Unsupervised Machine Learning with 2 Capstone ML Projects

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  • Reading time:9 mins read


Learn Complete Unsupervised ML: Clustering Analysis and Dimensionality Reduction

What you will learn

Understand the Working of K Means, Hierarchical, and DBSCAN Clustering.

Implement K Means, Hierarchical, and DBSCAN Clustering using Sklearn.

Learn Evaluation Metrics for Clustering Analysis.

Learn Techniques used for Treating Dimensionality.

Implement Correlation Filtering, VIF, and Feature Selection.

Implement PCA, LDA, and t-SNE for Dimensionality Reduction.

Analyze the Climatic Factors Best to Grow Certain Crops.

Recommend Crops by looking at Certain Climatic Factors.

Categorize the data into n number of relevant groups which are useful for Marketing Purposes.

Identify the Target Group of Customers.

Implement Soft K-Means Clustering in Code.

Understand the limitations of PCA and t-SNE.

Machine learning Concept and Different types of Machine Learning.

Description

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:-

  1. K Means Clustering
  2. Hierarchical Clustering
  3. DBSCAN Clustering
  4. Evaluation Metrics for Clustering Analysis
  5. Techniques used for Treating Dimensionality
  6. Different algorithms for clustering
  7. Different methods to deal with imbalanced data.
  8. Correlation filtering
  9. Variance filtering
  10. PCA & LDA
  11. t-SNE for Dimensionality Reduction

We have covered each and every topic in detail and also learned to apply them to real-world problems.


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You will have lifetime access to the resources and we update the course regularly to ensure that its up to date.

I assure you, there onwards, this course can be your go-to reference to answer all questions about these algorithms.

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.

Make This Investment in Yourself
If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!

Instructor Support – Quick Instructor Support for any queries.

Enroll now and become a master in Unsupervised machine learning.

English
language

Content

Introduction to Clustering Analysis

Introduction to Clustering
Types of Clustering
Applications of Clustering
Quiz on Introduction to Clustering
Using the Elbow Method for Choosing the Best Value for K
Introduction to K Means Clustering
Solving a Real World Problem
Implementing K Means on the Mall Dataset
Using Silhouette Score to analyze the clusters
Clustering Multiple Dimensions
Quiz on K Means Clustering
Introduction to Hierarchical Clustering
Introduction to Dendrograms
Implementing Hierarchical Clustering
Introduction to DBSCAN Clustering
Implementing DBSCAN Clustering
Quiz on Advanced Clustering Techniques

Introduction to Dimensionality Reduction

Why High Dimensional Datasets are a Problem
Methods to solve the problem of High Dimensionality
Solving a Real World Problem
Quiz on Introduction
Introduction to Correlation using Heatmap
Removing Highly Correlated Columns using Correlation
Quiz on Correlation Filtering
Introduction to Variance Inflation Filtering
Implementing VIF using statsmodel
Quiz on Variance Filtering
Introduction to Recursive Feature Selection
Implementing Recursive Feature Selection
Introduction the Boruta Algorithm
Implementing the Boruta Algorithm
Quiz on Feature Selection
Introduction to Principal Component Analysis
Implementing PCA
Introduction to t-SNE
Implementing t-SNE
Introduction to Linear Discriminant Analysis
Implementing LDA
Difference between PCA, t-SNE, and LDA
Quiz on Machine Learning

Optimizing Crop Production

Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Performing Descriptive Statistics
Analyzing Agricultural Conditions
Clustering Similar Crops
Visualizing the Hidden Patterns
Building a Machine Learning Classification Model
Real Time Predictions
Summarizing the Key-Points
Quiz on Optimizing Crop Production

Customer Segmentation Engine

Understanding the Problem Statement
Setting up the Environment
Data Analysis and Visualization
KMeans Clustering Analysis
Applying Hierarchical Clustering
Three Dimensional Clustering
Quiz on Customer Segmentation Engine

Outro Section

Conclusion
How to Get Your Certificate of Completion

Bonus Section

Bonus Lecture
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