Introduction to Python Machine Learning using Jupyter Lab

A quick introduction to machine learning using python scikit-learn linear regression for modelling and prediction

5.00 (1 reviews)
Data Science
2.5 hours
Apr 2022
last update
regular price

What you will 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...


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!


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Installing the tools

Installing python 3
Installing Jupyter lab

Linear regression

Intro to linear regression
Importing datasets
Creating dataframes
Plotting scatter graph using matplotlib
Performing linear regression
Regression score and salary prediction
Using regression-predict function in plot
The split train-test method
MAE, MSE, RMSE and R2Score evaluation methods

Multiple linear regression

Intro to multiple linear regression
Multiple linear regression for predicting C02 emissions

Resources for further studies

Google's Kaggle resources for further studies in machine learning


May 4, 2022
I was unsure whether to dive into Machine Learning and was looking for a quick and easy course to test the waters before diving in. And I found this. It quickly gave me a taste of Machine Learning without all the unnecessary talk. The instructor was direct to the point. This has helped me to decide whether I want to go in-depth into Machine Learning or not. And I found that I like it and will further my studies in this field. Thanks to the Instructor!



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