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Instructor
Lessons
22

Ending In:
access
lifetime
content
1.5 Hours
Cluster analysis is a staple of unsupervised machine learning and data science, used extensively for data mining and big data because it automatically finds patterns in data. The real-world applications for this process, then, are vital, making people who can implement cluster analyses a hot commodity in the business world. In this course, you'll become a master of clustering.

  • Access 22 lectures & 1.5 hours of content 24/7
  • Discuss k-means clustering & hierarchical clustering
  • Explore Gaussian mixture models & kernel density estimation
  • Create your own labels on clusters
Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of Python and Numpy coding is expected
  • All code for this course is available for download here, in the directory unsupervised_class

Compatibility

  • Internet required

Terms

  • Instant digital redemption
access
lifetime
content
1.5 Hours