The goal of this project is to introduce beginners to the basic concepts of machine learning using TensorFlow. The project will include, how to set up the tool and get started as well as understanding the fundamentals of machine learning/neural network model and its key concepts. Learning how to use TensorFlow for implementing machine learning algorithms, data preprocessing, supervised learning. Additionally, learners develop skills in evaluating and deploying machine learning models using TensorFlow.
Overview
Syllabus
- Project Overview
- In this project, you'll embark on an exciting journey of building a machine learning model to classify images of cats and dogs using the power of TensorFlow. This is a beginner project-based course which should take approximately 2 hours to finish. Image classification is a fundamental problem in computer vision, and with the popularity of deep learning, it has become even more accessible and accurate. From preprocessing a dataset containing labeled images of cats and dogs to dive into building a convolutional neural network (CNN), this project will provide you with insights into the process of building, training, and evaluating a convolutional neural network for image classification, while also giving you a deeper understanding of TensorFlow's capabilities. Furthermore, you'll explore the importance of hyperparameters such as learning rate, batch size, and the number of epochs and implement techniques like data augmentation and dropout to enhance your model's ability to generalize well to new data. This course is aimed at learners who are looking to get started with their deep learning journey with TensorFlow.
Taught by
Soumava Dey
