Machine Learning Methods Every Data Scientist Should Know
                     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 today’s time, we are making our presence globally in  the field of 
e-learning. Professionals and scholars would get a career growth with  
MindCypress’s 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, let�s 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
described offer an overview and foundation you can easily build with 
the machine  learning knowledge.
1. Regression
2. Classification
3. Clustering
4. Dimensionality Reduction
5. Ensemble Methods
6. Neural Nets and Deep Learning
7. Transfer Learning
8. Reinforcement Learning
9. Natural Language Processing
10.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
MATLAB makes machine learning easy and with the tools and functions for 
handling big  data and apps to make machine learning accessible, MATLAB is 
an ideal environment  for applying machine learning in the data analytics.
Conclusion
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.
MindCypress will help you with the training. Contact us today!
Resource: 
https://blog.mindcypress.com/p/machine-learning-  
methods-every-data-scientist-should-know