Along with AI, machine learning (ML) is the fuel required to power robots. We can use ML to power programs that can be easily updated and modified to adapt to new environments and tasks in order to complete tasks quickly and efficiently.
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Reasons to Pursue ML
Machine learning is a skill for the future.
Despite the exponential growth of machine learning, there is a skills shortage in the field. If you can meet the demands of large corporations by gaining expertise in ML, you will have a secure career in a growing field.
Work on real-world problems.
Businesses in the digital age face numerous challenges that ML promises to solve. As an ML engineer, you will work on real-world problems and create solutions that will have a significant impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world problems is extremely satisfying.
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Grow and learn.
Because ML is on the rise, by getting in early, you can observe trends firsthand and continue to increase your market relevance, thereby increasing your value to your employer.
A career graph with exponential growth.
After all, is said and done, machine learning is still in its infancy. As the technology matures and advances, you will have the experience and expertise to chart an upward career trajectory and approach your ideal employers.
Create a lucrative career.
One of the main reasons why ML appears to be a lucrative career to many of us is the average salary of an ML engineer. Because the industry is expanding, this figure is likely to rise further in the coming years.
Take a detour into data science.
Machine learning skills enable you to broaden your career options. You can wear two hats if you have ML skills, one of which is that of a data scientist. Gain expertise in both fields at the same time and embark on an exciting journey filled with challenges, opportunities, and knowledge to become a valuable resource.
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How Does a Machine Learning Career Path Look?
A machine learning career typically begins with the position of machine learning engineer. Machine learning engineers create applications and solutions that automate routine tasks that were previously performed by humans. The majority of these are repetitive tasks based on conditions and action pairs that machines can perform accurately and efficiently.
Here are a few other tech areas that an ML professional may be familiar with:
Statistics and Probability
The Bayes rule, Markov models, and other probability processes serve as the foundation for many ML algorithms. Statistics include meaning, median, deviation, Poisson distribution, and so on.
System Design
ML solutions are rarely sold separately. Most of these are part of a broader technological ecosystem. As a result, it is advantageous for ML professionals to have a solid understanding of software design.
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Algorithms and Libraries for ML
Knowing models like Linear Regression, Bagging, Boosting, and Genetic Algorithms is beneficial for ML professionals.
Modeling Data
As an ML practitioner, you should be able to estimate the structure of a dataset in order to find patterns, clusters, and correlations. Continuous evaluation of data models is also required to ensure that they are accurate. Furthermore, you should be able to test the data being evaluated for accuracy and completeness.
Programming languages
Python is an essential programming language for anyone pursuing a career in machine learning. Another technology is Apache Spark, which is followed by SAS.
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