Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

Overview

This course aims to introduce learners to machine learning concepts and neural network implementation using Python and TensorFlow. By the end of the course, students will be able to understand classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. They will also learn how to implement a feedforward neural network to predict outcomes like diabetes and use different neural net architectures for text classification. The teaching method includes video lectures and hands-on practice using Colab notebooks. This course is intended for individuals interested in starting their journey in machine learning and neural networks.

Syllabus

) Introduction.
) Colab intro (importing wine dataset).
) What is machine learning?.
) Features (inputs).
) Outputs (predictions).
) Anatomy of a dataset.
) Assessing performance.
) Neural nets.
) Tensorflow.
) Colab (feedforward network using diabetes dataset).
) Recurrent neural networks.
) Colab (text classification networks using wine dataset).