Text classification algorithms are used in a lot of different software systems to help process text data. For example, when you get an email, the email software uses a text classification algorithm to decide whether to put it in your inbox or in your spam folder. It's also how discussion forums know which comments to flag as inappropriate, and how search engines index the web.
We just published a course on the freeCodeCamp.org YouTube channel that will teach you how to classify text using TensorFlow.
This course will give you an introduction to machine learning concepts and neural network implementation using TensorFlow.
Kylie Ying developed this course. Kylie is a current computer science grad student at MIT, working on research in the domain of machine learning and particle physics. She has a YouTube channel focused on programming tutorials and projects, and is passionate about teaching code and inspiring people to pursue STEM.
Kylie explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews.
Here are all the sections covered in this course.
- Colab intro (importing wine dataset)
- What is machine learning?
- Features (inputs)
- Outputs (predictions)
- Anatomy of a dataset
- Assessing performance
- Neural nets
- Colab (feedforward network using diabetes dataset)
- Recurrent neural networks
- Colab (text classification networks using wine dataset)
Watch the full course below or on the freeCodeCamp.org YouTube channel (2-hour watch).