Scikit-Learn is a well-known software library for machine learning. It supports both supervised and unsupervised learning and has many tools for fitting models to data, preparing data, selecting and evaluating models, and selecting models.

Here is a list of the best resources to learn Scikit-Learn.

Python for Data Science and Machine Learning Bootcamp

This course will teach you how to use Python's power to analyze data, make visualizations, and use powerful machine-learning techniques. This course is made for both new programmers with some experience and seasoned programmers.

Scikit-Learn Tutorials

This course, which was made by the people who made Scikit-Learn, gives an introduction to machine learning with Scikit-Learn. It talks about things like setting up a problem, adding an example dataset, learning, and making predictions. The lesson is suitable for both new students and more experienced ones. 

Multiple Linear Regression with scikit-learn

In this project-based 2-hour course, you'll use Python to build and test multiple linear regression models. Then, you'll use Scikit-learn to figure out the regression, pandas to handle the data, and Seaborn to show the data. 

This course is run on Rhyme, Coursera's website, for hands-on projects. On Rhyme, you use your computer to do hands-on projects. As a result, you'll have instant access to cloud desktops already set up with all the software and data you need for the job. Everything is already set up in your web browser, so all you have to do is learn. For this project, this means having instant access to a cloud desktop with Jupyter Notebooks, Python 3.7, and all the necessary libraries already loaded.

Perform Sentiment Analysis with Scikit-Learn

You will master the principles of sentiment analysis and create a logistic regression model to classify movie reviews as positive or negative in this project-based course. In addition, you will learn how to use Scikit-Learn to develop and deploy a logistic regression classifier, feature extraction with The Natural Language Toolkit (NLTK), tuning model hyperparameters, and evaluating model accuracy, among other things.  

Intro to Machine Learning with TensorFlow

In this course, you'll learn the primary methods for machine learning, starting with cleaning up data and building supervised models. Then, move on to studying deep learning and learning without being watched. Finally, use what you've learned at each step by doing code tasks and projects.

This programme is for students who already know how to use Python but have yet to learn Machine Learning.

Python Machine Learning: Scikit-Learn Tutorial

This tutorial will teach you the fundamentals of Python machine learning. You will discover how to utilize Python and its tools to investigate your data using Matplotlib and Principal Component Analysis. (PCA). You will also learn how to use the KMeans technique to build an unsupervised model, fit it to your data, forecast values, and validate it.

Analyzing Data with Python

This course will teach you how to analyze data in Python using numpy multidimensional arrays, pandas DataFrames, the SciPy library of mathematical methods, and scikit-learn machine learning.

Predict Sales Revenue with Scikit-Learn

In this two-hour project-based course, you will construct and test a simple linear regression model in Python. In addition, you will use the Scikit-Learn module to calculate linear regression and pandas for data handling and visualization. This course will teach you to build a simple linear regression model using Scikit-Learn, Seaborn, and Pandas for Exploratory Data Analysis (EDA) on small data sets.

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