Machine Learning: Random Forest, Adaboost & Decision Tree
4.4 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,984 students enrolled

Machine Learning: Random Forest, Adaboost & Decision Tree

Learn Advanced Machine Learning on Random Forest, Adaboost, Decision Trees Hands-on
4.4 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,984 students enrolled
Last updated 9/2019
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 3 hours on-demand video
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Knowing how to write a Python code for Random Forests.
  • Implementing AdaBoost using Python.
  • Having a solid knowledge about decision trees and how to extend it further with Random Forests.
  • Understanding the Machine Learning main problems and how to solve them.
  • Understanding the differences between Bagging and Boosting.
  • Reviewing the basic terminology for any machine learning algorithm.
Course content
Expand all 22 lectures 02:57:58
+ Introduction
6 lectures 50:32
What is meant by learning part 3
Machine Learning Problems
Bias-Variance Trade-off
+ Random Forests and Decision Trees
7 lectures 01:00:55
How Random Forests Work
How Decision Trees work
Decision Tree Algorithm
Random Forests in Depth
Real-Life Analogy and Feature Importance
Difference Between Random Forests and Decision Trees
+ AdaBoost
9 lectures 01:06:31
What are Ensemble Methods
Implementing AdaBoost Classifier Part 1
Implementing AdaBoost Classifier Part 2
AdaBoost Algorithm
AdaBoost Demo 1
AdaBoost Demo 2
Bonus Video - Jupyter Notebook
Bonus Video- Jupyter Notebook 2
  • Python basics
  • NumPy, Matplotlib, Sci-Kit Learn
  • Basic Probability and Statistics

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.

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.

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.

Who this course is for:
  • Aspiring Data Scientists
  • Artificial Intelligence/Machine Learning/ Engineers
  • Student's/Professionals who have some basic knowledge in Machine Learning and want to know about the powerful models like Random Forest, AdaBoost
  • Entrepreneurs, professionals, and students who want to learn, and apply data science and machine learning to their work