If you have some background in machine learning and you'd like to learn how to quickly improve the quality of your models, you're in the right place! In this course, you will accelerate your Machine Learning expertise by learning how to:

tackle data types often found in real-world datasets (missing values, categorical variables),

design pipelines to improve the quality of your machine learning code,

use advanced techniques for model validation (cross-validation),

build state-of-the-art models that are widely used to win Kaggle competitions (XGBoost), and

avoid common and important data science mistakes (leakage).

Along the way, you'll apply your knowledge by completing a hands-on exercise with real-world data for each new topic. The hands-on exercises use data from the Housing Prices Competition for Kaggle Learn Users, where you'll use 79 different explanatory variables (such as the type of roof, number of bedrooms, and number of bathrooms) to predict home prices. You'll measure your progress by submitting predictions to this competition and watching your position rise on the leaderboard!

For more details, you can check it out below 👇

https://www.kaggle.com/learn/intermediate-machine-learning

Prerequisites:

1. Have access to a Browser (preferably Google Chrome)

2. Sign up to Kaggle

3. Eager to Learn !!!

4.Have Completed "Intro to Machine Learning" course