TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for classification. Finally, you'll discover how to implement more powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, you'll have a better understanding of how to build classification models with TensorFlow.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi. My name is Janani Ravi, and welcome to this course on Building Classification Models in TensorFlow. I'll introduce myself. I have a Masters in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on a real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. This course covers the fine points of classification models. We'll start by understanding how classification works and the metrics to use to evaluate classifiers, such as accuracy, position, and recall. We start off with basic machine learning models for classification, such as the logistic regression and nearest neighbor methods and implement them using TensorFlow's Python APIs. TensorFlow has a great library to implement powerful classification models using Convolutional Neural Networks and Recurrent Neural Networks. Convolutional Neural Networks, or CNNs, are a class of deep feet forward artificial neural network that have successfully been applied to analyzing visual imagery. CNNs are widely used in image and video recognition. Recurrent Neural Networks, or RNNs, are a versatile and powerful form of neural network fast gaining popularity in applications that need to consider context. RNNs are ideal for considering sequences of data, frames in a movie, sentences in a paragraph, or stock returns in a period. This course will cover all these techniques in detail.