Machine Learning with Python and Scikit-Learn – Full Course

This course is a practical and hands-on introduction to Machine Learning with Python and Scikit-Learn for beginners with basic knowledge of Python and statistics. It is designed and taught by Aakash N S, CEO and co-founder of Jovian. Check out their YouTube channel here: https://youtube.com/@jovianhq We'll start with the basics of machine learning by exploring models like linear & logistic regression and then move on to tree-based models like decision trees, random forests, and gradient-boosting machines. We'll also discuss best practices for approaching and managing machine learning projects and build a state-of-the-art machine learning model for a real-world dataset from scratch. We'll also look at unsupervised learning & recommendations briefly and walk through the process of deploying a machine-learning model to the cloud using the Flask web framework. 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. ❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba) ⭐️ Topics & Notebooks ⭐️ ⌨️ (00:00:00) Introduction ⌨️ (00:00:25) Lesson 1 - Linear Regression and Gradient Descent 🔗 https://jovian.ai/aakashns/python-sklearn-linear-regression ⌨️ (02:17:30) Lesson 2 - Logistic Regression for Classification 🔗 https://jovian.ai/aakashns/python-sklearn-logistic-regression ⌨️ (04:53:26) Lesson 3 - Decision Trees and Random Forests 🔗 https://jovian.ai/aakashns/sklearn-decision-trees-random-forests ⌨️ (07:25:29) Lesson 4 - How to Approach Machine Learning Projects 🔗 https://jovian.com/aakashns/how-to-approach-ml-problems ⌨️ (10:06:13) Lesson 5 - Gradient Boosting Machines with XGBoost