Introduction to Python Machine Learning using Jupyter Lab

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.JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. Inside JupyterLab, we can create multiple notebooks. Each notebook for every machine learning project.In this introductory course, we will cover very simplified machine learning by using python and scikit-learn to do predictions.  And we will perform machine learning all using the web-based interface workspace also known as Jupyter Lab.  I have chosen Jupyter Lab for