Machine Learning: Random Forest, Adaboost & Decision Tree
Machine Learning: Random Forest, Adaboost & Decision Tree, available at $19.99, has an average rating of 3.85, with 22 lectures, based on 18 reviews, and has 3003 subscribers.
You will learn about 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. This course is ideal for individuals who are Aspiring Data Scientists or Artificial Intelligence/Machine Learning/ Engineers or Student's/Professionals who have some basic knowledge in Machine Learning and want to know about the powerful models like Random Forest, AdaBoost or Entrepreneurs, professionals, and students who want to learn, and apply data science and machine learning to their work It is particularly useful for Aspiring Data Scientists or Artificial Intelligence/Machine Learning/ Engineers or Student's/Professionals who have some basic knowledge in Machine Learning and want to know about the powerful models like Random Forest, AdaBoost or Entrepreneurs, professionals, and students who want to learn, and apply data science and machine learning to their work.
Enroll now: Machine Learning: Random Forest, Adaboost & Decision Tree
Summary
Title: Machine Learning: Random Forest, Adaboost & Decision Tree
Price: $19.99
Average Rating: 3.85
Number of Lectures: 22
Number of Published Lectures: 22
Number of Curriculum Items: 22
Number of Published Curriculum Objects: 22
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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.
Who Should Attend
- 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
Target Audiences
- 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
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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: What is meant by learning part 1
Lecture 3: What is meant by learning part 2
Lecture 4: What is meant by learning part 3
Lecture 5: Machine Learning Problems
Lecture 6: Bias-Variance Trade-off
Chapter 2: Random Forests and Decision Trees
Lecture 1: How Random Forests Work
Lecture 2: How Decision Trees work
Lecture 3: Decision Tree Algorithm
Lecture 4: Decision Trees Demo
Lecture 5: Random Forests in Depth
Lecture 6: Real-Life Analogy and Feature Importance
Lecture 7: Difference Between Random Forests and Decision Trees
Chapter 3: AdaBoost
Lecture 1: What are Ensemble Methods
Lecture 2: Implementing AdaBoost Classifier Part 1
Lecture 3: Implementing AdaBoost Classifier Part 2
Lecture 4: AdaBoost Algorithm
Lecture 5: AdaBoost Efficiency
Lecture 6: AdaBoost Demo 1
Lecture 7: AdaBoost Demo 2
Lecture 8: Bonus Video – Jupyter Notebook
Lecture 9: Bonus Video- Jupyter Notebook 2
Instructors
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Teach Apex
Quality in Education | E-quality in Education -
Teach Apex Pro
Quality in Education | E-Quality in Education
Rating Distribution
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- 2 stars: 1 votes
- 3 stars: 4 votes
- 4 stars: 8 votes
- 5 stars: 5 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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