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1041 reviews on Udemy

Machine Learning with Javascript

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.
Instructor:
Stephen Grider
12,486 students enrolled
English [Auto-generated] More
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

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 questionWhy 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

What is Machine Learning?

1
Getting Started - How to Get Help
2
Solving Machine Learning Problems
3
A Complete Walkthrough
4
App Setup
5
Problem Outline
6
Identifying Relevant Data
7
Dataset Structures
8
Recording Observation Data
9
What Type of Problem?

Algorithm Overview

1
How K-Nearest Neighbor Works
2
Lodash Review
3
Implementing KNN
4
Finishing KNN Implementation
5
Testing the Algorithm
6
Interpreting Bad Results
7
Test and Training Data
8
Randomizing Test Data
9
Generalizing KNN
10
Gauging Accuracy
11
Printing a Report
12
Refactoring Accuracy Reporting
13
Investigating Optimal K Values
14
Updating KNN for Multiple Features
15
Multi-Dimensional KNN
16
N-Dimension Distance
17
Arbitrary Feature Spaces
18
Magnitude Offsets in Features
19
Feature Normalization
20
Normalization with MinMax
21
Applying Normalization
22
Feature Selection with KNN
23
Objective Feature Picking
24
Evaluating Different Feature Values

Onwards to Tensorflow JS!

1
Let's Get Our Bearings
2
A Plan to Move Forward
3
Tensor Shape and Dimension
4
Tensor Dimension and Shapes
5
Elementwise Operations
6
Broadcasting Operations
7
Broadcasting Elementwise Operations
8
Logging Tensor Data
9
Tensor Accessors
10
Creating Slices of Data
11
Tensor Concatenation
12
Summing Values Along an Axis
13
Massaging Dimensions with ExpandDims

Applications of Tensorflow

1
KNN with Regression
2
A Change in Data Structure
3
KNN with Tensorflow
4
Maintaining Order Relationships
5
Sorting Tensors
6
Averaging Top Values
7
Moving to the Editor
8
Loading CSV Data
9
Running an Analysis
10
Reporting Error Percentages
11
Normalization or Standardization?
12
Numerical Standardization with Tensorflow
13
Applying Standardization
14
Debugging Calculations
15
What Now?

Getting Started with Gradient Descent

1
Linear Regression
2
Why Linear Regression?
3
Understanding Gradient Descent
4
Guessing Coefficients with MSE
5
Observations Around MSE
6
Derivatives!
7
Gradient Descent in Action
8
Quick Breather and Review
9
Why a Learning Rate?
10
Answering Common Questions
11
Gradient Descent with Multiple Terms
12
Multiple Terms in Action

Gradient Descent with Tensorflow

1
Project Overview
2
Data Loading
3
Default Algorithm Options
4
Formulating the Training Loop
5
Initial Gradient Descent Implementation
6
Calculating MSE Slopes
7
Updating Coefficients
8
Interpreting Results
9
Matrix Multiplication
10
More on Matrix Multiplication
11
Matrix Form of Slope Equations
12
Simplification with Matrix Multiplication
13
How it All Works Together!

Increasing Performance with Vectorized Solutions

1
Refactoring the Linear Regression Class
2
Refactoring to One Equation
3
A Few More Changes
4
Same Results? Or Not?
5
Calculating Model Accuracy
6
Implementing Coefficient of Determination
7
Dealing with Bad Accuracy
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