Machine Learning Methods Every Data Scientist Should Know - PowerPoint PPT Presentation

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Machine Learning Methods Every Data Scientist Should Know

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Machine learning is evolving rapidly and to equip you with the finest knowledge through which you can learn Artificial Intelligence and Machine Learning. There are many workshops and E learning classes available through which the attendees can attend and gain proper knowledge. – PowerPoint PPT presentation

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Title: Machine Learning Methods Every Data Scientist Should Know


1
Machine Learning Methods Every Data Scientist
Should Know About Us
MindCypress is an excellent platform for
cognitive e-learning with a great progressive
course structure. We have been creating an impact
on the online education industry, since 2015.
Currently, we are catering to most parts of the
United States (USA), United Kingdom (UK), Middle
East, Africa and South East Asia for services
like Classroom and Live Virtual Training
Courses. In todays time, we are making our
presence globally in the field of e-learning.
Professionals and scholars would get a career
growth with MindCypresss innovative
self-learning certification program. E-learning
courses from MindCypress gives you the
convenience and flexibility to take sessions from
anywhere and indulge in the modules at your own
pace. Our courses are best suited for people who
want to continue working while, studying and earn
a certificate that can turn out to be beneficial
for their career growth.
Machine learning artificial intelligence is
becoming a hot topic in research and industry
and new methodologies are being developed all the
time. The speed and adaptability of the machine
learning and its algorithm makes the keeping with
the new techniques even complex for the expert
and overwhelming for the beginners. To simplify
the machine learning artificial Intelligence
offer the learning path for the people who are
new and interested, letï½s look at the
different methods using simple descriptions,
visualizations and examples for each one.
Machine learning algorithm is also known as model
and it is a mathematical expression that
represents data in context of the problem. The
aim is to migrate from data to insight. For
example, if an online retailer wants to predict
the sales for the next quarter, They can use the
machine learning algorithm that predict the sale
based on the past sale and other relevant data.
The ten methods of machine learning
2
described offer an overview and foundation you
can easily build with the machine learning
knowledge.
  • Regression
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Ensemble Methods
  • Neural Nets and Deep Learning
  • Transfer Learning
  • Reinforcement Learning
  • Natural Language Processing
  • Word Embedding
  • There are two categories of machine learning
    supervised and unsupervised. We apply supervised
    machine learning techniques when we have data
    that we want to predict or explain. Unsupervised
    learning looks at the ways to relate and group
    the data points without the use of a target
    variable.
  • More data, More questions and better answers
  • Machine learning algorithms find natural patterns
    that helps you to make better decisions and
    predictions. These patterns are used to make
    critical decisions in the highly computable jobs
    like medical domain, stock trading, energy load
    forecasting and many more.
  • Machine Learning with MATLAB

3
workshops and E learning classes available
through which the attendees can attend and gain
proper knowledge. MindCypress will help you
with the training. Contact us today! Resource
https//blog.mindcypress.com/p/machine-learning-
methods-every-data-scientist-should-know
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