Machine Learning and Deep Learning with JavaScript

About this course

Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.This course takes a step by step approach to teach you how to use JavaScript library, TensorFlow.js for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects. You will then learn how to include a pre-trained model into your own web application to detect human emotions based on pictures and voices. You will also learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Arish Ali started his machine learning journey 5 years ago by winning an all-India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some cutting-edge problems involved in multi-touch attribution modeling, market mix modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at the Bridge School of Management, which along with Northwestern University (SPS) offers a course in