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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

This Course Includes
udemy
3.9 (67 reviews )
5.5 total hours
english
Online - Self Paced
course
Udemy
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.