Complete Machine Learning Course > Unsupervised Learning Algorithms

DBscan Spatial Clustering in Machine Learning

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DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering base technique. It can find clusters of various forms and sizes in a big amount of data that includes noise and outliers.

The goal of DBSCAN is to locate data point neighbourhoods that exceed a specific density criterion. The radius of the neighbourhood (eps) and the minimum number of neighbors/data points (minPts) within the radius of the neighbourhood define the density threshold.

DBSCAN is a clustering method used in machine learning to distinguish high-density clusters from low-density clusters.

DBSCAN has two parameters: (eps) and the minimum number of points needed to build a dense zone (minPts). It begins with a random starting spot that has never been visited before. The -neighborhood of this point is retrieved, and if it contains enough points, a cluster is formed.

Advantages. DBSCAN does not require the number of clusters to be specified in advance. DBSCAN works effectively with clusters of any shape. DBSCAN understands noise and is resistant to outliers.

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Course Content

  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
    9m 1s
  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
    5m 22s
  2. Logistic Regression in Machine Learning
    4m 54s
  3. Logistic Regression in Machine Learning - Practical - 1
    32m 40s
  4. Logistic Regression in Machine Learning - Practical - 2
    12m 37s
  5. KNN in Machine Learning
    7m 22s
  6. KNN in Machine Learning Practical - 1
    35m 1s
  7. KNN in Machine Learning - Practical 2
    8m 46s
  8. Decision Trees for Classification in Machine Learning
    6m 17s
  9. Decision Trees for Classification in Machine Learning - Practical - 1
    22m 42s
  10. Decision Trees for Classification in Machine Learning - Practical - 2
    20m 42s
  11. Decision Tree Iterative Dichotomiser in Machine Learning - 3
    22m 52s
  12. Random Forest in Machine Learning
    8m 37s
  13. Support Vector Machine Classifier
    5m 44s
  14. Support Vector Machine Classifier - Practical - 1
    16m 59s
  15. Support Vector Machine Classifier - Practical - 2
    11m 19s
  16. Naive Bayes Classifier in Machine Learning
    5m 25s
  17. Naive Bayes Classifier in Machine Learning Practical - 1
    15m 21s
  18. Naive Bayes Classifier in Machine Learning Practical - 2
    11m 8s
  19. Evaluating Classification Models Performance in Machine Learning
    7m 31s
  20. Evaluating Classification Models Performance in Machine Learning Practical
    33m 2s
  21. Assignment : Supervised Learning Algorithms
    1m 12s

  1. Simple Linear Regression in Machine Learning
    8m 57s
  2. Simple Linear Regression in Machine Learning - Practical
    12m 34s
  3. Salary Prediction using Linear Regression in Machine Learning
    22m 42s
  4. Multi-Linear Regression in Machine Learning
    2m 53s
  5. Startup Prediction using Multiple Regression in Machine Learning
    25m 13s
  6. Support Vector Regressor in Machine Learning
    10m 45s
  7. Support Vector Regressor in Machine Learning - Practical - 1
    6m 48s
  8. Support Vector Regressor in Machine Learning Practical - 2
    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
    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
    18m 3s
  5. Hierarchical Clustering in Machine Learning - Agglomerative , Divisive
    4m 39s
  6. Agglomerative Clustering in Machine Learning - Practical
    4m 45s
  7. Divisive Clustering in Machine Learning - Practical
    14m 42s
  8. DBscan Spatial Clustering in Machine Learning
    8m 50s
  9. Mall Customers Prediction using Hierarchical Clustering in Machine Learning
    19m
  10. Assignment : Unsupervised Learning Algorithms
    2m 41s

  1. Association Rule in Machine Learning - Apriori, FP Growth
    7m 19s
  2. Association Rule in Machine Learning - Apriori Practical
    7m 15s
  3. Market Basket Analysis using Apriori in Machine Learning
    17m 50s
  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
    21m 4s
  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
    33m 47s
  5. Assignment : Reinforcement Learning
    1m 21s

  1. Overview of Dimensionality Reduction in Machine Learning
    4m 26s
  2. Principal Component Analysis in Machine Learning
    33m 44s
  3. Principal Component Analysis in Machine Learning - Practical
    6m 35s
  4. Linear Discriminant Analysis in Machine Learning
    18m 51s
  5. Linear Discriminant Analysis in Machine Learning - Practical
    6m 42s
  6. Assignment : Dimensionality Reduction
    55s

  1. Basics of Regularization and Optimization in Machine Learning
    7m 40s
  2. Cross Validation in Machine Learning
    20m 27s
  3. Hyperparameter Tuning in Machine Learning
    19m 21s
  4. Sampling Methods in Machine Learning
    19m 43s
  5. Underfitting and Overfitting in Models in Machine Learning
    6m 8s
  6. Variance and Bias in Machine Learning
    9m 49s
  7. Assignment : Regularization and Optimization
    1m 4s

  1. Advance Trends in Machine Learning
    10m 19s

  1. Introduction to Keras and Deep Learning
    7m 43s
  2. Practical Demonstration -Keras
    13m 59s

  1. Reinforcement Learning Project - Teach a Taxi Part 1
    16m 21s
  2. Reinforcement Learning Project - Teach a Taxi Part 2
    14m 37s
  3. Reinforcement Learning Project - Teach a Taxi Part 3
    13m 2s
  4. Reinforcement Learning Project - Teach a Taxi Part 4
    15m 26s
  5. Loan Prediction Project Part 1
    35m 45s
  6. Loan Prediction Project Part 2
    26m 17s

  1. Course Summary
    3m 52s

  1. Interview Questions Part 1
    19m 22s
  2. Interview Questions Part 2
    12m 23s
  3. Interview Questions Part 3
    15m 21s

  1. Career Guideline
    6m
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