Decision Trees for Machine Learning From Scratch
3.6 (72 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
285 students enrolled

Decision Trees for Machine Learning From Scratch

Learn to build decision trees for applied machine learning from scratch in Python.
3.6 (72 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
285 students enrolled
Last updated 5/2020
English
English
Current price: $69.99 Original price: $99.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 3 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • The most common decision tree algorithms
  • Understand the core idea behind decision trees
  • Developing code from scratch
  • Applying ML for practical problems
  • Bagging and Boosting
  • Random Forest, Gradient Boosting
Course content
Expand all 21 lectures 02:58:20
+ ID3 Decision Tree Algorithm
5 lectures 43:55
Entropy calculation
09:03
Information Gain
11:36
Iterative Dichotomiser
12:07
Extending ID3
04:46
+ C4.5 Decision Tree Algorithm
5 lectures 35:04
Gain Ratio Calculation
04:36
Handling with Continuous Features
16:59
Extending C4.5
05:16
Transforming decision rules to python if statements
05:57
+ Classification and Regression Trees (CART)
4 lectures 33:57
CART for classification
13:06
Regression Trees
12:24
In regression trees, we have terminated building branches if number of instances in the sub dataset is less than 5. You responsible for creating a new termination rule.
Pruning
1 question
Requirements
  • Basic python
Description

Decision trees are one of the hottest topics in Machine Learning. They dominate many Kaggle competitions nowadays. Empower yourself for challenges.

This course covers both fundamentals of decision tree algorithms such as CHAID, ID3, C4.5, CART, Regression Trees and its hands-on practical applications. Besides, we will mention some bagging and boosting methods such as Random Forest or Gradient Boosting to increase decision tree accuracy. Finally, we will focus on some tree based frameworks such as LightGBM, XGBoost and Chefboost.

We will create our own decision tree framework from scratch in Python. Meanwhile, step by step exercises guide you to understand concepts clearly.

This course appeals to ones who interested in Machine Learning, Data Science and Data Mining.

Who this course is for:
  • Interested in Machine Learning
  • Wonder Data Mining