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Getting started with machine learning in Python / [with] Rudy Lai.
- Format:
- Video
- Series:
- Academic Video Online
- Language:
- English
- Subjects (All):
- Python (Computer program language).
- Machine learning.
- Genre:
- Instructional films.
- Physical Description:
- 1 online resource (173 minutes)
- Place of Publication:
- Birmingham, England : PACKT Publishing, 2018.
- Language Note:
- In English.
- System Details:
- video file
- Summary:
- A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems. About This Video: Learn about supervised learning: how to classify data points and predict future numbers Practical exercises on unsupervised learning: how to segment clients and cluster documents. Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and helpful advice. In Detail: Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills. In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with. If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you! All the code and the supporting files are available on GitHub.
- Participant:
- Presenter, Rudy Lai.
- Notes:
- Title from resource description page (viewed April 12, 2019).
- OCLC:
- 1088414253
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