AI Machine Learning: Decision Trees & Random Forests 2017Merchant Name Location
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.
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.
Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.
Python Activity: Surviving aboard the Titanic! Build a decision tree to predict the survival of a passenger on the Titanic. This is a challenge posed by Kaggle (a competitive online data science community). We’ll start off by exploring the data and transforming the data into feature vectors that can be fed to a Decision Tree Classifier.
What’s the Deal?
- AI Machine Learning – Decision Trees & Random Forests 2017 – R449
What the course will teach you:
- Planting the seed: What are Decision Trees?
- Growing the Tree: Decision Tree Learning
- Branching out: Information Gain
- Decision Tree Algorithms
- Installing Python: Anaconda & PIP
- Back to Basics: Numpy & Scipy in Python
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: Regularisation
- 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
Course Length: 5 Hours
- Valid until 31 December 2021.
- Contact email@example.com with a copy of your voucher and they will give you access to the course.
- No refunds or cancellations once redeemed.
- 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)
- 12 months access.
- Access anywhere, any time.
- Fast effective training, written and designed by industry experts.
- Track your progress with our Learning Management System.
- Unlimited customer service.
- Save money, time and travel costs.
- Learn at your own pace and leisure.
- Easier to retain knowledge and revise topics than traditional methods.
Or visit for more info: https://e-courses4you.com/course/ai-machine-learning-decision-trees-random-forests/
Select an OptionValid until 31 December 2021.
R390.43 Excl. VAT R449.00 Incl. VAT R2,874.78 Excl. VAT R3,306.00 Incl. VAT39% Off
Contact firstname.lastname@example.org with a copy of your voucher and they will give you access to the course.
No refunds or cancellations once redeemed.