by Arish Ali | Released February 2019 | ISBN: 9781789613360

Build intelligent web applications by bringing machine learning to the browserAbout This VideoGet acquainted with machine learning capabilities using JavaScript and understand the JavaScript Machine Learning ecosystemWork with various powerful machine learning algorithms with this practical course to develop an intuitive understanding of the machine learning worldBuild and train different machine learning models on the browser or a node js serverIn DetailMachine Learning is a growing and in-demand skill 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. This course shows you various machine learning techniques in a practical way and helps you implement them using the JavaScript language.Hands-On Machine Learning using JavaScript gives you the opportunity to use the power of machine learning (without installing additional software on the customer's computer) and make them feel safe as the data resides in the system. This course covers basic as well as advanced topics in Machine Learning and gives a holistic picture of the JavaScript machine learning ecosystem by making use of libraries to design smarter applications.By the end of this course, you'll have gained hands-on experience in evaluating and implementing the right model using the power of JavaScript.Code files for the course can be found here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-using-JavaScriptDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Getting ready!
   • The Course Overview 00:01:20
   • Introduction to Machine Learning 00:06:36
   • Tour of the JavaScript Machine Learning Landscape 00:04:04
   • Setting Up Our Machine Learning Environment 00:07:57
2. Chapter 2 : Diving Headfirst into Supervised Learning
   • Understand Regression with Linear Regression 00:06:36
   • Understanding How Linear Regression Works 00:05:24
   • Predicting Salaries after College Using Linear Regression 00:04:10
   • Understand Classification with Logistic Regression 00:04:11
   • Classifying Clothes Using Logistic Regression 00:06:09
3. Chapter 3 : Improving Models
   • Model Evaluation 00:03:31
   • Better Measures than Accuracy 00:08:00
   • Understanding the Results 00:02:20
   • Improving the Models 00:02:56
4. Chapter 4 : Using Support Vector Machine and Random Forests for Complex Problems
   • What are Support Vector Machines? 00:02:22
   • Using SVM Kernels to Transform Problems 00:01:42
   • Image Classifier Using SVM 00:08:35
   • Making Better Decision with Decision Trees 00:07:59
   • Combining Decision Trees to Make Better Predictions 00:02:35
   • Predicting Customer Churn Using Random Forests 00:05:33
5. Chapter 5 : Finding Hidden Value in Unlabeled Data
   • Introduction and Advantage of Unsupervised Learning 00:02:40
   • Grouping Unlabeled Data in Meaningful Ways Using K-means Clustering 00:05:03
   • Using Principal Component Analysis to Speed-up Machine Learning Algorithms 00:04:25
   • Analyzing Plant Species Using K-means Clustering 00:03:12
6. Chapter 6 : Deep Neural Networks
   • Introduction to Neural Networks 00:01:47
   • How a Neural Network Works 00:06:19
   • Neural Networks in Tensorflow.js 00:04:15
   • Multiclass Classification Using TensorFlow.js 00:04:50
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