Master ensemble methods like boosting and bagging in this fun, hands-on Python course. Learn through real experiments and get a deep understanding of machine learning!
This course takes you through the fascinating world of ensemble methods in machine learning using Python. It's perfect for diving deep and really grasping how these algorithms work in practice.
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.
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.
"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...
Suggested Prerequisites:
Calculus (derivatives)
Probability
Object-oriented programming
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations
Simple machine learning models like linear regression and decision trees
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out
Understand and derive the bias-variance decomposition
Understand the bootstrap method and its application to bagging
Understand why bagging improves classification and regression performance
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The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into...
The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into...
Kept referring to other videos. Not stand alone.
Lazy Programmer is a great teacher. He seems to know everything regarding machine learning. I really learned more than expected, especially the lessons in which he lifts the curtain for mathematical details that explain how everything works behind the scenes.
So good that it made me gain a lot of interest to watch the course and it allows me to develop and explore on my own. The good part is his way of teaching and letting me to try codes.
Perfect course for anyone pursuing machine learning. This course focuses on all fundamentals of ensemble methods along with practical based approaches. I have been recommending Lazy Programmer for courses on machine learning and AI to a lot of friends and colleagues. Lazy Programmer is the only instructor online who can make you excel in both theory and practice.
Grateful to do the course. Enjoyed each section while studying. Thank you for giving me the opportunity to learn this advanced machine learning tutorial.
This course is very amazing course and there is practice exercise for every topic discussed.
I always struggled to understand the idea behind boosting from several free resources I looked at on the Internet. But with a well curated machine learning course from the Lazy Programmer, I've finally come to a firm understanding of the concepts I once struggled with. It's a must have if you want to learn about ensemble methods.
Course is literally great and the Lazy Programmer is an amazing tutor. His way of explaining things is just amazing. He explains concepts in a clear and concise way, which is why I gave this course a 5 star rating.
This course is short but very complete and the instructor explains everything very well. I can't find any other machine learning courses on Udemy that are as advanced as these. I would totally recommend them if you want to learn better machine learning techniques.
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Rupinder R.
Great course and journey. I updated my machine learning skills and sharped them up, I had some basics, but updated my skills to a completely new level.