What you would learn in Introduction to Python Machine Learning using Jupyter Lab course?
If you're searching for a rapid and accessible introduction to machine learning using python, This course is the one for you. It's designed to provide beginners an accessible introduction to machine learning through performing hands-on exercises using python and JupyterLab. Some beginners would like to know what learning is without a lot of tedious theory and spending time cleaning up data. In this class, we'll avoid data cleaning, and every dataset is already cleaned, meaning that you can start machine learning immediately.
Machine learning (ML) is a form of artificial intelligence (AI) that lets software programs improve their accuracy in forecasting outcomes without being explicitly programmed to do it. Machine learning algorithms use previous data as input to determine the future output value.
Scikit-learn (also called Sklearn) is a no-cost machine learning software library compatible with Python. Python programming language. It includes a variety of methods of clustering, classification, and regression algorithms.
Python can be described as an interpreter-based, high-level, general-purpose language for programming. The design philosophy of the language is based on code readability and the use of indentation marks to indicate code blocks. This is the preferred language for artificial intelligence and machine learning.
JupyterLab is the latest interactive web-based development environment that allows notebooks, codes, and data. The flexible interface lets users set up and organize scientific computing, data science, computational journalism, and machine learning workflows. In JupyterLab, we can make several notebooks. Each notebook is for every machine-learning project.
This introductory course will go over simple machine learning using scikit-learn and python to perform predictions. We will also perform machine learning through the web-based interface workspace commonly referred to by the name of Jupyter Lab. I've chosen Jupyter Lab for its simplicity compared to Anaconda, which is a bit more complicated for those who are new. With Jupyter Lab, installing any Python modules is made easy using the native package manager, pip. It eases user experience significantly in comparison to Anaconda.
Minimalistic and straightforward, straight to the essential
created to be a perfect first step for absolute beginners
a rapid and accessible introduction to Machine Learning using Linear Regression
Data cleaning is not included since the entire dataset has been cleaned
For those looking for the fastest and most efficient method to experience the benefits of machine learning
All tools (Jupyter Lab) used are free
Kaggle introduction to further study
Visualization and analysis of data that is exploratory
Building prediction models
Making Jupyter notebooks with Jupyter Lab
Everyday Python operations in Jupypter notebooks
Utilizing scikit-learn to learn machine learning
and much more...
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