Machine Learning Algorithms Tutorial for Beginners
Learn machine learning algorithms, along with data preprocessing: regression, classification, component analysis and boosting all in Scikit-Learn, one of the most popular machine learning libraries for Python.
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Learn machine learning algorithms, along with data preprocessing: regression, classification, component analysis and boosting all in Scikit-Learn, one of the most popular machine learning libraries for Python.
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
What you’ll learn
How to implement regression, classification and boosting algorithms
Which algorithms work best for a given dataset
Data preprocessing
Are there any course requirements or prerequisites?
Basic python knowledge
Google Colab account
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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