Machine Learning Scope and Career Prospects!

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SynergisticIt
The best programmers in the bay areaPeriod!
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• 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.

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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.
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Essential ML Skills you need to Develop
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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.
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Beginning your ML Journey
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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.
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How to become an ML Engineer?
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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.

1. Learn Python To begin your journey, you should
   start with learning python and gain basic
   programming skills.
2. Data science It is advised to brush up your
   knowledge of statistics and data science before
   committing to machine learning full-fledged.
3. ML frameworks At this stage, you can go ahead
   and start dipping your toes in ML theory and
   frameworks.
4. Experiment Now you have gathered enough
   knowledge to start experimenting with datasets
5. 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.

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ML Job Roles
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The most prominent ML jobs include ML engineer,
data architect, data mining specialist,
cybersecurity analyst, and many more.

1. 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.
2. 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.
3. 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.
4. 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.

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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.
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