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Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Learn the most important language for data science.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- Others
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Learn SQL for working with databases, using Google BigQuery to scale to massive datasets.
- Subjects:
- Computing
- Keywords:
- Database management SQL (Computer program language)
- Resource Type:
- Others
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Others
Distinguish yourself by learning to work with text data.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Natural language processing (Computer science)
- Resource Type:
- Others
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Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Discover the most effective way to improve your models.
- Subjects:
- Computing
- Keywords:
- Machine learning Data mining Python (Computer program language)
- Resource Type:
- Others
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Solve short hands-on challenges to perfect your data manipulation skills.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Electronic data processing Information visualization
- Resource Type:
- Others
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Projections of commercial airline flight schedules worldwide. Describe characteristics of each flight such as: departure and arrival airports, flight times, carrier, fares, capacity, and more. Projections are made at the beginning of every time period (month or year) and project the schedules for that time period until the next update is received. Data from 1979-March 1987 are available monthly. Data from 1987 onward are available annually.
- Subjects:
- Aeronautical and Aviation Engineering
- Keywords:
- Airlines Aeronautics Aeronautics -- Flights
- Resource Type:
- Others
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This work introduces a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019.
- Subjects:
- Computing
- Keywords:
- Optical pattern recognition
- Resource Type:
- Others
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Others
This package contains: 1. SUFR-W, a dataset of “in the wild” natural images of faces gathered from the internet. The protocol used to create the dataset is described in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition 2. The full set of SUFR synthetic datasets, called the “Subtasks of Unconstrained Face Recognition Challenge” in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition
- Subjects:
- Computing
- Keywords:
- Human face recognition (Computer science) Optical pattern recognition
- Resource Type:
- Others
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