Our Decision Tree and Random Forest Training course is here to help you design and implement the solution to a famous problem in machine learning!
You will learn to understand 2 great Machine Learning techniques, decision trees and random forests.
This course is led by a Stanford-educated, ex-Googler, IIT, IIM – educated ex-Flipkart lead analyst that will teach you everything you need to know!
You do not need any prior experience to take this course, however some knowledge of undergraduate level Mathematics would be helpful to you (however it is not mandatory). You do not need knowledge of Python, but again it would be helpful for you to understand the provided source code and perform the coding exercise.
You will get 12 months worth of access to this 5 course course, covering 3 chapters of valuable information!
Some of the subjects you will get to learn about are:
-What are Decision Trees?
-Decision Tree Algorithms
-Decision Trees predict Survival
And much more!
Sign up now!
Unlimited access for 12 months
Access anywhere, any time
Fast effective training, written and designed by industry experts
Track your progress with our Learning Management System
Save money, time and travel costs
Learn at your own pace and leisure
Easier to retain knowledge and revise topics than traditional methods
Exam preparation quizzes, tests and mock exams to ensure that you are 100% ready
£239.00 Add to basket
Course Length: 5 Hours
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
Minimum specifications for the computer are:
Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)
OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)
Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)