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 its simplicity compared to Anaconda which can be complicated for beginners. Using Jupyter Lab, installation of any python modules can be easily done using python's native package manager called pip. It simplifies the user experience a lot as compared to Anaconda.

Features of this course:

1. simplicity and minimalistic, direct to the point

2. designed with absolute beginners in mind

3. quick and fast intro to machine learning using Linear Regression

4. data cleaning is omitted as all datasets has been cleaned

5. for those who want a fast and quick way to get a taste of machine learning

6. all tools (Jupyter Lab)  used are completely free

7. introduction to kaggle for further studies

Learning objectives:

At the end of this course, you will:

1. Have a very good taste of what machine learning is all about

2. Be equipped with the fundamental skillsets of Jupyter Lab and Jupyter Notebook, and

3. Ready to undertake more advanced topics in Machine Learning

Enroll now and I will see you inside!