Using Machine Learning techniques opens the technology to a whole new host of possibilities in every domain. It has made self-learning possible for a machine, thus helping it improve from experience. A Machine Learning expert is amongst the most sought-after professionals in 2020 as ML has found its place in a variety of industries and job roles.
Learning of the Blog
- Career Paths in Machine Learning
- Step by step Guide
o Learn a Programming Language
o Take up a Machine Learning certification course
o Practice online and sharpen your concepts
o Work on Projects and Real-Life Applications
o Secure a well-paying job
If you are a Machine Learning enthusiast, starting a career in it, or even planning to get into it after years of working- here are few tips that will help you advance. So, without any further ado, let us dive in.
Career Paths in Machine Learning
In simple words, Machine Learning is a combination of high school mathematics and statistics linked with Data Visualization and Exploratory Data Analytics. The work field boasts an average salary of $146,085, with a growth rate of 344 % last year.
A person who knows Machine Learning or has an ML certification can become a:
o Machine Learning Engineer
o Data Scientist
o NLP Scientist
o Business Intelligence Developer
o Human-Centered Machine Learning designer
and other job profiles that might come up in future
Step By Step Guide
These few steps will come handy for those who are looking for a guide on Machine Learning for beginners.
1. Learn a Programming Language
For any technology, you are required to have some knowledge of programming languages. Machine Learning can be implemented using Python, R, Julia, Java, etc. Most people go for Python or R because of several inbuilt libraries, which makes your work faster. Initially, try to understand basic syntax and variables, then go for functions and, when comfortable, get hold of the OOPs concept. The last step would be to know some libraries, such as NumPy and pandas. Once you are familiar with indexing, data frames, and series, you can go for the first step of the cycle- Data Preprocessing. It is advisable to revise basic statistical tools such as probability, variance, correlation along with it as they come in handy once you start implementing ML.
2. Take up a Machine Learning Certification Course
There are many ways to go about Machine Learning – one can always self-learn, but it is advisable to take up a course offline or online because it is more structured and would be great if you are just starting. A Machine Learning course would provide you with templates that you can work on and give you material to refer later. Some of these courses are designed to test your performance before you can get a certification for Machine Learning. The recent trend is to go for a Machine Learning certificate online as it is self-paced and affordable as compared to those available offline.
3. Practice Online and Sharpen Your Concepts
Once you are done with learning the basics and implementing the use cases, try the problems available on Kaggle and Analytics Vidhya. Here you can get a variety of datasets and practice at your convenience by participating in various active and closed competitions. You can also go through solutions provided by fellow participants and learn from instructors around the globe, be a part of the data science community, and team up with genius minds to work together. To begin with, you can try basic regression and classification problems and then dive deeper into it, exploring feature detection, hyperparameter optimization, neural networks, and so on.
4.Work on Projects and Real-Life Applications
Any Kaggle task requires you to work on a dataset and learn the functioning and algorithms required for achieving the best accuracy in that case. In real life, data sets are humongous, with more than a million rows and hundreds of parameters. You can always work on datasets available on official sources and use them to your advantage to solve real-life issues, e.g., predicting the total number of COVID-19 cases, classifying an email as spam, etc. A project looks good on your resume and helps you gain insight into complex data frames and unclean data. Try to get a professor, a senior, a friend, or a colleague who shares the same passion as you to work on something that is yours.
5. Secure a Well-Paying Job
It is very important to build a portfolio and have a good Kaggle ranking. There is a high chance company will consider you for an internship/ job if you have a Kaggle ranking below 100. If you have worked for a while time in some other domain, then you can apply Machine Learning techniques to your project and see if it is beneficial enough for higher management to notice you and allow sliding into Data Science. The easiest way to look for opportunities is by using LinkedIn. Apply for openings and crack the interviews using all the knowledge you have incurred.
There are several blogs, YouTube channels, books, and courses available that can be used as a reference for Machine Learning training.
From smartphones to chatbots, demand for Machine Learning specialists is only going to increase, so it’s a great time to get in on the ground floor of a growing industry.
We hope that this article helped to enhance your knowledge about Machine Learning or helped you find the information you were looking for. Now, as you have gotten an idea of how to go about, it is time to take the first step. You can stay ahead of your competitors by knowing your strengths and working on your weaknesses. Enroll yourself in a Certified Machine Learning Expert course for a strong foundation and a rewarding career in Machine Learning.