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30-Day Money-Back Guarantee

This course includes:

  • 17.5 hours on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Machine Learning

Machine Learning with Javascript

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.
Rating: 4.6 out of 54.6 (2,323 ratings)
22,294 students
Created by Stephen Grider
Last updated 1/2021
English
English [Auto], Indonesian [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Assemble machine learning algorithms from scratch!
  • Build interesting applications using Javascript and ML techniques
  • Understand how ML works without relying on mysterious libraries
  • Optimize your algorithms with advanced performance and memory usage profiling
  • Use the low-level features of Tensorflow JS to supercharge your algorithms
  • Grow a strong intuition of ML best practices
Curated for the Udemy for Business collection

Course content

15 sections • 185 lectures • 17h 39m total length

  • Preview00:57
  • Preview06:04
  • A Complete Walkthrough
    09:53
  • App Setup
    02:01
  • Problem Outline
    02:53
  • Preview04:11
  • Preview05:47
  • Recording Observation Data
    03:59
  • What Type of Problem?
    04:35

  • How K-Nearest Neighbor Works
    08:23
  • Lodash Review
    09:56
  • Preview07:16
  • Finishing KNN Implementation
    05:53
  • Preview04:47
  • Interpreting Bad Results
    04:12
  • Test and Training Data
    04:05
  • Randomizing Test Data
    03:48
  • Preview03:41
  • Gauging Accuracy
    05:18
  • Preview03:29
  • Refactoring Accuracy Reporting
    05:13
  • Investigating Optimal K Values
    11:38
  • Updating KNN for Multiple Features
    06:36
  • Multi-Dimensional KNN
    03:56
  • N-Dimension Distance
    09:50
  • Arbitrary Feature Spaces
    08:27
  • Magnitude Offsets in Features
    05:36
  • Feature Normalization
    07:32
  • Normalization with MinMax
    07:14
  • Applying Normalization
    04:22
  • Feature Selection with KNN
    07:47
  • Objective Feature Picking
    06:10
  • Evaluating Different Feature Values
    02:53

  • Let's Get Our Bearings
    07:27
  • A Plan to Move Forward
    04:31
  • Tensor Shape and Dimension
    12:04
  • Tensor Dimension and Shapes
    9 questions
  • Elementwise Operations
    08:18
  • Broadcasting Operations
    06:47
  • Broadcasting Elementwise Operations
    3 questions
  • Logging Tensor Data
    03:47
  • Tensor Accessors
    05:24
  • Creating Slices of Data
    07:46
  • Tensor Concatenation
    05:28
  • Summing Values Along an Axis
    05:13
  • Massaging Dimensions with ExpandDims
    07:47

  • KNN with Regression
    04:56
  • A Change in Data Structure
    04:04
  • KNN with Tensorflow
    09:18
  • Maintaining Order Relationships
    06:30
  • Sorting Tensors
    08:00
  • Averaging Top Values
    07:43
  • Moving to the Editor
    03:26
  • Loading CSV Data
    10:10
  • Running an Analysis
    06:10
  • Reporting Error Percentages
    06:26
  • Normalization or Standardization?
    07:33
  • Numerical Standardization with Tensorflow
    07:37
  • Applying Standardization
    04:01
  • Debugging Calculations
    08:14
  • What Now?
    04:00

  • Linear Regression
    02:39
  • Why Linear Regression?
    04:52
  • Understanding Gradient Descent
    13:04
  • Guessing Coefficients with MSE
    10:19
  • Observations Around MSE
    05:56
  • Derivatives!
    07:12
  • Gradient Descent in Action
    11:46
  • Quick Breather and Review
    05:46
  • Why a Learning Rate?
    17:05
  • Answering Common Questions
    03:48
  • Gradient Descent with Multiple Terms
    04:43
  • Multiple Terms in Action
    10:39

  • Project Overview
    06:01
  • Data Loading
    05:17
  • Default Algorithm Options
    08:32
  • Formulating the Training Loop
    03:18
  • Initial Gradient Descent Implementation
    09:24
  • Calculating MSE Slopes
    06:52
  • Updating Coefficients
    03:11
  • Interpreting Results
    10:07
  • Matrix Multiplication
    07:09
  • More on Matrix Multiplication
    06:40
  • Matrix Form of Slope Equations
    06:21
  • Simplification with Matrix Multiplication
    09:28
  • How it All Works Together!
    14:01

  • Refactoring the Linear Regression Class
    07:40
  • Refactoring to One Equation
    08:58
  • A Few More Changes
    06:13
  • Same Results? Or Not?
    03:19
  • Calculating Model Accuracy
    08:37
  • Implementing Coefficient of Determination
    07:44
  • Dealing with Bad Accuracy
    07:47
  • Reminder on Standardization
    04:36
  • Data Processing in a Helper Method
    03:38
  • Reapplying Standardization
    05:57
  • Fixing Standardization Issues
    05:36
  • Massaging Learning Rates
    03:15
  • Moving Towards Multivariate Regression
    11:44
  • Refactoring for Multivariate Analysis
    07:28
  • Learning Rate Optimization
    08:04
  • Recording MSE History
    05:21
  • Updating Learning Rate
    06:41

  • Observing Changing Learning Rate and MSE
    04:17
  • Plotting MSE Values
    05:21
  • Plotting MSE History against B Values
    04:22

  • Batch and Stochastic Gradient Descent
    07:17
  • Refactoring Towards Batch Gradient Descent
    05:06
  • Determining Batch Size and Quantity
    06:02
  • Iterating Over Batches
    07:48
  • Evaluating Batch Gradient Descent Results
    05:41
  • Making Predictions with the Model
    07:37

  • Introducing Logistic Regression
    02:27
  • Logistic Regression in Action
    06:31
  • Bad Equation Fits
    05:31
  • The Sigmoid Equation
    04:31
  • Decision Boundaries
    07:47
  • Changes for Logistic Regression
    01:11
  • Project Setup for Logistic Regression
    05:51
  • Project Download
    00:10
  • Importing Vehicle Data
    04:27
  • Encoding Label Values
    04:18
  • Updating Linear Regression for Logistic Regression
    07:08
  • The Sigmoid Equation with Logistic Regression
    04:27
  • A Touch More Refactoring
    07:46
  • Gauging Classification Accuracy
    03:27
  • Implementing a Test Function
    05:16
  • Variable Decision Boundaries
    07:16
  • Mean Squared Error vs Cross Entropy
    05:46
  • Refactoring with Cross Entropy
    05:08
  • Finishing the Cost Refactor
    04:36
  • Plotting Changing Cost History
    03:24

Requirements

  • Basic understanding of terminal and command line usage
  • Ability to read basic math equations

Description

If you're here, you already know the truth: Machine Learning is the future of everything.

In the coming years, there won't be a single industry in the world untouched by Machine Learning.  A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change.  You probably already use apps many times each day that rely upon Machine Learning techniques.  So why stay in the dark any longer?

There are many courses on Machine Learning already available.  I built this course to be the best introduction to the topic.  No subject is left untouched, and we never leave any area in the dark.  If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.


A common question - Why Javascript?  I thought ML was all about Python and R?

The answer is simple - ML with Javascript is just plain easier to learn than with Python.  Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'.  A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic.

Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build.  Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case!


Does this course focus on algorithms, or math, or Tensorflow, or what?!?!

Let's be honest - the vast majority of ML courses available online dance around the confusing topics.  They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you.  Although this can lead you to quick successes, in the end it will hamper your ability to understand ML.  You can only understand how to apply ML techniques if you understand the underlying algorithms.

That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms.  Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.

Don't have a background in math?  That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on.


A short list of what you will learn:

  • Advanced memory profiling to enhance the performance of your algorithms

  • Build apps powered by the powerful Tensorflow JS library

  • Develop programs that work either in the browser or with Node JS

  • Write clean, easy to understand ML code, no one-name variables or confusing functions

  • Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)

  • Comprehend how to twist common algorithms to fit your unique use cases

  • Plot the results of your analysis using a custom-build graphing library

  • Learn performance-enhancing strategies that can be applied to any type of Javascript code

  • Data loading techniques, both in the browser and Node JS environments

Who this course is for:

  • Javascript developers interested in Machine Learning

Featured review

Daniel Kukuła
Daniel Kukuła
433 courses
28 reviews
Rating: 5.0 out of 5a year ago
If you want to learn about machine learning then this is the course to go. It uses tensorflow.js but it doesn't really matter - the theory is the same and with thiss explanation it finally clicked in my head. Stephen - we want deep neural networks now !!!!!

Instructor

Stephen Grider
Engineering Architect
Stephen Grider
  • 4.7 Instructor Rating
  • 278,813 Reviews
  • 733,523 Students
  • 29 Courses

Stephen Grider has been building complex Javascript front ends for top corporations in the San Francisco Bay Area.  With an innate ability to simplify complex topics, Stephen has been mentoring engineers beginning their careers in software development for years, and has now expanded that experience onto Udemy, authoring the highest rated React course. He teaches on Udemy to share the knowledge he has gained with other software engineers.  Invest in yourself by learning from Stephen's published courses.

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