This course will build on concepts learnt in the Intro to Python and Data Science course.
You will start with an overview of Machine Learning concepts using scikit-learn and learn techniques such as Linear Regression, Classification using nearest neighbors, random forests and bayesian models and Clustering using k-means clustering. You will also learn some preprocessing, dimensionality reduction and testing and validation techniques before diving into deep learning.
PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models.
By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch.
You will learn how to work with Tensors, build neural networks from scratch, build complex neural models for image recognition, use pretrained models for solving Computer Vision problems and use style transfer to build sophisticated AI models. This course will introduce you to the fundamental ideas behind Convolutional Neural networks(CNN) and Recurrent neural networks(RNN), This course will take an application oriented approach that will help you build models to solve real world problems and you will demonstrate this learning by a capstone project.