Introduction to Python Machine Learning using Jupyter Lab

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What you’ll learn: 

Python 3

Exploratory data analysis and visualizations

Machine learning

Building prediction models

Linear regression

Evaluating models

Creating Jupyter notebooks in Jupyter Lab

Common python operations in Jupypter notebooks

Using scikit-learn for machine learning

and more…

Requirements:

Windows PC

No programming experience needed. You will learn everything you need to know.

Description:

If you are looking for a fast and quick introduction to python machine learning, then this course is for you. It is designed to give beginners a quick practical introduction to machine learning by doing hands-on labs using python and JupyterLab. I know some beginners just want to know what machine learning is without too much dry theory and wasting time on data cleaning. So, in this course, we will skip data cleaning. All datasets is highly simplified already cleaned, so that you can just jump to machine learning directly.

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. 

Scikit-learn (also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.

Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of indentations to signify code-blocks. It is also the language of choice for machine learning and artificial intelligence.

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