Scikit-learn is an open-source machine learning library for Python, known for its simplicity, versatility, and accessibility. The library is well-documented and supported by a large community, making it a popular choice for both beginners and experienced practitioners in the field of machine learning.

We just published an 18-hour course on the YouTube channel that is a practical and hands-on introduction to Machine Learning with Python and Scikit-Learn. It is directed at beginners with basic knowledge of Python and statistics.

The course is designed and taught by Aakash N S, CEO and co-founder of Jovian. Aakash has created many popular machine learning courses.

The course starts with the basics of machine learning by exploring models like linear & logistic regression and then moves on to tree-based models like decision trees, random forests, and gradient-boosting machines.

The course also discuss best practices for approaching and managing machine learning projects and demonstrates how to build a state-of-the-art machine learning model for a real-world dataset from scratch. Then the course looks at unsupervised learning & recommendations briefly and walks through the process of deploying a machine-learning model to the cloud using the Flask web framework.

You will learn everything you need to know to start using Scikit-learn for machine learning. Scikit-learn offers a wide range of tools for various machine learning tasks, including classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. Scikit-learn is built upon NumPy, SciPy, and Matplotlib, and its user-friendly interface allows for easy integration into Python applications.

By the end of this course, you'll be able to confidently build, train, and deploy machine learning models in the real world. To get the most out of this course, follow along & type out all the code yourself, and apply the techniques covered here to other real-world datasets & competitions that you can find on platforms like Kaggle.

Here are the lessons in this course:

  • Lesson 1 - Linear Regression and Gradient Descent
  • Lesson 2 - Logistic Regression for Classification
  • Lesson 3 - Decision Trees and Random Forests
  • Lesson 4 - How to Approach Machine Learning Projects
  • Lesson 5 - Gradient Boosting Machines with XGBoost
  • Lesson 6 - Unsupervised Learning using Scikit-Learn
  • Lesson 7 - Machine Learning Project from Scratch
  • Lesson 8 - Deploying a Machine Learning Project with Flask

You can watch the full course on the YouTube channel (18-hour watch).