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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yogesh bandewar
4

where is dataset of this

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Sanjeev raut
5

gooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooodddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd

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Vamsi Goud
5

Good

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Ayush tomar
4

where are datasets of all this available

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Abdul Salam
5

This one is one of the best online free source to study the coding or any particular course.

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happy
4

Course is nice but where is the link for installation of Anaconda

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Digvijay Kewale
4

Course is good understandable but I am not able to download resources (one star less only for this not able to download resources)

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Shivendra Shahi
5

I am impresses by the way of teaching, what a magical teaching skill he has.

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Sandeep Kumar
5

good content with free of cost

Sucheta Kumari
5

Course content and explanation method is just awesome. I like the way they presenting and specially at the end of each video content they feeding next intro content which makes motivated, excited .

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  1. Getting Started with Machine Learning
    4m 4s

  1. Introduction to Machine Learning
    13m 51s
  2. Environment Setup Part 1
    3m 32s
  3. Environment Setup Part 2
    4m 43s
  4. Environment Setup Part 3
    2m 34s

  1. Data Wrangling in Machine Learning
    12m 26s
  2. Importing Libraries and Dataset in Machine Learning
    11m 19s
  3. Handling Missing Data in Machine Learning
    11m 51s
  4. Handling Missing Data in Machine Learning - Practical
    17m 3s
  5. Encoding Categorical Data in Machine Learning
    6m 34s
  6. Encoding Categorical Data in Machine Learning - Practical
    8m 3s
  7. Splitting Dataset in Machine Learning
    3m 38s
  8. Splitting Dataset in Machine Learning - Practical
    9m 36s
  9. Normalizing the Data in Machine Learning - Part 1
    6m 19s
  10. Normalizing the Data in Machine Learning - Part 2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis in Machine Learning
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  13. Plotting Graphs in Machine Learning - Part 1
    19m 12s
  14. Plotting Graphs in Machine Learning - Part 2
    23m 45s
  15. Distribution Models in Machine Learning - Part 1
    33m 55s
  16. Distribution Models in Machine Learning - Part 2
    13m 56s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 40s

  1. Machine Learning Paradigms
    9m 55s
  2. Assignment : Machine Learning Paradigms
    3m 25s

  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
    32m 40s
  4. Logistic Regression in Machine Learning - Practical - 2
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  5. KNN in Machine Learning
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  6. KNN in Machine Learning Practical - 1
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  7. KNN in Machine Learning - Practical 2
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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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  12. Random Forest in Machine Learning
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  13. Support Vector Machine Classifier
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  14. Support Vector Machine Classifier - Practical - 1
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  15. Support Vector Machine Classifier - Practical - 2
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  16. Naive Bayes Classifier in Machine Learning
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  17. Naive Bayes Classifier in Machine Learning Practical - 1
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  18. Naive Bayes Classifier in Machine Learning Practical - 2
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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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  1. Simple Linear Regression in Machine Learning
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  2. Simple Linear Regression in Machine Learning - Practical
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  3. Salary Prediction using Linear Regression in Machine Learning
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  4. Multi-Linear Regression in Machine Learning
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  5. Startup Prediction using Multiple Regression in Machine Learning
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  6. Support Vector Regressor in Machine Learning
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  7. Support Vector Regressor in Machine Learning - Practical - 1
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  8. Support Vector Regressor in Machine Learning Practical - 2
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  9. Decision Tree Regressor in Machine Learning
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  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
    9m 7s
  13. Evaluating Regression Model Performance in Machine Learning
    8m 46s
  14. Evaluating Regression Model Performance in Machine Learning - Practical
    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
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  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
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  7. Divisive Clustering in Machine Learning - Practical
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  8. DBscan Spatial Clustering in Machine Learning
    8m 50s
  9. Mall Customers Prediction using Hierarchical Clustering in Machine Learning
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  10. Assignment : Unsupervised Learning Algorithms
    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
    7m 19s
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    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
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  4. FP Growth in Machine Learning
    7m 43s
  5. Market Basket Analysis using FP Growth in Machine Learning
    11m 46s
  6. Assignment : Association Rule Mining
    46s

  1. Reinforcement Learning in Machine Learning - Multi Armed Bandits
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  2. Upper Confidence Bound in Machine Learning - Practical
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  3. Thompson Sampling in Machine Learning - Practical
    4m 18s
  4. Q Learning in Machine Learning
    33m 47s
  5. Assignment : Reinforcement Learning
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  1. Overview of Dimensionality Reduction in Machine Learning
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  2. Principal Component Analysis in Machine Learning
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  3. Principal Component Analysis in Machine Learning - Practical
    6m 35s
  4. Linear Discriminant Analysis in Machine Learning
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  5. Linear Discriminant Analysis in Machine Learning - Practical
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  6. Assignment : Dimensionality Reduction
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  1. Basics of Regularization and Optimization in Machine Learning
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  3. Hyperparameter Tuning in Machine Learning
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  7. Assignment : Regularization and Optimization
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  1. Advance Trends 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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  3. Reinforcement Learning Project - Teach a Taxi Part 3
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  5. Loan Prediction Project Part 1
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  1. Course Summary
    3m 52s

  1. Interview Questions Part 1
    19m 22s
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  3. Interview Questions Part 3
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  1. Career Guideline
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