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A Machine Learning certification is one of the best options for building a successful career in technology. Not only is the skill set in great demand at the moment, but the need for machine learning engineers to operate ML tools and technologies will rise significantly in the next few years.
Let’s look at a few statistics to understand the importance of machine learning and its impact on the world.
- Currently, there are more than 1,33,000 machine learning jobs in the US on LinkedIn.
- When it comes to the adoption of machine learning technologies, North America is at the forefront. Almost 80% of the continent is familiar with machine learning technologies.
- Business executives of 15 industries in more than ten countries use machine learning tools as a critical business component.
- More industries are readily investing in ML, especially the banking, IT, and manufacturing sectors. The ML budget in these industries has risen by 25%.
- Industries are keen on adopting machine learning technologies for security purposes, business analytics, increasing revenue, and performance analysis. More than 4400 companies worldwide have invested over $3.1 billion in ML.
- Moreover, in 2019, machine learning was ranked as the best job in the US by Indeed, with an average base salary of $146,085 per annum. In addition, there was a massive rise of 344% in machine learning jobs.
The above statistics help us conclude that machine learning is a growing field with a vast scope and plenty of career opportunities for skilled candidates.
If you are interested in machine learning and wish to build a career in this field, you can follow the below-given steps.
Step 1: Study machine learning, its concepts, uses, impact, and the various ML technologies. You can read blogs, articles, or research papers on machine learning.
Step 2: If you are a recent graduate, you can pursue a master's degree in machine learning. For professionals, it is best to enroll in online machine learning courses or machine learning certification courses.
Step 3: Engage in projects, deploy your own projects or join an internship to gain relevant experience.
Step 4: Once you acquire relevant skills and gain significant experience, you can add all of them to your CV and start applying to machine learning jobs.
Machine learning overlaps data science and artificial intelligence technologies. Therefore, acquiring a broad skill-set will give you an added advantage, making you eligible for most machine learning jobs. Here are a few of the most in-demand ML skills:
- Big Data Analytics
- Hadoop Software
- Problem-Solving Skills
- Critical Thinking Skills
- Neural Network Architecture
- Signal Processing Techniques
- Data Modeling
In 2020, the global ML market size was $7.3 billion. By 2024, it is expected that the machine learning market size will grow at a CAGR of 43% and reach $30.6 billion. And by 2027, the global machine learning market is projected to reach $117 billion.
In the last few years, machine learning tools have received substantial funding in multiple sectors worldwide:
- ML applications have received $28 billion in funding
- Smart robots, computer vision platforms, and language processing tools have received around $7 billion in funding
Machine Learning technologies have a vast target audience including cloud service providers, network operators, data professionals, IoT manufacturers, training providers, and more. Therefore, there is a wide array of career opportunities in Machine Learning. Some of the most popular jobs in this sector are:
1. Machine Learning Engineer The primary job of a machine learning engineer is to design and develop ML platforms, run software tests and execute ML algorithms. They also perform in-depth research on various ML algorithms. ML engineers are master statistical analysts.
Salary: The average salary of a Machine Learning Engineer in the US is $140,308 annually.
2. Data Scientist A lot of companies hire data scientists in machine learning to deploy data models. Data scientists juggle colossal amounts of structured and unstructured data, aiming to find meaningful patterns hidden within. They work closely with data analysts and BI developers to transform these insights into business-ready information.
Salary: The average salary of a Data Scientist in the US is $117,168 annually.
3. Cloud Architect Machine Learning is an integral part of cloud solutions. Therefore, cloud architects must possess relevant ML skills. The role of a cloud architect entails building a cloud computing strategy, both front-end, and back-end development, and managing cloud apps.
Salary: The average salary of a Cloud Architect is $128,418 annually.