?
You must be able to comprehend the various stages of the pipeline. Tutorials and courses are extremely well-structured. They can give you advice, but they rarely give you an idea of all the different ways your solution could have been built. If you read about different teams and conduct research on similar problems, you will not find this flaw. You will be exposed to a variety of ideas and points of view. It will also provide you with access to other members of the community. You can discuss the findings with knowledgeable professionals.
In a nutshell
Experimenting with research will show you how many different ways there are to solve a problem. It will introduce you to new ideas and concepts. In a nutshell, it’s a hack. It will allow you to get involved in ML in the same way that people who work on it full-time do. It will allow you to learn about ML in the same way that people learn about it on the job (by discovering and testing new ideas) without having to put in the hours of trial and error yourself.
What to Do Next?
Spend about 2–4 hours per week researching new ML research (through one of the channels, or just following publications). Make sure you understand the papers well enough to be able to explain what they do at a high level. Even at the low end, 2 papers per week equals 8 papers per month. This will compound exponentially (compounding returns do amazing things). Use the internet to communicate with others. It’s a wonderful thing, and you can make the most of it.
Create a simple version of the cool project for the technical side. This is simply to familiarise you with the technology stack and implementation. Make certain that you are well-versed in the technologies. Also, use your research findings to experiment with new additions to this project. And soon, you’ll be transitioning smoothly into ML Mastery, with recommendations like this and get to the top. Click here to know how to become a full-stack developer in India