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Machine Learning with JavaScript

  • Course Code: Artificial Intelligence - Machine Learning with JavaScript
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants a definitive guide to creating an intelligent web application with the best of machine learning and JavaScript.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning with JavaScript skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants a definitive guide to creating an intelligent web application with the best of machine learning and JavaScript. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this course. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this course, you’ll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. 

Working in a hands-on learning environment, led by our Machine Learning with JavaScript expert instructor, students will learn about and explore: 

  • Solve complex computational problems in browser with JavaScript 
  • Teach your browser how to learn from rules using the power of machine learning 
  • Understand discoveries on web interface and API in machine learning 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get an overview of state-of-the-art machine learning 
  • Understand the pre-processing of data handling, cleaning, and preparation 
  • Learn Mining and Pattern Extraction with JavaScript 
  • Build your own model for classification, clustering, and prediction 
  • Identify the most appropriate model for each type of problem 
  • Apply machine learning techniques to real-world applications 
  • Learn how JavaScript can be a powerful language for machine learning 

Audience & Pre-Requisites 

This course is geared for attendees who wish a definitive guide to creating an intelligent web application with the best of machine learning and JavaScript. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Exploring the Potential of JavaScript 
  • Exploring the Potential of JavaScript 
  • Why JavaScript? 
  • Why machine learning, why now? 
  • Advantages and challenges of JavaScript 
  • The CommonJS initiative 
  • Node.js 
  • TypeScript language 
  • Improvements in ES6 
  • Preparing the development environment 
  1. Data Exploration 
  • Data Exploration 
  • An overview 
  • Feature identification 
  • Cleaning and preparing data 
  1. Tour of Machine Learning Algorithms 
  • Tour of Machine Learning Algorithms 
  • Introduction to machine learning 
  • Types of learning 
  • Categories of algorithms 
  1. Grouping with Clustering Algorithms 
  • Grouping with Clustering Algorithms 
  • Average and distance 
  • Writing the k-means algorithm 
  • Example 1 – k-means on simple 2D data 
  • Example 2 – 3D data 
  • k-means where k is unknown 
  1. Classification Algorithms 
  • Classification Algorithms 
  • k-Nearest Neighbor 
  • Naive Bayes classifier 
  • Support Vector Machine 
  • Random forest 
  1. Association Rule Algorithms 
  • Association Rule Algorithms 
  • The mathematical perspective 
  • The algorithmic perspective 
  • Association rule applications 
  • Example – retail data 
  1. Forecasting with Regression Algorithms 
  • Forecasting with Regression Algorithms 
  • Regression versus classification 
  • Regression basics 
  • Example 1 – linear regression 
  • Example 2 – exponential regression 
  • Example 3 – polynomial regression 
  • Other time-series analysis techniques 
  1. Artificial Neural Network Algorithms 
  • Artificial Neural Network Algorithms 
  • Conceptual overview of neural networks 
  • Backpropagation training 
  • Example – XOR in TensorFlow.js 
  1. Deep Neural Networks 
  • Deep Neural Networks 
  • Convolutional Neural Networks 
  • Recurrent neural networks 
  1. Natural Language Processing in Practice 
  • Natural Language Processing in Practice 
  • String distance 
  • Term frequency – inverse document frequency 
  • Tokenizing 
  • Stemming 
  • Phonetics 
  • Part of speech tagging 
  • Word embedding and neural networks 
  1. Using Machine Learning in Real-Time Applications 
  • Using Machine Learning in Real-Time Applications 
  • Serializing models 
  • Data pipelines 
  1. Choosing the Best Algorithm for Your Application 
  • Choosing the Best Algorithm for Your Application 
  • Mode of learning 
  • The task at hand 
  • Format, form, input, and output 
  • Available resources 
  • When it goes wrong 
  • Combining models 
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