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Home » Technology » AI / Machine Learning » Machine Learning with JavaScript

Course Skill Level:

Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    MLJAVSL21E09

Who should attend & recommended skills:

Those with basic IT, machine learning, and Linux skills

Who should attend & recommended skills

  • This course is geared for those seeking a definitive guide to creating an intelligent web application with the best of machine learning and JavaScript.
  • Skill-level: Foundation-level Machine Learning with JavaScript skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with JavaScript expert instructor, students will learn about and explore:
  • Solving complex computational problems in browser with JavaScript
  • Teaching your browser how to learn from rules using the power of machine learning
  • Discoveries on web interface and API in machine learning
  • Getting an overview of state-of-the-art machine learning
  • The pre-processing of data handling, cleaning, and preparation
  • Mining and Pattern Extraction with JavaScript
  • Building your own model for classification, clustering, and prediction
  • Identifying the most appropriate model for each type of problem
  • Applying machine learning techniques to real-world applications
  • How JavaScript can be a powerful language for machine learning

Course breakdown / modules

  • 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

  • An overview
  • Feature identification
  • Cleaning and preparing data

  • Introduction to machine learning
  • Types of learning
  • Categories of 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

  • k-Nearest Neighbor
  • Naive Bayes classifier
  • Support Vector Machine
  • Random forest

  • The mathematical perspective
  • The algorithmic perspective
  • Association rule applications
  • Example – retail data

  • Regression versus classification
  • Regression basics
  • Example 1 – linear regression
  • Example 2 – exponential regression
  • Example 3 – polynomial regression
  • Other time-series analysis techniques

  • Conceptual overview of neural networks
  • Backpropagation training
  • Example – XOR in TensorFlow.js

  • Convolutional Neural Networks
  • Recurrent neural networks

  • String distance
  • Term frequency – inverse document frequency
  • Tokenizing
  • Stemming
  • Phonetics
  • Part of speech tagging
  • Word embedding and neural networks

  • Serializing models
  • Data pipelines

  • Mode of learning
  • The task at hand
  • Format, form, input, and output
  • Available resources
  • When it goes wrong
  • Combining models

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