Certificate in Unsupervised Machine Learning

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive Online e-Learning Access (LMS)
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification


$49.00 /-

Unsupervised Machine Learning is a type of machine learning where the model is trained on data without labeled responses. The goal is to uncover hidden patterns or structures in the data. Unlike supervised learning, where the model is trained on input-output pairs, unsupervised learning works with data that doesn't have predefined labels or targets.

Key Points

  • Objective To find patterns, groupings, or relationships in the data.
  • Common Techniques Clustering (e.g., K-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE).
  • Applications Customer segmentation, anomaly detection, feature reduction, and data exploration.

Note: Please note that only e-learning videos will be provided.

Why should one take Unsupervised Machine Learning Certification?

Unsupervised Machine Learning is important and relevant for several reasons

  • Pattern Discovery
  • Data Exploration
  • Dimensionality Reduction Techniques
  • Anomaly Detection
  • Segmentation
  • Feature Learning
  • Exploratory Data Analysis

Overall, unsupervised learning provides valuable tools for analyzing and interpreting data where labeled examples are not available, offering insights that can drive strategic decisions and innovations.

Who will benefit from taking Unsupervised Machine Learning Certification?

A Certificate in Unsupervised Machine Learning can benefit a wide range of professionals, including

  • Data Scientists
  • Machine Learning Engineers
  • Business Analysts
  • Researchers
  • Product Managers
  • Data Engineers
  • Consultants
  • Financial Analysts
  • Marketing Professionals

Overall, the certification is valuable for anyone involved in data-driven decision-making or who wishes to deepen their understanding of data analysis techniques.

Unsupervised Machine Learning Table of Contents

https://www.vskills.in/certification/unsupervised-machine-learning-certification-table-of-contents

Unsupervised Machine Learning Practice Questions

https://www.vskills.in/practice/unsupervised-machine-learning-practice-questions

Unsupervised Machine Learning Interview Questions

https://www.vskills.in/interview-questions/unsupervised-machine-learning-interview-questions

Companies that hire Unsupervised Machine Learning Professionals

Companies across various industries hire professionals with a Certificate in Unsupervised Machine Learning. Some of the key sectors and types of companies that look for such expertise include

  • Financial Services
  • Healthcare and Pharmaceuticals
  • Retail and E-commerce
  • Telecommunications
  • Consulting Firms
  • Startups and Tech Innovators
  • Education and Research Institutions

These companies and sectors value the ability to uncover patterns, segment data, and improve decision-making processes using unsupervised learning techniques.

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TABLE OF CONTENT


Unsupervised Learning

  • What is unsupervised learning used for?
  • Why Use Clustering?

K-Means Clustering

  • An Easy Introduction to K-Means Clustering
  • Hard K-Means: Exercise Prompt 1
  • Hard K-Means: Exercise 1 Solution
  • Hard K-Means: Exercise Prompt 2
  • Hard K-Means: Exercise 2 Solution
  • Hard K-Means: Exercise Prompt 3
  • Hard K-Means: Exercise 3 Solution
  • Hard K-Means Objective: Theory
  • Hard K-Means Objective: Code
  • Visual Walkthrough of the K-Means Clustering Algorithm (Legacy)
  • Soft K-Means
  • The K-Means Objective Function
  • Soft K-Means in Python Code
  • How to Pace Yourself
  • Visualizing Each Step of K-Means
  • Examples of where K-Means can fail
  • Disadvantages of K-Means Clustering
  • How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
  • Using K-Means on Real Data: MNIST
  • One Way to Choose K
  • K-Means Application: Finding Clusters of Related Words
  • Clustering for NLP and Computer Vision: Real-World Applications

Hierarchical Clustering

  • Visual Walkthrough of Agglomerative Hierarchical Clustering
  • Agglomerative Clustering Options
  • Using Hierarchical Clustering in Python and Interpreting the Dendrogram
  • Application: Evolution
  • Application: Donald Trump vs. Hillary Clinton Tweets

Gaussian Mixture Models (GMMs)

  • Gaussian Mixture Model (GMM) Algorithm
  • Write a Gaussian Mixture Model in Python Code
  • Practical Issues with GMM
  • Comparison between GMM and K-Means
  • Kernel Density Estimation
  • GMM vs Bayes Classifier (pt 1)
  • GMM vs Bayes Classifier (pt 2)
  • Expectation-Maximization (pt 1)
  • Expectation-Maximization (pt 2)
  • Expectation-Maximization (pt 3)

Setting Up Your Environment

  • Pre-Installation Check
  • Anaconda Environment Setup
  • How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow

Extra Help With Python Coding for Beginners

  • How to Code Yourself (part 1)
  • How to Code Yourself (part 2)
  • Proof that using Jupyter Notebook is the same as not using it
  • How to use Github & Extra Coding Tips (Optional)

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