Why Do Programmers Find The Machine Learning Path Hard?

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Why Do Programmers Find The Machine Learning Path
Hard?
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(No Transcript)
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• Machine learning is the buzz these days. If we
  look around, it is everywhere, from our daily use
  of mobile apps to autonomous vehicles. And at the
  rate the advancements are happening, it is safe
  to assume that the growth isnt going to slow
  down in the coming years either. All of these
  factors have put additional pressure on an
  average programmer to learn the skills to stay
  viable in the market.

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• Now, there is a sudden rush in the sector to
  become the best machine learning programmer.
  Despite all this development, it is not easy for
  every coder to venture onto this path with the
  required confidence and skills, hence they face
  many challenges.Here are some of the obstacles
  that programmers face and how they can overcome
  them

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The math connection
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• Not everybody is brave enough to embrace math, it
  is a subject that still scares a lot of people.
  When we talk about the daily functions of an
  average programmer, it does not involve the use
  of a lot of math but to master ML, it is
  mandatory to be familiar with it. To be specific
  statistics, probability, and linear algebra are
  what you need to know. So start revising your
  high school math.

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Data analysis

• Day 1

• The second most dreaded thing about this field is
  the analysis of data. The ability to analyze data
  and turn it into useful insights is the core duty
  of anyone working in the machine learning field
  but not every developer has a knack to do it.

• Day 2

• Cleansing, organizing, and finding missing data
  is a difficult task and hence not many developers
  are keen on becoming an ML programmer. So to
  begin, you need to develop a power of
  visualization before you jump into the data
  analysis process.

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The debate of Python vs. R

• The best machine learning programmer not only
  knows how to carry out data analysis but has a
  strong foundation of one of the supporting
  programming languages Python, R, or Julia. But
  coders are often stuck in the debate of which one
  to learn first in order to ensure a smooth
  learning process. The choice becomes even more
  difficult for developers who dont have any idea
  about the field. Python is still a favored
  language as its libraries and frameworks help
  develop ML algorithms easily but R is also
  preferred by another group of traditional
  statisticians. Julia is gaining popularity but
  python seems to be enjoying a top spot.

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Diversity of frameworks

• Even if you are a good programmer and have decent
  coding skills, one of the challenges you will
  face is to choose the right framework to figure
  out an ML problem.

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There are plenty of frameworks available these
days that apply differently to different
situations and your success will depend on making
the right choice. Out of all the libraries
available, NumPy, Pandas, Caffe2, Microsoft
Cognitive Toolkit, Apache MXNet are the main
ones. So gaining an understanding of how these
libraries and tools work will help you handle
different tasks easily.
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Once programmers gain an understanding of various
tools and frameworks, the next problem they face
is to decide which approach to follow and how to
deal with a particular problem.
The choice sometimes will be right but can be
wrong too which could become a reason for
discouragement for many programmers. So you need
to learn the concepts clearly and gain certain
familiarity so that you can start to predict
better solutions. For this, you need to build
evaluation skills that can be achieved by
enrolling in a coding bootcamp.
Multiple approaches
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Too many learning resources

• With self-paced learning methods, online
  tutorials, and coding bootcamps, it is not easy
  to decide which is the best machine learning
  training path. This has lead to creating even
  further confusion in the minds of developers. To
  figure out which is the suitable learning path
  for you, you need to evaluate the pros and cons
  of each one.

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• Out of all, coding bootcamps are the most
  effective and quickest way to become a certified
  ML engineer. They are fast-paced and provide the
  right kind of training within a short time span.
  If you are looking for a credible suggestion,
  SynergisticIT is a great place to start. They
  have a team of certified experts that enable
  every student to begin a career in this
  ever-growing field. You learn through a series of
  projects and assignments along with gaining
  real-world experience.So, dont let these
  obstacles stop you from pursuing this path and
  begin your machine learning journey now.

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Thanks!

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