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Unsupervised Machine Learning From First Principles

  Unsupervised Machine Learning From First Principles

Unsupervised Machine Learning From First Principles | Udemy Coupon ED

Introduction to performing unsupervised machine learning algorithms and using them to improve supervised learning Get Coupon Code
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What you'll learn
  • Principles of Unsupervised Learning
  • Dimension Reduction Techniques
  • Clustering Techniques
  • Anomaly Detection Techniques
  • AutoEncoders
Requirements
  • Python for Data Science
  • Differences between Supervised Learning and Unsupervised Learning techniques
  • Inquisitive mindset!

Description
 
There are some interesting statistics regarding how much data is being generated every day, including:
  1. There are 2.5 quintillion bytes of data created each day at our current pace
  2. Over the last two years alone 90 percent of the data in the world was generated
  3. On average, Google now processes more than 40,000 searches EVERY second (3.5 billion searches per day)
  4. Every minute of the day, Snapchat users share 527,760 photos, more than 120 professionals join LinkedIn, users watch 4,146,600 YouTube videos, 456,000 tweets are sent on Twitter, Instagram users post 46,740 photos and there are 510,000 comments posted and 293,000 statuses updated on Facebook
This data explosion has fueled the need for data scientists and inviduals who can analyse it. The problem is most of that data is unstructured. There is no format to it and so a different set on techniques from the common Supervised Learning are required to analyse it. Those techniques are taught in this course from a First Principles perspective. It is not about the code but the understanding of the concepts of Unsupervised Learning in order to apply them in the real world. What I have actually come to realise is the connection between supervised and unsupervised learning. When doing your Exploratory Data Analysis (EDA), you will probably make use of the techniques discussed in this course, for example Clustering.

The content discussed in this course will be from different sources including:
  1. My "Supervised Machine Learning Course From First Principles" course here on Udemy,
  2. Introduction to Statistical Learning textbook
  3. Attribution for the core content is given to the textbook "Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" which I would urge you to buy on Amazon
  4. Elements of Statistical Learning textbook
  5. Different articles and journal papers.

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