Ensemble Machine Learning in Python Random Forest, AdaBoost. Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python
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.
Ensemble Machine Learning in Python Random Forest, AdaBoost Description
Numpy, Matplotlib, Sci-Kit Learn
K-Nearest Neighbors, Decision Trees
Probability and Statistics (undergraduate level)
Linear Regression, Logistic Regresion
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
Who this course is for:
- 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