Unsupervised Machine Learning with 2 Capstone ML Projects
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Crazy about Unsupervised Machine Learning?
This course is a perfect fit for you.
This course will take you step by step into the world of Unsupervised Machine Learning.
Unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets.
These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.
This course will give you theoretical as well as practical knowledge of Unsupervised Machine Learning.
This Unsupervised Machine Learning course is fun as well as exciting.
It will cover all common and important algorithms and will give you the experience of working on some real-world projects.
This course will cover the following topics:-
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K Means Clustering
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Hierarchical Clustering
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DBSCAN Clustering
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Evaluation Metrics for Clustering Analysis
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Techniques used for Treating Dimensionality
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Different algorithms for clustering
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Different methods to deal with imbalanced data.
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Correlation filtering
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Variance filtering
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PCA & LDA
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t-SNE for Dimensionality Reduction
We have covered each and every topic in detail and also learned to apply them to real-world problems.
There are lots and lots of exercises for you to practice and also 2 bonus Unsupervised Machine Learning Project “Optimizing Crop Production” and “Customer Segmentation Engine“.
In this Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.
You will make use of all the topics read in this course.
You will also have access to all the resources used in this course.
Enroll now and become a master in Unsupervised machine learning.
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1
Introduction to Clustering -
2
Types of Clustering
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3
Applications of Clustering
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4
Quiz on Introduction to Clustering -
5
Using the Elbow Method for Choosing the Best Value for K
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6
Introduction to K Means Clustering
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7
Solving a Real World Problem
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8
Implementing K Means on the Mall Dataset
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9
Using Silhouette Score to analyze the clusters
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10
Clustering Multiple Dimensions
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11
Quiz on K Means Clustering
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12
Introduction to Hierarchical Clustering
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13
Introduction to Dendrograms
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14
Implementing Hierarchical Clustering
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15
Introduction to DBSCAN Clustering
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16
Implementing DBSCAN Clustering
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17
Quiz on Advanced Clustering Techniques
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18
Why High Dimensional Datasets are a Problem
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19
Methods to solve the problem of High Dimensionality
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20
Solving a Real World Problem
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21
Quiz on Introduction
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22
Introduction to Correlation using Heatmap
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23
Removing Highly Correlated Columns using Correlation
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24
Quiz on Correlation Filtering
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25
Introduction to Variance Inflation Filtering
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26
Implementing VIF using statsmodel
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27
Quiz on Variance Filtering
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28
Introduction to Recursive Feature Selection
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29
Implementing Recursive Feature Selection
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30
Introduction the Boruta Algorithm
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31
Implementing the Boruta Algorithm
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32
Quiz on Feature Selection
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33
Introduction to Principal Component Analysis
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34
Implementing PCA
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35
Introduction to t-SNE
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36
Implementing t-SNE
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37
Introduction to Linear Discriminant Analysis
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38
Implementing LDA
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39
Difference between PCA, t-SNE, and LDA
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40
Quiz on Machine Learning
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41
Setting up the Environment
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42
Understanding the Dataset
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43
Understanding the Problem Statement
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44
Performing Descriptive Statistics
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45
Analyzing Agricultural Conditions
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46
Clustering Similar Crops
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47
Visualizing the Hidden Patterns
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48
Building a Machine Learning Classification Model
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49
Real Time Predictions
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50
Summarizing the Key-Points
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51
Quiz on Optimizing Crop Production
