Machine Learning Scope and Career Prospects!

 


Machine learning has become one of the hottest professions these days owing to its rising demand and attractive salary packages. It is estimated that AI field will produce 2.3 million ML jobs by 2021. There are 9.8 times more ML engineers working today than five years ago, and this development is the highest as compared to other domains. Both data science and ML fields are generating more jobs than there are candidates available, hence it is the right time to begin your machine learning training to become a part of this exciting and fastest-growing sector.

About machine learning

It is a division of AI that uses various algorithms and mathematical models to devise better solutions. The main objective of ML is to develop programs and software that can help machines learn on their own. In simpler terms, ML is a science of helping computers behave like a human. When fed the data and figures, machines form observations about real-life events and then learn autonomously over time.

Essential ML Skills you need to Develop

To build a rewarding career in this field, it is important for a coder to gain a formal education either through a CS degree or machine learning bootcamps. An ML engineer should have an in-depth knowledge of data structures, algorithms, computer architecture, statistics, and some basic mathematics. One should be acquainted with the standard ML algorithm implementation which can be understood with the help of available libraries and frameworks. Knowledge of probability techniques is also helpful while planning to begin a career in this field.

Beginning your ML Journey

Many people dread the machine learning course because of the involvement of math, but it is not that difficult as you only need to know high school calculus and algebra. You can find plenty of online prep courses that can help make your journey a lot easier and most of them are available for free. In fact, some coding camps have specifically designed curriculum to suit beginners or inexperienced people. Just like any other field, you would require at least 5-6 months to acquire the basic skills including but not limited to linear regression algorithms, neural networks, and logistic regression.

How to become an ML Engineer?

Apart from gathering some foundational knowledge of the above-mentioned concepts, there are a few steps involved in making sure that you become a proficient ML engineer and can avail the best job opportunities.
  • Learn Python: To begin your journey, you should start with learning python and gain basic programming skills.
  • Data science: It is advised to brush up your knowledge of statistics and data science before committing to machine learning full-fledged.
  • ML frameworks: At this stage, you can go ahead and start dipping your toes in ML theory and frameworks.
  • Experiment: Now you have gathered enough knowledge to start experimenting with datasets
  • Deep learning and big data: These come at a later stage once you have acquired enough knowledge of the ML libraries and frameworks. Learning these will take you to the next level and assist you in developing ML models quickly and with fewer efforts.

ML Job Roles

The most prominent ML jobs include ML engineer, data architect, data mining specialist, cybersecurity analyst, and many more.
  • ML Engineer: They are responsible for creating algorithms that help devise meaningful patterns and insights from a large set of data. ML engineers should be familiar with java, scala, python, and C++. Their main job is to classify, sort, protect, and make predictions with the given data set.
  • Data architect: They should be familiar with MapReduce, Hive, Hadoop, data streaming, NoSQL, MongoDB, etc. Data architects are supposed to develop, test, and maintain data management systems in order to contribute to the data analysis process.
  • Data scientist: They are experts in SAS, R, SQL, Hive, Spark, and MatLab. They utilize coding to analyze a huge set of unstructured data so as to find valuable insights to plan future strategies.
  • Data analyst: Data analysts should be familiar with data storing systems, data warehousing, Hadoop based analytics among other things to manage the flow of data. Having a background in statistics, ML, programming, and math is quite helpful. Their key responsibility is to deploy algorithms, recognize risk, prune data, and solve coding problems.

Conclusion

Now that you have understood what it takes to enter the field, you need to ensure that you are hired by a top machine learning company. For that, you need to enroll with a reliable ML bootcamp to advance your journey. SynergisticIT is a leading camp that provides in-depth training to prepare students for a successful career in this domain. You get an opportunity to gain practical knowledge by working on real-world problems. They cover all training modules from beginners to advance and help fulfill your professional aspirations quickly.

Location: United States

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