In this project-based course you will learn how to use Tensorflow to construct neural network models. Specifically, we will design, execute, and evaluate a neural network model to help a retail company with their marketing campaign by classifying images of clothing items into 10 different categories. Throughout this course, you will learn how to use Tensorflow to build and analyze neural neural networks that can perform multi-label classification for applications in image recognition. You will also be able to identify and adapt the main components of neural networks as well as evaluate the performance of different models and implement measures to improve their accuracy. At the end of the project, you will be able to design and implement convolutional neural networks helping a retail store with their targeted ad campaign, and the models can be easily adapted for self-driving cars, computer-assisted medical diagnosis, etc. This course is aimed at learners who want to get started with the design and implementation of neural networks with an intuitive and effective approach thanks to the Tensorflow library. Basic familiarity with the Python programming language is required. Among the skills needed to complete this project are: importing libraries, defining variables, arrays, functions, and classes, as well as creating plots using the matplotlib library. Basic familiarity with mathematical vectors and matrices is also required. Computer users with programming experience in Python should be able to complete the project successfully.