Practical Machine Learning with Scikit-Learn

Practical Machine Learning with Scikit-Learn

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Course Details –:


Published by:
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What you’ll learn:-

  • How to implement regression, classification and boosting algorithms
  • Which algorithms work best for a given dataset
  • Data preprocessing

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Requirements

  • Basic python knowledge
  •  Google Colab account

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Description:-
Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it’s most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.
Algorithms we’ll go over (in order):

  • Linear Regression
  • Polynomial Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees
  • Random Forest
  • Principle Component Analysis
  • Gradient Boosting
  • XGBoost
Who this course is for:
  • People looking to get into AI but don’t know where to start
  • People who want to build accurate models as quickly as possible
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Free 12800 100% off







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