In recent years, we've seen a resurgence in
AI, or
artificial intelligence, and
machine 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
NVIDIA and
Amazon have 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's used 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 neighbor and
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 base models 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 important topic 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 install Python, Numpy, and Scipy with simple commands on Windows, 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 want more than just a superficial look at machine learning models, this course is for you.
Suggested Prerequisites:
- calculus
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- Linear Regression, Logistic Regression
- K-Nearest Neighbor, Decision Trees
Tips for success:
- Use the video speed changer! Personally, I like to watch at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
- Write code yourself, this is an applied course! Don't be a "couch potato".