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Course
- AI
- Data
Designing a Machine Learning Model
This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
What you'll learn
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.
In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it.
First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated.
Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks.
When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.
Table of contents
- Version Check | 16s
- Module Overview | 1m 13s
- Prerequisites and Course Outline | 1m 49s
- A Case Study: Sentiment Analysis | 6m 50s
- Sentiment Analysis as a Binary Classification Problem | 2m 19s
- Rule Based vs. ML Based Analysis | 6m 34s
- Traditional Machine Learning Systems | 4m 28s
- Representation Machine Learning Systems | 2m 16s
- Deep Learning and Neural Networks | 4m 37s
- Traditional ML vs. Deep Learning | 2m 59s
- Traditional ML Algorithms and Neural Network Design | 4m 42s
- Module Summary | 1m 28s
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
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