When I started studying “Machine Learning,” I was quite confused about my future, but now, I’m confident enough to clear your doubts about “Machine Learning as a Career.”

Right now, Machine Learning is halfway down to peak, and even at this stage, Machine Learning professionals and experts make a decent amount of money each year. The average salary of a Machine Learning expert varies from $112k to $115k. So, this makes Machine Learning a great career.

7 Reasons Why Machine Learning is a Great Career

#1. Attractive Payscale

Before stepping into any career, it is important to know the money you’ll earn in that career. Since we are talking about Machine Learning here, on average, you’ll make somewhere between $112k to $115k per year as a fresher. Later your pay scale may go up to $300k once you become an experienced Machine Learning expert.

Different Countries have different Pay Scales for ML experts. According to Payscale.com, the US has the following Pay Scales for different regions:

Region	Payscale (Per Annum)
San Francisco, CA	$198,520
Cupertino, CA	$188,910
Cambridge, CA	$170,851
New York, NY	$169,305
Austin, TX	$162,512
Santa Calara, CA	$155,066

It is estimated that every five years, your salary will be doubled as you accumulate experience in this field. This calculation is applicable in the US and other countries. The only difference is the starting package!

Interesting Article to Read: This is How much Data Analysts Make (By Industry, Experience and by Country)

#2. Endless Opportunities

Machine Learning is a newly emerged technology, and we humans are still learning about its potential. Machine Learning’s implementation is only limited to major industries, but as per experts, Machine Learning will be a revolutionary technology that will be implemented in every sector.

According to Dresner Advisory Services, On average, Machine Learning can be implemented in 60% of the projects in almost every industry.

Recently, the European Union presented a “Whitepaper of AI“. All the European countries aim to invest 20 billion Euros every year for the coming ten years in Artificial Intelligence and Machine Learning to boost its implementation in the industries.

“Machine Learning has created over 2 million jobs in 2020”, as per Mygreatlearning.

Though ML and AI will replace over 44 million human workers in different industries by 2030

(as per the study conducted by McKinsey ), At the same time, Machine Learning and Artificial Intelligence will create millions of job opportunities in the Health care and IT sectors.

General Manager of Mixed Reality Education at Microsoft, Dan Ayoub, said: “AI and ML will be the great transformers, improving the efficiency of many sectors and enabling the creation of higher-value services that can lead to overall economic growth“- Source.

Highly Recommended Article: 7 Key Differences between Machine Learning and AI

According to Indeed, Jobs for Machine Learning Engineer had a growth of 344% from 2015 to 2019.

Now you can understand that Machine Learning is a pool of opportunities; you have to be prepared enough to dive deep inside.

#3. Exponential Growth

Machine Learning is a field that doesn’t have a saturation point, or at least we humans can’t predict one yet. Every University/Organization is trying to figure out the areas where Machine Learning can be implemented in the coming years.

According to BusinessWire, “The Machine Learning Industry is going to grow by $11.16 billion from 2020 to 2024.” Apart from this, In 2020 itself, the growth rate of Machine Learning in the real-time world was 38%.

According to Forbes, “the Machine Learning Industry was valued at $1.58b in 2017, and it is expected to grow up to $20.83b by 2024 with a compound annual growth rate (CAGR) of 44.06%.”

According to MRFR analysis, “the Machine Learning Industry is currently standing at $7.3b, and it is expected to grow up to $30.6b with a CAGR of 43%.”

Now, if you closely look at these predictions, you will find that Machine Learning is a field that has a golden future, and if you are associated with it, you will get an ample amount of opportunities.

#4. Vast Work Areas

One of the significant problems that IT industry professionals face is their limited work areas. For instance, if you are an Angular developer, your work is limited to specific fields only. Whereas a Machine Learning professional always has a couple of options on their work plate to pick. An ML expert can go into any of the following fields:

Data Scientist
Artificial Intelligence
NLP Scientist
Business Intelligence Expert

Though, before making a switch, you need to make sure that you have a complete understanding of the field and are familiar with the different technologies involved in that field.

It is suggested to go through an introductory course about the field you plan to step in. You’ll get hundreds of courses on platforms like Edx, Udemy or Coursera.

#5. Multiple Streams

This is something related to the previous reason. Here multiple streams represent that being a Machine Learning expert, you can switch between different niches anytime you want. Unlike other programming languages or software development technologies, you don’t need to learn or master some new skills to enter a new ML niche. Apart from this, whatever niche you pick, the industry will welcome you with open hearts.

For instance, if you are working as a BI expert and want to switch to a Data scientist company, you can easily do it because most of BI’s elements are similar to Data Science. Every work ground is identical to others, though there are some differences.

#6. Flexible Work Hours

In the IT industry, “work hours” is a topic of endless debate. Two colleagues can argue on it for hours. If you are looking for a work-life balance and still want to work in the IT industry, you must try your hands on Machine Learning.

Working with Machine Learning technologies is less tiring than other software development technologies or programming languages. The sole reason behind this statement is the “work flexibility” of the Machine learning Industry.

However, as an ML expert, you have to follow the traditional 9-5 schedule, but since Machine Learning is a field that demands a lot of study in every project. Therefore, half of the time, you’ll be busy learning something new and exciting. This makes Machine Learning an exciting and less stressful career.

Read this Medium Blog to understand “A day in the life of a Machine Learning Engineer“.

Machine Learning is implemented primarily using Python. If you are familiar with Python, it is a modern programming language with vast (immense) library support. Most complex tasks can be done in just a few code statements using those libraries. Therefore Python makes working with Machine Learning easy and hustle-free.

Apart from Python, Java is the second most used programming language in Machine Learning. But since Python is modern, it supports hundreds of libraries and is more efficient in comparison with Java, it is preferred in most situations.

If you are looking forward to make a career in Machine Learning, it is suggested to learn at least the basics of Java and master Python programming.

#7. Futuristic Approach

According to a French computer scientist Yann Lecun “Deep Learning and Machine Learning are going to change a lot in the next five years.” Even Google Officials in a press conference stated that “Machine Learning is the Future.”

This is what industry experts think about the future of Machine Learning.

Another interesting statement that came out recently is from Matt Reaney (CEO of BigCloud), “Quantum computing is going to play a huge part in the future of machine learning. Integration of quantum computing into machine learning will transform the field as we’ll see faster processing, accelerated learning and increased capabilities.”

Now, if you process these three statements, you will conclude that everyone is looking forward to Machine Learning; from farmers to the stock market, every sector will evolve with the rise of Machine Learning technology.

If you are further interested in reading about Machine Learning’s future in different sectors, you can go through this content.

The rise that we are expecting in the Machine Learning field in terms of opportunities is quite revolutionary.

Machine Learning Courses

To Master Machine learning, you need to have some guidance from an industry expert. In this section, I have listed 3 Machine Learning Courses offered by industry experts and trusted by thousands of students worldwide.

Machine Learning

Your instructor in this course will be Andrew Ng, who is the top instructor on Coursera. In this course, over 3 million students have enrolled so far. On Coursera, this course has a rating of 4.9 out of 5. If you are looking for something that can quickly teach Machine Learning basics, this Coursera course is for you. It’s a 60 hours long video lecture course and includes some real-time practice sessions.

Machine Learning with Python: A Practical Introduction

If you have a technical background and know the basics of programming, then Machine Learning with Python is the best course available for you. In this course, IBM will be your instructor. This course is available on edx.org and is free of cost. Machine learning is a technology that needs practical sessions. This course will give you enough opportunities to try your hands on real-time practicals. It is a five-week-long video course in which you will be taught everything about Machine Learning from scratch.

Machine Learning Fundamentals

Programming is a skill where theoretical knowledge is as important as practical knowledge. To be a successful machine learning professional, you need to have a sturdy grip on ML Basics. Machine Learning Fundamentals will help you grasp every minute element of Machine Learning. Once you have completed this course, it won’t be tough for you to work or instruct others in Machine Learning projects. It’s a ten weeks long video course available on edx.org, and again it’s free of cost. You’ll get an immense amount of information about Machine learning if you complete this course wisely.

Note: All the above-listed courses are free of cost and created by Top Universities and Professionals of the world.

Machine Learning Projects

Once you have completed a few machine learning courses, all you need afterwards is practice. In programming, practice plays a vital role. To learn skills like debugging and optimized coding, you need to try your hands on several minor projects before jumping on an actual project that has someone’s expectations at stake.

Every new programmer must create a few projects before calling himself/herself a programming expert. In this section, I’ll share some of the Machine Learning Projects ideas that will enhance your coding and debugging skills.

Examples of projects are for Machine Learning beginners.

Human Activity Recognition.
Traffic predictor using traffic footages.
Wine Quality Prediction using Wine datasheet.
Social Media Sentiments Analysis
Retail Price Optimization

Conclusion

So, Is Machine Learning a Good Career?

Being a Machine Learning professional will give you hundreds of opportunities to work on different projects, and ultimately you’ll end up learning something new from every single project. On top of that, the demand for Machine Learning professionals is increasing at a tremendous pace. Therefore you won’t have problems with the pay scale. All you need to do is “keep yourself up to date with the latest Machine Learning Technologies.”

If you deliver as per the market requirements, you will never face problems with jobs and opportunities. Overall, Machine learning is one of the best careers you can have in this decade.

References

Is Machine Learning a Good Career? Here are 7 Reasons