Machine Learning: Decision Trees and Random Forests Course
Short course
Online
Description
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Type
Short course
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Methodology
Online
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Class hours
24h
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Duration
12 Months
This excellent Machine Learning – Decision Trees & Random Forests course will teach you two helpful machine learning techniques, decision trees and random forests. If you’re someone who works in analytics, or with big data, this Machine Learning – Decision Trees & Random Forests course will help you to problem solve in a better way.
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Location
Start date
Start date
About this course
"•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
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Those who needs to get to grips with machine learning, this course is for you, and it will help you to learn to use decision trees and random forests.
"•You must be 16 or over
•You should have a basic understanding of English, Maths and ICT
•You will need a computer or tablet with internet connection (or access to one)"
On receiving your request, one of our staff members will call you or send you a message by explaining everything about the course you are requesting information including how you can sign up, payment options, exam and enrollment requirements etc.
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Subjects
- Numpy in Python
- Ensemble Learning
- Bagging
- Cross-Validation
- Overfitting
- Installing Python
- Numpy & Scipy in Python
- Decision Tree Algorithms
- Boosting & Stacking
- The Bane of Machine Learning
Teachers and trainers (1)
Vitthal Srinivasan
Flipkart, Credit Suisse and INSEAD
Course programme
· Module 01: Introduction: You, This Course & Us!
· Module 02: Planting the Seed: What Are Decision Trees?
· Module 03: Growing the Tree: Decision Tree Learning
· Module 04: Branching Out: Information Gain
· Module 05: Decision Tree Algorithms
· Module 06: Installing Python: Anaconda & Pip
· Module 07: Back To Basics: Numpy in Python
· Module 08: Back To Basics: Numpy & Scipy in Python
· Module 09: Titanic: Decision Trees Predict Survival (Kaggle) – I
· Module 10: Titanic: Decision Trees Predict Survival (Kaggle) – II
· Module 11: Titanic: Decision Trees Predict Survival (Kaggle) – III
· Module 12: Overfitting: The Bane of Machine Learning
· Module 13: Overfitting Continued
· Module 14: Cross-Validation
· Module 15: Simplicity Is a Virtue: Regularization
· Module 16: The Wisdom Of Crowds: Ensemble Learning
· Module 17: Ensemble Learning Continued: Bagging, Boosting & Stacking
· Module 18: Random Forests: Much More Than Trees
· Module 19: Back On the Titanic: Cross Validation & Random Forests
"Additional information
What careers can you get with this qualification? Once you have completed this Machine Learning – Deep Learning & Computer Vision: An Introduction course you will have desirable skills. You could go on to further study of this topic, or could gain entry level employment in analytics or big data. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £43,318, and this will go up with experience (payscale.com). When you complete this Machine Learning – Deep Learning & Computer Vision: An Introduction, you could fulfil any of the following roles: Data Scientist Big Data Specialist Data Architect Data Analyst
Machine Learning: Decision Trees and Random Forests Course