Learn Complete Unsupervised ML: Clustering Analysis and Dimensionality Reduction
Duration: 3 Hours
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What you'll 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 Gro
- Python and Jupyter Notebook installed in your System.
- Knowledge about Basic Concepts of Python and its functions.
- Familiarity with Concepts of Data Analysis.
- Understanding of Data Visualizations.
- Understanding of Data Processing.
- Knowledge of Unsupervised Algorithms.
- Knowledge of K Means Clustering Algorithm.
- Good if you have interest in Agricultural Domain.
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:-
K Means Clustering
Evaluation Metrics for Clustering Analysis
Techniques used for Treating Dimensionality
Different algorithms for clustering
Different methods to deal with imbalanced data.
PCA & LDA
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.
Who this course is for:
- Anyone who want to start a career in Unsupervised Machine Learning.
- Any people who want to level up their Unsupervised Machine Learning Knowledge.
- Software developers or programmers or Tech lover who want to change their career path to Unsupervised machine learning.