Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasets, stock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.

The human face has been a topic of interest for deep learning engineers for quite some time now. Understanding the human face not only helps in facial recognition but finds applications in facial morphing, head pose detection and virtual makeovers. If you are a regular user of social media apps like Instagram or Snapchat, have you wondered how the filters fit perfectly for each face? Though every face on the planet is unique, these filters seem to magically align on your nose, lips and eyes. These filters or face-swapping applications make use of facial landmarks.