AI Machine Learning Course - Decision Trees and Random Forests
AI Machine Learning |Decision Trees and Random Forests | Certificate on completion | Student discount card eligible |
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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.
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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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This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
