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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  • Lectures 32
  • Length 3 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 12/2016 English

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 morethan just a superficial look at machine learning models, this course is for you.


NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: supervised_class2

Make sure you always "git pull" so you have the latest version!

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.

USEFUL COURSE ORDERING:

  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Natural Language Processing with Deep Learning in Python

What are the requirements?

  • Differential calculus
  • Numpy, Matplotlib, Sci-Kit Learn
  • K-Nearest Neighbors, Decision Trees
  • Probability and Statistics (undergraduate level)
  • Linear Regression, Logistic Regresion

What am I going to get from this course?

  • 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

Who is the target audience?

  • Understand the types of models that win machine learning contests (Netflix prize, Kaggle)
  • Students studying machine learning
  • Professionals who want to apply data science and machine learning to their work
  • Entrepreneurs who want to apply data science and machine learning to optimize their business
  • Students in computer science who want to learn more about data science and machine learning
  • Those who know some basic machine learning models but want to know how today's most powerful models (Random Forest, AdaBoost, and other ensemble methods) are built

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Get Started
Outline and Motivation
Preview
04:40
Where to get the Code and Data
Preview
02:04
All Data is the Same
Preview
03:15
Plug-and-Play
Preview
02:11
Section 2: Bias-Variance Trade-Off
Bias-Variance Key Terms
06:37
Bias-Variance Trade-Off
03:09
Bias-Variance Decomposition
03:32
Polynomial Regression Demo
18:07
K-Nearest Neighbor and Decision Tree Demo
06:32
Cross-Validation as a Method for Optimizing Model Complexity
04:26
Section 3: Bootstrap Estimates and Bagging
Bootstrap Estimation
09:55
Bootstrap Demo
05:20
Bagging
02:36
Bagging Regression Trees
07:19
Bagging Classification Trees
08:39
Stacking
03:54
Section 4: Random Forest
Random Forest Algorithm
08:54
Random Forest Regressor
07:05
Random Forest Classifier
04:56
Random Forest vs Bagging Trees
03:47
Implementing a "Not as Random" Forest
04:12
Connection to Deep Learning: Dropout
02:38
Section 5: AdaBoost
AdaBoost Algorithm
07:09
Additive Modeling
01:50
AdaBoost Loss Function: Exponential Loss
07:15
AdaBoost Implementation
08:26
Comparison to Stacking
03:29
Connection to Deep Learning
03:48
Summary and What's Next
04:55
Section 6: Appendix
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:22
Confidence Intervals
10:17
BONUS: Where to get Udemy coupons and FREE deep learning material
02:20

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Instructor Biography

Lazy Programmer Inc., Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.

I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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