Using BigML: An Introduction to Machine Learning & BigML
Machine Learning 2020
| Beginner
- 11 videos | 1h 10m 42s
- Includes Assessment
- Earns a Badge
From self-driving cars to predicting stock prices, machine learning has an exciting range of applications. BigML, due to its ease of use, makes these algorithms widely accessible. This course outlines machine learning fundamentals and how these are applied in BigML. You'll start by examining various machine learning algorithm categories and the kinds of problems they're used to solve. You'll then investigate the classification problem and the process involved in training and evaluating such models. Next, you'll examine linear regression and how this can help predict a continuous value. Moving on, you'll explore the concept of unsupervised learning and its application in clustering, Principal Component Analysis (PCA), and generating associations. Finally, you'll recognize how all of this comes together when using BigML to significantly simplify the building and maintenance of your machine learning models.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
recognize machine learning algorithm types and their applications
describe the process of using training data to construct a machine learning model
recall the various metrics used to evaluate the quality of a machine learning model
describe the features and use cases of linear regression
distinguish between supervised and unsupervised learning algorithms -
recognize the purpose of clustering algorithms and list some of their use cases
describe the factors involved in extracting principal components from large datasets
recognize what association rules are and state their applications
list the features of BigML and the variety of models that can be built using this tool
summarize the key concepts covered in this course
IN THIS COURSE
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2m 34s
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7m 11s
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3. How Machine Learning Works7m 40s
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4. Confusion Matrix7m 6s
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5. Linear Regression9m 19s
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6. Supervised vs. Unsupervised Learning8m 7s
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7. Clustering in ML6m 25s
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8. Principal Component Analysis (PCA)7m 13s
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9. Associations in ML8m 18s
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10. BigML and Machine Learning5m 17s
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11. Course Summary1m 31s
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