Free Certification Course Title: Learn Machine Learning in 21 Days

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.

Learn Machine Learning in 21 Days

Requirements:

  • Some basic python programming experience.
  • Basic understanding of python libraries like numpy, pasdas and matplotlib.(Optional)
  • Some high school mathematics.

What you’ll learn:

  • Master Machine Learning on Python
  • Make accurate predictions
  • Make robust Machine Learning models
  • Use Machine Learning for personal purpose
  • Have a great intuition of many Machine Learning models
  • Know which Machine Learning model to choose for each type of problem
  • Use SciKit-Learn for Machine Learning Tasks
  • Make predictions using linear regression, polynomial regression, and multiple regression
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.

Who this course is for:

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.

This course includes:

  • 4.5 hours on-demand video
  • 17 downloadable resources
  • Certificate of completion

How to Subscribe for Learn Machine Learning in 21 Days?

  1. Sign Up on Udemy.com
  2. Subscribe Here(Learn Machine Learning in 21 Days): Click Here

Apply Coupon Code: CODEWARRIORS

**Note: Free coupon/offer may expire soon.**


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