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 Students/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 Students/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

Students/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

Students/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

Teach Apex
Quality in Education | E-quality in Education

Teach Apex Pro
Quality in Education | E-Quality in Education

Rating Distribution

1 stars: 0 votes

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!