Machine Learning has incorporated itself into our everyday lives, without us even noticing. The world around us is powered by Machine Learning algorithms. This includes surge pricing at Uber, fraud detection at different financial institutions, product recommendations at Amazon, as well as the content used by Facebook, Instagram, LinkedIn, and Twitter. These are just a few examples of how machine learning impacts our daily lives.
According to Mark Cuban, “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.”
So, if you want to get into the field, now is the right time to go for Machine Learning training. Here are the advantages you will be able to enjoy a career in the field of Machine Learning:
1.Better growth and career opportunities
According to a report by TMR notes, Machine Learning as a Service or MLaaS is predicted to grow up to $19.9 billion by the end of the year 2025. The industry was just worth $1.07 billion in 2016. This shows that the field is growing at a significant rate. Machine Learning is for individuals who want to take their career to the next level or want to get involved with something that has contemporary as well as global relevance. The field is used in different verticals including medicine, cybersecurity, image recognition, facial recognition, and so many more. As more and more businesses are realizing the profound impact machine learning has on business intelligence, they are choosing to make an investment. For example, Netflix announced a prize worth $1 million to anyone who could sharpen their ML algorithm and increase the accuracy by 10%.
2. Better Salaries
The best Machine Learning professionals are among the highest-paid IT professionals today. According to Glassdoor, the average annual salary of a machine learning engineer is $1,14,121 in the United States and INR 7,45,441 in India. If you decide to go for a certification program, you will learn about Machine Learning, AI, Deep Learning, NLP, Graphical Models, Reinforcement Learning, and much more. You will also have a strong foundation in Statistics and Predictive Analytics as well.
3. Lack of ML skills is causing companies to be left behind
The advent of machine learning has caused the industry to take a giant technological leap. However, not every corporation is able to catch up. Machine Learning is a huge industry. But, the truth is that there are not enough, skilled machine learning professionals for catering to the demands of the industry. Even though more and more IT professionals are entering the field, the gap between the supply and demand remains wide. You don’t need to have exceptional qualifications to work as a Machine Learning professional. What is required is a specific set of abilities and skills.
4. Data Science and Machine Learning are linked
Machine Learning is a part of Data Science. If you want to take your career to the next level, you will need to be competent in both these fields. It will allow you to analyze large volumes of data and then extract value from it to provide insights. In most organizations, data scientists and ML engineers work together. So, if you are already an ML engineer, chances are that you have already been exposed to the perspective of a Data Scientist.
Career Path for a Machine Learning Professional
The career path for a machine learning professional usually starts with Machine Learning Engineer. They are responsible for developing solutions and applications that can automate common, repetitive tasks that were previously performed by humans. These tasks are mostly based on action pairs and conditions- which can be performed efficiently by a machine, that too without any errors.
The next step after working as an ML engineer is becoming an ML architect. Such professionals are involved with developing and designing applications prototypes. Other roles that are available in the Machine Learning field are Senior Architect, ML Software Engineer, ML Data Scientist, and so on. If you are a software engineer with knowledge of core ML libraries and python, you can easily switch to the Machine Learning field. If you want to become an ML professional, here are a few other skills that you must have:
- Statistics and Probability – Most of the Machine Learning Algorithms are developed using Markov models, Bayes rules, and other probability processes. They also involve statistics like mean, median, Poisson distribution, deviation, and so on.
- System Design – ML solutions usually don’t work as standalone products. They are a part of an integrated ecosystem. Therefore, ML professionals must have a strong foundation in software design.
- ML Libraries and Algorithms – Having an understanding of models like Linear Regression, Genetic Algorithms, Boosting, and Bagging are often used by ML professionals.
- Data Modeling – Every ML practitioner should be able to estimate the dataset structure for finding patterns, correlations, and clusters. They also have to evaluate data models continuously for ensuring that they are on point. Apart from this, they should also be familiar with the process of testing data to evaluate its completeness and accuracy.
- Programming Languages – For anyone who wants to make a career in ML, learning Python is crucial. Another important technology is Apache Spark, followed by SAS.
It is important to note that the above-mentioned list is not comprehensive that you can undertake once and then be done with. It is just to get you started in the field. You always have to be on your toes and upgrade your knowledge and skills to have an upward career graph.
If you have started to pursue a career in Machine Learning, you will become a part of the digital revolution affecting every sector ranging from manufacturing to logistics, retail to healthcare, and so on. ML skills will open several different avenues for you. You will be completely in control of which industry you want to work in. This is the best time to get started in the field and take your career to the next level.
