This is an introductory workshop that will thoroughly explain Machine Learning. We dive into Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
I breakdown all of the algorithms and use cases associated with the different forms of Machine Learning.
This is the first Machine Learning workshop that ACTUALLY breaks down Machine Learning in a way that can be understood from people that are not Data Scientists and Engineers!
I have professionally taught Data Science and Machine Learning at Chegg, Thinkful, General Assembly, Springboard, Tech Talent South, & the World Data Science Institute.
What is Machine Learning:
Machine Learning gives Computer the ability to make decisions without being programmed.
The primary goal of supervised learning is to accurately predict the future based on past structured and/or labeled data!
The primary goal of unsupervised learning is to group unstructured data based on similarities!
The primary goal of reinforment learning is to simulate human behavior using robots!
Machine Learning use cases:
Customer and client satisfaction. Machine learning helps financial services firms track customer happiness
Used in several platforms to identify a customer is about to leave based on user activity
Reacting to market trends
Market analysis
Used in banking to approve or disapprove credit applications/bank accounts
Identify fraud based on purchasing behaviors
Social media to recommend who you should follow or who should follow you
Used in retail to help us determine what product we should buy in future or with current product
Also used for platforms like Netflix to identify suggested shows for us to watch
Automatically adjusts prices companies that compete with Price Match Guarantee
Siri, Alexa, and Google Map
Uber uses for hot zones and price setting
Image and Speech Recognition
Predicting lifespan based on specific data points previously collected
Stock picks, sports picks, and the list goes on…..
What you'll learn:
*What Machine Learning actually is?
*Supervised Learning Algorithms
*Unsupervised Learning Algorithms
*Machine Learning Use Cases
*Classification Use Cases
*What Unsupervised Learning actually is?
*Unsupervised Learning Algorithms
*Clustering Algorithms
*Association Algorithms
*Dimensionality Reduction Algorithms
*Hard Clustering
*Soft Clustering
*Hierarchical Clustering
*Partitioning Clustering
*Dimension Reduction
*Dimension Reduction Use Cases
*Feature Extraction
