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Data is the core of machine learning.
It should not surprise you that most of the work you& #39;ll have to do is related to capturing, managing, processing, and validating data.
A few recommendations for those who would like to start.
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It should not surprise you that most of the work you& #39;ll have to do is related to capturing, managing, processing, and validating data.
A few recommendations for those who would like to start.
↓ 1/7
As you get your feet wet, these are roughly some of the areas that you should cover:
• Data collection
• Data visualization
• Imputation
• Handling outliers
• Encoding
• Normalization and scaling
• Binning and grouping
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• Data collection
• Data visualization
• Imputation
• Handling outliers
• Encoding
• Normalization and scaling
• Binning and grouping
↓ 2/7
Here is a good, introductory, free course provided by Google:
"Data Preparation and Feature Engineering in ML." — https://developers.google.com/machine-learning/data-prep/
It">https://developers.google.com/machine-l... covers the process of collecting, transforming, splitting, and creating datasets that machine learning algorithms can use.
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"Data Preparation and Feature Engineering in ML." — https://developers.google.com/machine-learning/data-prep/
It">https://developers.google.com/machine-l... covers the process of collecting, transforming, splitting, and creating datasets that machine learning algorithms can use.
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If you prefer books, check out "Feature Engineering for Machine Learning."
https://amzn.to/3usjyzD
It& #39;s">https://amzn.to/3usjyzD&q... a practical introduction to the fundamental techniques for extracting and transforming features into a suitable format for machine learning models.
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https://amzn.to/3usjyzD
It& #39;s">https://amzn.to/3usjyzD&q... a practical introduction to the fundamental techniques for extracting and transforming features into a suitable format for machine learning models.
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Many people wonder whether it& #39;s a good idea to start their machine learning career as a data analyst.
Absolutely, yes!
People with a strong data background make a killing when they start learning and applying algorithms to that data.
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Absolutely, yes!
People with a strong data background make a killing when they start learning and applying algorithms to that data.
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Yes, you should learn SQL.
I understand that non-relational databases are sexy. I understand that Mary says that they are better, and Johnny thinks nothing is like them.
Don& #39;t listen to them. Go and learn SQL. The "SELECT thing FROM there" type of SQL.
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I understand that non-relational databases are sexy. I understand that Mary says that they are better, and Johnny thinks nothing is like them.
Don& #39;t listen to them. Go and learn SQL. The "SELECT thing FROM there" type of SQL.
↓ 6/7
