Machine learning(ML) is an application or subset of Artificial Intelligence (AI) that allows machines to learn from data without being programmed explicitly. It is the process through which an AI learns. While AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior.
With machine learning, algorithms use a set of training data to enable computers to learn to do something they are not programmed to do. Machine learning provides us with technology to augment our human capabilities.
In 2020, a machine learning fresher can get paid anywhere between Rs. 4,50,000 – 8,90,000. As your experience in the field increases, the salary package also increases and can range somewhere Rs. 15,00,000 – 50,00,000 per year.
The experts predict that the scope of machine learning is huge and it will create close to 2.3 million jobs by 2020. However, it is also forecasted that this technology will wipe out over 1.7 million jobs, resulting in about half a million new jobs worldwide.
Machine learning is used in almost every industry, from entertainment to transportation, yet we have a massive need for qualified, skilled professionals. The future scope of machine learning is almost endless. (we have just scratched the surface of this subject)
Companies like Google, Netflix, Uber, Lyft, Boeing, Facebook, Snapchat, Amazon, Apple, and a lot more, know the power of ML & AI and leverage it on a daily basis to perform various important tasks.
Although Facebook or Google might not be hiring you anytime soon in India (if you are a fresher), big names like Fractal Analytics, Deloitte, IBM, Accenture, Amazon, LinkedIn, Citrix, Flipkart, and Myntra, recruit for various ML positions including Machine Learning Engineer, Machine Learning Analyst, Data Scientist, NLP Data Scientist, Data Analyst, Research Engineer, to name a few.
Now that you know what ML is & how it’s being used… Let’s get to the main subject of this post…
How can you become a Machine Learning Engineer?
1. Understand what Machine Learning is
Before you start building a career in any field, you need to gain as much information about the field as possible, the same goes for machine learning. You need to first understand what AI is, then you can start diving deeper into what machine learning is, and then it’s subcategories.
You need to do as much homework as possible because you at least need to get a good idea of the whole ecosystem(i.e. artificial intelligence) before you dive into the finer details (i.e. machine learning, deep learning, etc). Now, you don’t need to know everything, but at least try to gain a general idea. It will aid you in the long term.
It’s going to take some time for sure, but it’s going to be worth it. It’s true for any subject really. Let’s take an example, you can’t learn the English language before learning about the alphabet, right?
In the case of machine learning, you need to have a good understanding of Statistics, Probability, Linear Algebra, Calculus, and Programming Languages (we’ll discuss that in the next point).
Don’t worry if you don’t have these prerequisites. There is no limit to the human learning potential and you can learn anything if you have the drive to.
2. Learn Basic Coding Languages
You don’t have to be a programming genius to establish a successful career in machine learning. For a smooth machine learning journey, it’s necessary to choose the appropriate coding language right from the start, as this will determine your future. You must think strategically and prioritize correctly. It’s quite sufficient to master one coding language for building a career in machine learning, and Python or R is the perfect choice for beginners.
After learning a programming language, you can dive into machine learning libraries. Many data scientists recommend Scikit-learn and TensorFlow as some popular options.
3. Practice on Datasets
If you are interested in practicing machine learning, you need datasets on which to practice.
This problem can stop you dead.
- Which dataset should you use?
- Should you collect your own or use one off the shelf?
- Which one and why?
For beginners, you can get everything you need and more in terms of datasets to practice on from the UCI Machine Learning Repository.
The UCI Machine Learning Repository is a database of machine learning problems that you can access for free.
It is hosted and maintained by the Center for Machine Learning and Intelligent Systems at the University of California, Irvine.
4. Build a Portfolio
If you are just starting out as a beginner in machine learning, a machine learning portfolio can keep you on track and demonstrate your skills. Your machine learning portfolio will be a collection of completed independent projects, each of which uses machine learning in some way. The portfolio presents the collection of projects and allows the review of individual projects.
Consider using a public source code repository such as GitHub or BitBucket that naturally list your public projects. These sites encourage you to provide a readme file at the root of each project that describes what the project is all about. Use this feature to clearly describe the purpose and findings for each project. Don’t be afraid to include images, graphs, videos, and links.
This portfolio of completed projects can be used as an indicator of specific skills, ability to communicate, and a demonstration of drive and passion for the subject.
Bonus: Be Curious
Machine learning and AI are modern things that will only continue to evolve in the future, so having a healthy sense of curiosity and love of learning is essential to keep learning new technologies and what goes with them.
Go online, read about new frameworks, read new articles, take advantage of online courses, and keep developing your skills. This is one of the most important steps if you want to be successful as someone in the machine learning career path.
The machine learning job opportunities are ample and they are only going to increase more over the years.
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