This is the Day 9 of Kaggle's 30 Days of ML Challenge where you can learn Machine Learning (based on Python) in 30 days (Kind of). It's not a competition but a challenge to make a habit of coding ML every day. If you haven't registered for the Kaggle Challenge Don't worry, You can follow along my videos every day.
From Kaggle Email:
???? What You’ll Learn
In Lesson 3 (Your First Machine Learning Model), you’ll create a machine learning model using the scikit-learn library, one of the most popular and efficient tools for data analysis.
Along the way, you’ll learn some basic techniques for working with very large datasets. These skills are especially important for modern data scientists, who often work with “big data” containing millions of variables ― many more than a human can conceivably understand! Thankfully, machines excel at discovering useful patterns in datasets that are too large for humans to wrap their heads around. :)
Once you have built a model, how good is it? How exactly should you judge how close the model’s predictions are to what actually happened? In Lesson 4 (Model Validation), you’ll use model validation to measure the quality of your model.
Tutorial 1 (from Lesson 3 of the Intro to ML course) - https://www.kaggle.com/dansbecker/your-first-machine-learning-model
Exercise 1 (from Lesson 3 of the Intro to ML course) - https://www.kaggle.com/kernels/fork/1404276
Tutorial 2 (from Lesson 4 of the Intro to ML course) - https://www.kaggle.com/dansbecker/model-validation
Exercise 2(from Lesson 4 of the Intro to ML course) -
https://www.kaggle.com/kernels/fork/1259097
Courtesy: Kaggle Learn
Related Videos:
Day 1 - https://youtu.be/gZ8hD9MpQyo
Day 2 - https://www.youtube.com/watch?v=KxBhF-uxOD0
Day 3 - https://www.youtube.com/watch?v=yHOqH7GSfW0
Day 4 - https://youtu.be/Dq72Ng3Cc4Y
Day 5 Part 1 - https://www.youtube.com/watch?v=duquxFeqsiE
Day 5 Part 2 - https://www.youtube.com/watch?v=3RZfILSG7Is
Kaggle Introduction Walkthrough to get you started with Data Science - Webinar
- https://youtu.be/sstKQYgZRPo
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