Categories
- Data Engineering
- Data Analysis
- Modeling
- Algorithms
- Implementation and Operations
Datasets
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Datasets: A collection of data used as the “fuel” for our machine learning models. -
Features: describe information about your data set in a columnar way. -
Observations: describing your data as each instance of a set of features.
Dataset Types
- Datasets can be comma separated or JSON
- Datasets can also be images, audio, or video
Ways to organize data
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Structured data: has a schema- Example: table of values such as a relational database table
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Unstructured data: doesn’t have a schema- Example: PDFs, images, video, audio, logs, tweets
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Semi-structured data: contains tags to separate semantic elements and enforce hierarchies- Examples: CSV, JSON, XML
Labeled/Unlabeled Data
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Labeled data: Has a target attribute -
Unlabeled data: No target attribute
Supervised/Unsupervised Learning
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Supervised Learning: Mostly used with labeled data -
Unsupervised Learning: Mostly used with unlabeled data
Categorical/Continuous Features
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Categorical features- Made up of a set of categories
- They describe the quality not quantity
- They are distinct
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Continuous features- They describe the quantity not the quality
Corpus Data
- Data collected from text
- Used in Natural Language Processing (NLP)
- Speech recognition, text-to-speech
Ground Truth Data
Data that has been labeled either by human labelers or by machine learning algorithms.
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