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Machine Learning and Data Science Essentials with Python & R

Master Machine Learning with Python, Tensorflow & R. Data Science is the most in-demand and Highest Paying Job of 2018

     
  • 3.9
  •  |
  • Reviews ( 67 )
₹799

This Course Includes

  • iconudemy
  • icon3.9 (67 reviews )
  • icon5.5 total hours
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconUdemy

About Machine Learning and Data Science Essentials with Python & R

Meet Machine Learning, the in-demand and Highest Paying job skill of 2018 and beyond. Machine learning is  increasingly shaping future of work and jobs. With an average salary of $120,000 (Glassdoor and Indeed), Machine Learning will help you to get one of the top-paying jobs. 

Machine Learning,  provides computers the ability to automatically learn and improve from experience.

Today, data scientists are generally divided among two languages , some prefer R, some prefer Python. The course touches both R and Python implementations of Machine Learning.

By the end of the course you will be able to 

Master Machine Learning using Python and R

Understand Linear Algebra

Matrix Operations in R and Python

Implement Linear Regression with R, Python & Tensorflow

Logistic Regression with R, Python & Tensorflow

Practical Machine Learning Problems and solution

Implement K-means and K-NN algorithm on R

Implement K-NN on python using tensorflow

Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities.

100% MONEY-BACK GUARANTEE

This course comes with a 30-day money back guarantee. If you're not happy, ask for a refund, all your money back, no questions asked.

Feel forward to have a look at course description and demo videos and we look forward to see you inside.

What You Will Learn?

  • Master Machine Learning using Python and R.
  • Understand Linear Algebra.
  • Matrix Operations in R and Python.
  • Implement Linear Regression with R, Python & Tensorflow.
  • Logistic Regression with R, Python & Tensorflow.
  • Practical Machine Learning Problems and solution.
  • Implement K-means and K-NN algorithm on R.
  • Implement K-NN on python using tensorflow.