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AI Machine Learning Course - Decision Trees and Random Forests

AI Machine Learning |Decision Trees and Random Forests | Certificate on completion |


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Summary

Price
£29.95 inc VAT
Study method
Online
Duration
5 hours · Self-paced
Access to content
12 months
Qualification
No formal qualification
Additional info
  • Tutor is available to students

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Overview

E-Courses4you are here to bring you our AI Machine Learning Course - Decision Trees and Random Forests, to give you a crisp yet thorough primer on two great Machine Learning techniques that help cut through the noise: decision trees and random forests.

You will learn about Decision Fatigue, Decision Trees, Overfitting, Random Forests and much more.

Sign up now and get started on your AI Machine Learning journey!

Course media

Description

Modules:

Chapter 01: Decision Fatigue & Decision Trees

Lesson 01: Introduction: You, This Course & Us!
Lesson 02: Planting the seed: What are Decision Trees?
Lesson 03: Growing the Tree: Decision Tree Learning
Lesson 04: Branching out: Information Gain
Lesson 05: Decision Tree Algorithms
Lesson 06: Installing Python: Anaconda & PIP
Lesson 07: Back to Basics: Numpy in Python
Lesson 08: Back to Basics: Numpy & Scipy in Python
Lesson 09: Titanic: Decision Trees predict Survival (Kaggle) – I
Lesson 10: Titanic: Decision Trees predict Survival (Kaggle) – II
Lesson 11: Titanic: Decision Trees predict Survival (Kaggle) – III

Chapter 02: A Few Useful Things to Know about Overfitting

Lesson 01: Overfitting: The Bane of Machine Learning
Lesson 02: Overfitting continued
Lesson 03: Cross-Validation
Lesson 04: Simplicity is a virtue: Regularization
Lesson 05: The Wisdom of Crowds: Ensemble Learning
Lesson 06: Ensemble Learning continued: Bagging, Boosting & Stacking

Chapter 03: Random Forests

Lesson 01: Random Forests: Much more than trees
Lesson 02: Back on the Titanic: Cross Validation & Random Forests

In an age of decision fatigue and information overload, this “Machine Learning: Decision Trees & Random Forests” course is a crisp yet thorough primer on two great Machine Learning techniques that help cut through the noise: decision trees and random forests.

Design and Implement the solution to a famous problem in machine learning: predicting survival probabilities aboard the Titanic. Understand the perils of overfitting, and how random forests help overcome this risk. Identify the use-cases for Decision Trees as well as Random Forests.

This course is taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst.

Who is this course for?

Our Machine Learning package is for driven individuals that have a passion to learn and would like to gain knowledge of a wide range of areas of Machine Learning, Deep Learning and Python.

Requirements

No prerequisites required, but knowledge of some undergraduate level mathematics would help, but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code.

Career path

Data Scientist
Developer
Data Engineer
Senior Data Scientist
Analyst
Architect
Python Engineer
Software Engineer

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FAQs

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