Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python
What you'll learn:
- Understand and derive the bias-variance decomposition
- Understand the bootstrap method and its application to bagging
- Understand why bagging improves classification and regression performance
- Understand and implement Random Forest
- Understand and implement AdaBoost
In recent years, we've seen a resurgence inAI, orartificial intelligence, andmachine learning.
Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.
Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.
Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.
Google famously announced that they are now "machine learning first", and companies like NVIDIAand Amazonhave followed suit, and this is what's going to drive innovation in the coming years.
Machine learning is embedded into all sorts of different products, and it'sused in many industries, like finance, online advertising, medicine, and robotics.
It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.
Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?
This course is all about ensemble methods.
We've already learned some classic machine learning models like k-nearest neighborand decision tree. We've studied their limitations and drawbacks.
But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?
In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the basemodels they are made of.
In particular, we will study the Random Forest and AdaBoost algorithms in detail.
To motivate our discussion, we will learn about an importanttopic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.
We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.
Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
All the materials for this course are FREE. You can download and installPython, Numpy, andScipywith simple commands onWindows, Linux, or Mac.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about"seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you wantmore than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations
Simple machine learning models like linear regression and decision trees