Machine Learning with Javascript
Machine Learning with Javascript, available at $109.99, has an average rating of 4.56, with 187 lectures, 2 quizzes, based on 3320 reviews, and has 31162 subscribers.
You will learn about 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 This course is ideal for individuals who are Javascript developers interested in Machine Learning It is particularly useful for Javascript developers interested in Machine Learning.
Enroll now: Machine Learning with Javascript
Summary
Title: Machine Learning with Javascript
Price: $109.99
Average Rating: 4.56
Number of Lectures: 187
Number of Quizzes: 2
Number of Published Lectures: 187
Number of Published Quizzes: 2
Number of Curriculum Items: 189
Number of Published Curriculum Objects: 189
Original Price: $124.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Javascript developers interested in Machine Learning
Target Audiences
- Javascript developers interested in Machine Learning
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:
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Advanced memory profiling to enhance the performance of your algorithms
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Build apps powered by the powerful Tensorflow JS library
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Develop programs that work either in the browser or with Node JS
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Write clean, easy to understand ML code, no one-name variables or confusing functions
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Pick up the basics of Linear Algebraso you can dramatically speed up your code with matrix-based operations. (Don’t worry, I’ll make the math easy!)
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Comprehend how to twist common algorithms to fit your unique use cases
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Plot the results of your analysis using a custom-build graphing library
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Learn performance-enhancing strategies that can be applied to any type of Javascript code
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Data loading techniques, both in the browser and Node JS environments
Course Curriculum
Chapter 1: What is Machine Learning?
Lecture 1: Getting Started – How to Get Help
Lecture 2: Course Resources
Lecture 3: Join Our Community!
Lecture 4: Solving Machine Learning Problems
Lecture 5: A Complete Walkthrough
Lecture 6: App Setup
Lecture 7: Problem Outline
Lecture 8: Identifying Relevant Data
Lecture 9: Dataset Structures
Lecture 10: Recording Observation Data
Lecture 11: What Type of Problem?
Chapter 2: Algorithm Overview
Lecture 1: How K-Nearest Neighbor Works
Lecture 2: Lodash Review
Lecture 3: Implementing KNN
Lecture 4: Finishing KNN Implementation
Lecture 5: Testing the Algorithm
Lecture 6: Interpreting Bad Results
Lecture 7: Test and Training Data
Lecture 8: Randomizing Test Data
Lecture 9: Generalizing KNN
Lecture 10: Gauging Accuracy
Lecture 11: Printing a Report
Lecture 12: Refactoring Accuracy Reporting
Lecture 13: Investigating Optimal K Values
Lecture 14: Updating KNN for Multiple Features
Lecture 15: Multi-Dimensional KNN
Lecture 16: N-Dimension Distance
Lecture 17: Arbitrary Feature Spaces
Lecture 18: Magnitude Offsets in Features
Lecture 19: Feature Normalization
Lecture 20: Normalization with MinMax
Lecture 21: Applying Normalization
Lecture 22: Feature Selection with KNN
Lecture 23: Objective Feature Picking
Lecture 24: Evaluating Different Feature Values
Chapter 3: Onwards to Tensorflow JS!
Lecture 1: Let's Get Our Bearings
Lecture 2: A Plan to Move Forward
Lecture 3: Tensor Shape and Dimension
Lecture 4: Elementwise Operations
Lecture 5: Broadcasting Operations
Lecture 6: Logging Tensor Data
Lecture 7: Tensor Accessors
Lecture 8: Creating Slices of Data
Lecture 9: Tensor Concatenation
Lecture 10: Summing Values Along an Axis
Lecture 11: Massaging Dimensions with ExpandDims
Chapter 4: Applications of Tensorflow
Lecture 1: KNN with Regression
Lecture 2: A Change in Data Structure
Lecture 3: KNN with Tensorflow
Lecture 4: Maintaining Order Relationships
Lecture 5: Sorting Tensors
Lecture 6: Averaging Top Values
Lecture 7: Moving to the Editor
Lecture 8: Loading CSV Data
Lecture 9: Running an Analysis
Lecture 10: Reporting Error Percentages
Lecture 11: Normalization or Standardization?
Lecture 12: Numerical Standardization with Tensorflow
Lecture 13: Applying Standardization
Lecture 14: Debugging Calculations
Lecture 15: What Now?
Chapter 5: Getting Started with Gradient Descent
Lecture 1: Linear Regression
Lecture 2: Why Linear Regression?
Lecture 3: Understanding Gradient Descent
Lecture 4: Guessing Coefficients with MSE
Lecture 5: Observations Around MSE
Lecture 6: Derivatives!
Lecture 7: Gradient Descent in Action
Lecture 8: Quick Breather and Review
Lecture 9: Why a Learning Rate?
Lecture 10: Answering Common Questions
Lecture 11: Gradient Descent with Multiple Terms
Lecture 12: Multiple Terms in Action
Chapter 6: Gradient Descent with Tensorflow
Lecture 1: Project Overview
Lecture 2: Data Loading
Lecture 3: Default Algorithm Options
Lecture 4: Formulating the Training Loop
Lecture 5: Initial Gradient Descent Implementation
Lecture 6: Calculating MSE Slopes
Lecture 7: Updating Coefficients
Lecture 8: Interpreting Results
Lecture 9: Matrix Multiplication
Lecture 10: More on Matrix Multiplication
Lecture 11: Matrix Form of Slope Equations
Lecture 12: Simplification with Matrix Multiplication
Lecture 13: How it All Works Together!
Chapter 7: Increasing Performance with Vectorized Solutions
Lecture 1: Refactoring the Linear Regression Class
Lecture 2: Refactoring to One Equation
Lecture 3: A Few More Changes
Lecture 4: Same Results? Or Not?
Lecture 5: Calculating Model Accuracy
Instructors
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Stephen Grider
Engineering Architect
Rating Distribution
- 1 stars: 23 votes
- 2 stars: 33 votes
- 3 stars: 157 votes
- 4 stars: 992 votes
- 5 stars: 2115 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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