Complete Machine Learning Course in English > Unsupervised Learning Algorithms

Agglomerative Clustering - Practical

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Overview

Hello,

I am (name) from LearnVern,

You are welcome to the course of Machine Learning, and this tutorial is in continuation of our previous tutorial., So let's see ahead.

In today's tutorial we will see hierarchical clustering, as to How we can perform clustering through forming a hierarchy,

So, in this we have two techniques as we had seen,

One is Agglomerative, so what happens in Agglomerative is that… seemingly we have data points like A, B and C, so from start we will have separate clusters,

Then we see that A and B look like one cluster, and C looks to be a part of some other cluster, so in this way we start making the groups to form clusters by combining two or three.

So, this is the direction in which Agglomerative works,

Now, the second is

In this in the start, we consider all the datasets as one cluster, so what happens in this that your A B and C is one cluster,

So, here our approach will be to find the differences, so it seems, A and B look the same and C looks a little different, so both are opposite approaches.

So, let's see Agglomerative,

So from sklearn dot cluster, last time we had performed k means from clusters, so this time we will import Agglomerative here.

" So cannot import name Agglomerative",

Yes! Because this is Agglomerative Clustering.

So, we took this, and along with that we will import

Numpy as np, so this also we will import,

And, let's create sample data now, as we had done earlier also.

Here, x is equal to np dot.. array, and pass a list, and in this list we will create a data 1 comma 2, second record is 1 comma 4, third record is 1 comma 0, so 1 comma 0, next record is 4 comma 2, next is 4 comma 4, and in the last we will end with 4 comma 0,

So, my data is formed.

Now, we will apply Agglomerative Clustering on this,

So, here clustering is equal to Agglomerative Clustering, so I created a model for this.

So, now we will train this clustering model with the help of fit method, so with fit we will train this and pass x.

Now, in the next step What can we do?

So, here… clustering dot, here we can predict.. so here it has already divided this in clusters, so I don't need to perform this.

So, instead I can just see the labels on the basis it has divided,

it has given as 1,1,1,0,0,0

So, it has given the first ones as 1,1 and the last one, it has given them as 0,0

1,2 1,4 1,0.

So, here the magnitude is less in 1,1 and in the below records the magnitude is high, so it seems logical as how it has shown us the clustering here.

Ok!

So, this is Agglomerative Clustering, and you practice this by implementing on real time datasets, and then tell us by writing on the forum that on which dataset you performed this.

So, friends, let's conclude here for today. We will stop this session here, and it's further parts we will cover in the next session.

So, keep learning and remain motivated.

Thank you.

If you have any queries or comments, click the discussion button below the video and post there. This way, you will be able to connect to fellow learners and discuss the course. Also, Our Team will try to solve your query.

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Mohammad Faruk M
5

Where can i get the datasets for assignment

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Aryan Ambat
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Yes

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zeyana Fathima
5

thanks for giving this wonderful course in a understandable way please provide the details from where can i get the datasets

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Losika Nicholas
5

were can i get the dataset

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Kumar Madduru
5

Thanks for giving this course

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Dinesh Kumar
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Your screen is very blur and it doesn't has clarity even in 720P.Please make sure that will not happen again.

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DOGALA UDAYKUMAR
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bettor

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Naresh Kulunge
4

good learning but the content titles are jumbled up, like first title of this module is decision tree dichotomiser which is practical part ahead of theory part. Same with the SVM practical 1 title has

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Eswar Veeranki
5

good

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Isakki Alias Devi P
5

Wonderful course

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Course Content

  1. Getting Started with Machine Learning
    4m 4s
  2. How to use LearnVern
    3m 8s

  1. Introduction to Machine Learning
    16m 19s
  2. Environment Setup Part 1
    3m 21s
  3. Environment Setup Part 2
    4m 52s
  4. Environment Setup Part 3
    2m 35s

  1. Data Wrangling
    12m 36s
  2. Importing Libraries and Dataset
    11m 35s
  3. Handling Missing Data
    12m 13s
  4. Handling Missing Data - Practical
    19m 46s
  5. Encoding Categorical Data
    7m 3s
  6. Encoding Catergorical Data - Practical
    8m 6s
  7. Splitting Dataset
    3m 21s
  8. Splitting Dataset - Practical
    9m 43s
  9. Normalizing the Data - Part 1
    7m 29s
  10. Normalizing the Data - Part 2
    7m 30s
  11. Finding Machine Learning Datasets
    8m 56s
  12. Exploratory Data Analysis
    9m 49s
  13. Plotting Graphs - Part 1
    17m 51s
  14. Plotting Graphs - Part 2
    21m 44s
  15. Distribution Models - Part 1
    29m 48s
  16. Distribution Models - Part 2
    10m 23s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 54s

  1. Machine Learning Paradigms
    10m 37s
  2. Assignment : Machine Learning Paradigms
    3m 22s

  1. Decision Tree Iterative Dichotomiser 3
    20m 2s
  2. Random Forest
    5m 25s
  3. Support Vector Machine Classifier
    6m 22s
  4. Support Vector Machine Classifier - Practical 1
    16m 16s
  5. Support Vector Machine Classifier - Practical 2
    10m 25s
  6. Naive Bayes Classifier
    6m 36s
  7. Naive Bayes Classifier - Practical 1
    14m 34s
  8. Naive Bayes Classifier - Practical 2
    9m 42s
  9. Evaluating Classification Models Performance
    9m 8s
  10. Evaluating Classification Models Performance - Practical
    29m 33s
  11. Overview of Classification
    6m 16s
  12. Logistic Regression
    5m 35s
  13. Logistic Regression - Practical - 1
    30m 22s
  14. Logistic Regression - Practical - 2
    13m 28s
  15. KNN
    7m 34s
  16. KNN Practical - 1
    29m 39s
  17. KNN - Practical 2
    9m 53s
  18. Decision Trees for Classification
    6m 1s
  19. Decision Trees for Classification - Practical 1
    23m 27s
  20. Decision Trees for Classification - Practical 2
    18m 39s
  21. Assignment : Supervised Learning Algorithms
    1m 34s

  1. Simple Linear Regression
    10m 4s
  2. Simple Linear Regression - Practical
    11m 46s
  3. Salary Prediction using Linear Regression
    19m 25s
  4. Multi-Linear Regression
    2m 56s
  5. Startup Prediction using Multiple Regression
    20m 34s
  6. Support Vector Regressor
    11m 15s
  7. Support Vector Regressor - Practical 1
    7m 28s
  8. Support Vector Regressor - Practical 2
    14m 44s
  9. Decision Tree Regressor
    5m 31s
  10. Decision Tree Regressor - Practical 1
    5m 33s
  11. Decision Tree Regressor - Practical 2
    16m 31s
  12. Regressor Model Selection
    11m 8s
  13. Evaluating Regression Model Performance
    10m 45s
  14. Evaluating Regression Model Performance - Practical
    12m 11s
  15. Assignment : Regression Algorithms
    1m 5s

  1. Distance Metrics
    6m 1s
  2. K-Means Clustering
    11m 40s
  3. K-Means Clustering - Practical
    9m 32s
  4. Mall Customers Prediction using K Means Clustering
    16m 42s
  5. Hierarchical Clustering - Agglomerative , Divisive
    5m 25s
  6. Agglomerative Clustering - Practical
    4m 37s
  7. Divisive Clustering - Practical
    13m 43s
  8. DBscan Spatial Clustering
    10m 53s
  9. Mall Customers Prediction using Hierarchical Clustering
    17m 48s
  10. Assignment : Unsupervised Learning Algorithms
    3m 11s

  1. Association Rule Learning - Apriori, FP Growth
    8m 17s
  2. Association Rule Learning - Apriori Practical
    7m 41s
  3. Market Basket Analysis using Apriori
    19m 30s
  4. FP Growth
    8m
  5. Market Basket Analysis using FP Growth
    12m 20s
  6. Assignment : Association Rule Mining
    56s

  1. Reinforcement Learning Theory - Multi Armed Bandits
    27m 30s
  2. Upper Confidence Bound - Practical
    10m 53s
  3. Thompson Sampling - Practical
    5m 34s
  4. Q Learning
    32m 24s
  5. Assignment : Reinforcement Learning
    1m 43s

  1. Overview of Dimensoionality Reduction
    4m 40s
  2. Princinpal Component Analysis
    39m 4s
  3. Principal Component Analysis - Practical
    6m 37s
  4. Linear Discriminant Analysis
    19m 9s
  5. Linear Discriminant Analysis - Practical
    6m 29s
  6. Assignment : Dimensionality Reduction
    52s

  1. Basics of Regularization and Optimization
    7m 58s
  2. Cross Validation
    20m 3s
  3. Hyperparameter Tuning
    20m 7s
  4. Sampling Methods
    19m 8s
  5. Underfitting and Overfitting in Models
    5m 33s
  6. Variance and Bias
    9m 33s
  7. Assignment : Regularization and Optimization
    1m 1s

  1. Advance Trends in Machine Learning
    12m 48s

  1. Introduction to Keras and Deep Learning
    9m 2s
  2. Practical Demonstration -Keras
    18m 35s

  1. Reinforcement Learning Project - Teach a Taxi Part 1
    18m 10s
  2. Reinforcement Learning Project - Teach a Taxi Part 2
    15m 11s
  3. Reinforcement Learning Project - Teach a Taxi Part 3
    14m 35s
  4. Reinforcement Learning Project - Teach a Taxi Part 4
    17m 23s
  5. Loan Prediction Project Part 1
    32m 16s
  6. Loan Prediction Project Part 2
    28m 42s

  1. Course Summary
    3m 53s

  1. Interview Questions Part 1
    18m 22s
  2. Interview Questions Part 2
    13m 23s
  3. Interview Questions Part 3
    16m 2s

  1. Career Guidelines
    6m 14s
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