Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

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Course Data

Lectures: 41
Length: 05h 04m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, 30-day money back guarantee

Course Description

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.



HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • 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 getting through the course):

  • Watch it 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!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don't just sit there and look at my code.


Lectures

Get Started

  1. Outline and Motivation (04:40) (FREE preview available)
  2. Where to get the Code and Data (02:04)
  3. All Data is the Same (03:15)
  4. Plug-and-Play (02:11)

Bias-Variance Trade-Off

  1. Bias-Variance Key Terms (06:37)
  2. Bias-Variance Trade-Off (03:10)
  3. Bias-Variance Decomposition (03:33)
  4. Polynomial Regression Demo (18:08)
  5. K-Nearest Neighbor and Decision Tree Demo (06:32)
  6. Cross-Validation as a Method for Optimizing Model Complexity (04:27)

Bootstrap Estimates and Bagging

  1. Bootstrap Estimation (09:55)
  2. Bootstrap Demo (05:20)
  3. Bagging (02:36)
  4. Bagging Regression Trees (07:19)
  5. Bagging Classification Trees (08:40)
  6. Stacking (03:55)

Random Forest

  1. Random Forest Algorithm (08:55)
  2. Random Forest Regressor (07:06)
  3. Random Forest Classifier (04:56)
  4. Random Forest vs Bagging Trees (03:47)
  5. Implementing a 'Not as Random' Forest (04:13)
  6. Connection to Deep Learning: Dropout (02:39)

AdaBoost

  1. AdaBoost Algorithm (07:09)
  2. Additive Modeling (01:50)
  3. AdaBoost Loss Function: Exponential Loss (07:16)
  4. AdaBoost Implementation (08:26)
  5. Comparison to Stacking (03:29)
  6. Connection to Deep Learning (03:49)
  7. Summary and What's Next (04:55)

Helpful Review

  1. Confidence Intervals (10:17)

Appendix

  1. What is the Appendix? (02:48)
  2. Windows-Focused Environment Setup 2018 (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
  4. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
  5. How to Code Yourself (part 1) (15:55)
  6. How to Code Yourself (part 2) (09:23)
  7. What order should I take your courses in? (part 1) (11:19)
  8. What order should I take your courses in? (part 2) (16:07)
  9. Python 2 vs Python 3 (04:38)
  10. How to Succeed in this Course (Long Version) (10:25)
  11. Where to get discount coupons and FREE deep learning material (02:21)