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Can a machine distinguish between a honey bee and a bumble bee? Being able to identify bee species from images, while challenging, would allow researchers to more quickly and effectively collect field data. In this project, you will build a simple deep learning model that can automatically detect honey bees and bumble bees, then load a pre-trained model for evaluation. You will use [keras](https://keras.io/), [scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html), [scikit-image](http://scikit-image.org/docs/stable/), and [numpy](https://docs.scipy.org/doc/numpy-1.14.2/reference/), among other popular Python libraries. This project is the third part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision. Before taking this project, it will help to have completed [Naïve Bees: Image Loading and Processing](https://www.datacamp.com/projects/374) and [Naïve Bees: Predict Species from Images](https://www.datacamp.com/projects/412).
- 1Import Python libraries
- 2Load image labels
- 3Examine RGB values in an image matrix
- 4Importing the image data
- 5Split into train, test, and evaluation sets
- 6Normalize image data
- 7Model building (part i)
- 8Model building (part ii)
- 9Compile and train model
- 10Load pre-trained model and score
- 11Visualize model training history
- 12Generate predictions
Data Scientist at DrivenData
Emily Miller is a Data Scientist at DrivenData. Her personal passion is to combine data science with satellite imagery, text data, and other non-traditional data sources to make poverty alleviation efforts more effective. She was previously a Data Scientist at the Bill & Melinda Gates Foundation and a Data Science Fellow at Metis.
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