Complete Machine Learning Course > Supervised Learning Algorithms

Decision Tree Iterative Dichotomiser in Machine Learning - 3

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To decide whether to break a node into two or more sub-nodes, decision trees employ a variety of techniques. The homogeneity of the generated sub-nodes improves with the generation of sub-nodes. To put it another way, the purity of the node improves as the target variable grows.

ID3 stands for Iterative Dichotomiser 3, and it is so named because the method separates features into two or more groups iteratively (repeatedly) at each stage. ID3 is a top-down greedy way to building a decision tree that was invented by Ross Quinlan.

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R
Rishu Shrivastav
5

explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?

V
VIKAS CHOUBEY
5

very nicely explained

V
Vrushali Kandesar
5

Awesome and very nicely explained!!! One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)

M
Mohd Mushraf
5

Amazing Teaching

J
Juboraj Juboraj
5

Easy to understand & explain details.

J
Joydeb
5

Awesome Course sir and your teaching style is very GOOD.

S
Shaga Chandrakanth Goud
5

Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld

N
Neel Khairnar
5

Kushal is very good explainer he is covering all topics nicely 👍

S
Shaik Feroz
5

good

H
HANEEF
5

where do we get datasets for assignments

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  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
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  3. Environment Setup Part 2
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  1. Data Wrangling in Machine Learning
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  2. Importing Libraries and Dataset in Machine Learning
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  3. Handling Missing Data in Machine Learning
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  5. Encoding Categorical Data in Machine Learning
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  6. Encoding Categorical Data in Machine Learning - Practical
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  7. Splitting Dataset in Machine Learning
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    9m 36s
  9. Normalizing the Data in Machine Learning - Part 1
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  15. Distribution Models in Machine Learning - Part 1
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  17. Assignment : Data Preprocessing for Machine Learning
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  1. Machine Learning Paradigms
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  1. Overview of Classification
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  2. Logistic Regression in Machine Learning
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  3. Logistic Regression in Machine Learning - Practical - 1
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  5. KNN in Machine Learning
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  6. KNN in Machine Learning Practical - 1
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  8. Decision Trees for Classification in Machine Learning
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  9. Decision Trees for Classification in Machine Learning - Practical - 1
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  10. Decision Trees for Classification in Machine Learning - Practical - 2
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  11. Decision Tree Iterative Dichotomiser in Machine Learning - 3
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  19. Evaluating Classification Models Performance in Machine Learning
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  20. Evaluating Classification Models Performance in Machine Learning Practical
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  21. Assignment : Supervised Learning Algorithms
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  6. Support Vector Regressor in Machine Learning
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    15m 46s
  9. Decision Tree Regressor in Machine Learning
    4m 53s
  10. Decision Tree Regressor in Machine Learning Practical - 1
    5m 2s
  11. Decision Tree Regressor in Machine Learning Practical - 2
    17m 10s
  12. Regressor Model Selection in Machine Learning
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    11m 35s
  15. Assignment : Regression Algorithms
    1m 12s

  1. Distance Metrics in Machine Learning
    6m 10s
  2. K-Means Clustering in Machine Learning
    11m 3s
  3. K-Means Clustering in Machine Learning - Practical
    9m 49s
  4. Mall Customers Prediction using K Means Clustering in Machine Learning
    18m 3s
  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
    4m 45s
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    14m 42s
  8. DBscan Spatial Clustering in Machine Learning
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    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
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    7m 15s
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    11m 46s
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  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
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  2. Upper Confidence Bound in Machine Learning - Practical
    8m 41s
  3. Thompson Sampling in Machine Learning - Practical
    4m 18s
  4. Q Learning in Machine Learning
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  5. Assignment : Reinforcement Learning
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  1. Overview of Dimensionality Reduction in Machine Learning
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  1. Introduction to Keras and Deep Learning
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  1. Reinforcement Learning Project - Teach a Taxi Part 1
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  1. Course Summary
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  1. Interview Questions Part 1
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  1. Career Guideline
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