This course is for individuals or groups who need to gain an understanding of the Intermediate concepts in Data Science. It can be delivered in-house by our industry experts.
Price per person (Ex. VAT): Provided on request
KEY OUTCOMES
- Understanding Linear Regression, its parameters and analysis limits.
- Knowledge of the fundamentals of Cluster Analysis.
- The fundamental knowledge of Intermediate Statistics and Probability.
- Understanding the differences between Supervised vs. Unsupervised Learning.
- An introduction to neural networks and what they mean.
- How to host a machine learning model, and maintaining their functionality.
COURSE OVERVIEW
Take a look at what our course modules include in a little more detail:
Module One: Understanding Linear Regression
What is linear regression?; Required parameters; Limits of regression analysis
Module Two: Cluster Analysis
Dividing unlabelled data; Supporting many clusters; Common applications of clustering
Module Three: Intermediate Statistics and Probability
Normal distribution; Tendency and skewness; Standard deviation, variance, etc
Module Four: Supervised vs. Unsupervised Learning
Examples of supervised learning; Examples of unsupervised learning; Value of labelled data; Using unsupervised learning to confirm label accuracy
Module Five: Neural Networks Introduction
What is a neural network?; How to define network nodes; Key tools and platforms
Module Six: Deployment
How to host a machine learning model; Continued learning; Updating/improving models in production
If you have any questions about this course, please feel free to get in touch via email on info@thezishi.com or by calling +44 (0)204 551 8568 (please choose option 2).
This course is for individuals or groups who need to gain an understanding of the Intermediate concepts in Data Science. It can be delivered in-house by our industry experts.
Price per person (Ex. VAT): Provided on request
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