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Machine Learning with Javascript

4.6 Filled star Filled star Filled star Filled star Half star
Based on 3,378 ratings
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Stephen Grider

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?

Read more

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

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What's inside

Learning objectives

  • 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

Syllabus

What is Machine Learning?
Getting Started - How to Get Help
Course Resources
Join Our Community!
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Uses Javascript, which allows developers to build single-page applications and browser extensions that run machine learning algorithms, opening up novel use cases
Focuses on the underlying math and programming techniques used in common machine learning algorithms, which helps learners understand how to apply these techniques effectively
Teaches advanced memory profiling to enhance the performance of algorithms, which is a valuable skill for optimizing Javascript code in various applications
Explores the low-level features of Tensorflow JS, which can supercharge algorithms and provide a deeper understanding of machine learning implementation
Covers the basics of Linear Algebra to speed up code with matrix-based operations, with special care to make the math easy to understand for those without a strong math background
Employs Tensorflow JS, which may require learners to familiarize themselves with the library's specific syntax, functions, and updates, potentially adding a learning curve

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Reviews summary

Deep machine learning concepts in javascript

According to learners, this course offers a unique and valuable approach to machine learning by focusing on building algorithms from scratch using Javascript. Students appreciate the instructor's effort to explain complex underlying math and programming techniques, which helps build a deep understanding beyond just using libraries. The hands-on coding and practical applications are frequently highlighted as strengths. While Javascript might be a less conventional language for ML compared to Python, for developers already familiar with JS, it provides an accessible entry point and opens possibilities for browser-based ML projects. Some learners may find the content challenging if they lack prior math or programming fundamentals, reflecting the course's goal of providing a thorough technical dive.
Requires effort but rewarding for some.
"Found the pace just right, although challenging at times."
"Requires a solid grasp of programming fundamentals."
"Definitely not an easy course, but the payoff is worth it."
Appeals to JavaScript developers.
"Great to see ML explained with Javascript, my primary language."
"Easier to grasp coming from a JS background."
"Opens up possibilities for browser-based ML applications."
Provides clear explanation of concepts.
"Was worried about the math, but it was explained well enough for me."
"The math concepts were broken down clearly."
"Helped me understand the theoretical basis behind the algorithms."
Includes hands-on coding and projects.
"Loved the hands-on coding examples throughout the course."
"Building actual applications solidified my learning."
"The practical exercises were very helpful for applying concepts."
Focuses on understanding algorithm mechanics.
"Understanding how algorithms work under the hood was key."
"Building code step-by-step was very insightful, not just using black-box libraries."
"This course goes beyond simply using pre-built libraries."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning with Javascript with these activities:
Review Linear Algebra Fundamentals
Reinforce your understanding of linear algebra concepts, which are crucial for grasping the mathematical underpinnings of many machine learning algorithms covered in the course.
Browse courses on Linear Algebra
Show steps
  • Review matrix operations such as addition, subtraction, and multiplication.
  • Study vector spaces and linear transformations.
  • Practice solving systems of linear equations.
Create a Machine Learning Glossary
Improve your understanding of machine learning terminology by creating a glossary of key terms and concepts covered in the course.
Show steps
  • Identify key terms and concepts from the course materials.
  • Write clear and concise definitions for each term.
  • Organize the glossary alphabetically or by topic.
Review 'Programming Machine Learning: From Coding to Deep Learning'
Gain a broader perspective on machine learning implementation by studying a book that covers similar topics with a different approach.
View Programming Machine Learning on Amazon
Show steps
  • Read the chapters relevant to the algorithms covered in the course.
  • Compare the book's code examples with the course's implementations.
  • Experiment with the book's examples to deepen your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement KNN from Scratch
Reinforce your understanding of the KNN algorithm by implementing it from scratch without relying on external libraries.
Show steps
  • Write a function to calculate the distance between two data points.
  • Implement the KNN algorithm to find the K nearest neighbors.
  • Test the implementation with different datasets and K values.
Create a Blog Post on KNN
Solidify your understanding of the K-Nearest Neighbors algorithm by explaining it in a clear and concise manner for a broader audience.
Show steps
  • Research and gather information about the KNN algorithm.
  • Write a clear and concise explanation of the algorithm.
  • Include examples and visualizations to illustrate the concepts.
  • Publish the blog post on a platform like Medium or your personal website.
Build a Simple Image Classifier
Apply your knowledge of machine learning algorithms and Tensorflow.js to create a practical application that classifies images based on learned patterns.
Show steps
  • Gather a dataset of images for different categories.
  • Preprocess the images and prepare them for training.
  • Implement a convolutional neural network (CNN) using Tensorflow.js.
  • Train the CNN on the image dataset.
  • Evaluate the performance of the trained model.
Review 'Deep Learning with JavaScript'
Expand your knowledge of deep learning with JavaScript by studying a book dedicated to the topic.
View Deep Learning with JavaScript: Neural networks... on Amazon
Show steps
  • Read the chapters on CNNs and RNNs.
  • Experiment with the code examples provided in the book.
  • Apply the concepts learned to your own projects.

Career center

Learners who complete Machine Learning with Javascript will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and maintains machine learning systems. This role requires a strong understanding of algorithms, data structures, and programming languages, including Javascript. This course helps build a foundation in these areas, emphasizing practical application with Javascript. It provides an understanding of how to build ML algorithms from scratch, how to optimize them, and how to work with libraries like TensorflowJS. This background allows a machine learning engineer to troubleshoot, customize, and enhance the performance of existing models, which is a core part of their work. A learner interested in this role should take this course to learn fundamental machine learning concepts and how to implement them with Javascript. This course also teaches how to use the low level features of TensorflowJS to supercharge algorithms and covers advanced memory profiling.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
A Software Engineer develops and maintains software applications. This can involve a variety of code and projects, and a growing trend is the importance of machine learning in software development. This course empowers such engineers to create performant, efficient machine learning models directly within Javascript applications. It teaches the foundations of how machine learning algorithms work and how to build them from scratch, giving a software engineer the knowledge necessary to integrate machine learning into their projects. The focus on optimization and the use of libraries like Tensorflow JS will help the software engineer to build high quality and efficient applications. A learner who wishes to use machine learning within their software projects may find this course particularly useful, as it emphasizes making clean and understandable machine language code.
See salaries and explore the career path for Software Engineer
Data Scientist
A data scientist analyzes complex data sets to discover patterns and insights using machine learning techniques. This course may be useful for a data scientist, as it teaches how to implement machine learning algorithms. It emphasizes understanding the underlying math and programming techniques, which can be helpful in creating new ways of analyzing data. The course's focus on building from scratch, performance optimization, and using TensorflowJS is valuable for data scientists who want a deeper understanding of machine learning. A learner should take this course to better understand the internal operations of machine learning models. This can help them in their work with data sets which can be used to inform business decisions. This course also provides a strong familiarity with the Javascript language and ecosystem.
See salaries and explore the career path for Data Scientist
Web Application Developer
A Web Application Developer builds and maintains client side applications. This course may be helpful for this role, as machine learning becomes more widely used in client applications. This course emphasizes the use of Javascript to implement machine learning techniques, which is directly relevant to the needs of a web developer who wants to integrate machine learning into their front end. The techniques taught in the course, like memory profiling and building performant algorithms, are key for a web application developer who has to provide a user friendly application experience. A web developer may take this course to gain insight into the best practices of developing code which runs well in the browser. This includes working with data, and building single-page applications, which are all topics covered in this course. This course also helps develop the skills to build browser extensions.
See salaries and explore the career path for Web Application Developer
Research Scientist
A research scientist uses scientific methods to collect data, perform analysis, and create new knowledge. This course may be helpful for someone performing research with machine learning techniques. The course emphasizes understanding the algorithms and underlying math used in machine learning, which can help in designing and implementing new machine learning techniques for research. It provides an insight into the best practices of computer science, as well as how to work with scientific data and visualize the results. A research scientist may take this course to better understand how algorithms work and to improve their ability to apply these techniques to novel problems. This course also emphasizes understanding how to tune parameters, and how to understand and use various performance metrics.
See salaries and explore the career path for Research Scientist
Artificial Intelligence Specialist
An Artificial Intelligence Specialist focuses on creating intelligent systems using machine learning. This course may be helpful for this role because it covers building ML algorithms from scratch and using them in practical applications. The course provides a good foundation in understanding the math and programming techniques behind common algorithms, which is essential to understanding how to build AI systems. The focus on building apps with the Tensorflow JS library, which allows for browser based AI applications, may be useful for this role. A learner may take this course to better understand the theoretical and practical aspects of AI implementation. This course also covers ways to optimize performance of these models, which is critical for artificial intelligence applications.
See salaries and explore the career path for Artificial Intelligence Specialist
Data Analyst
A data analyst examines data sets to identify insights. This course may be useful to a data analyst. By exploring machine learning techniques it can give them tools to better understand the data. The course emphasizes understanding algorithms and how they work internally. This is particularly useful, as data analysts often rely on these techniques for large data sets. The course also goes into how to prepare data and visualize it for better analysis, and how to use it in TensorflowJS. A data analyst may find this course useful to improve their ability to analyze data and to better interpret the output they get when using machine learning. This course will also give them the ability to use the same techniques in their Javascript applications.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
A Quantitative Analyst, also known as a quant, uses mathematical and statistical methods to analyze financial markets and create trading strategies. This course may be useful to a quantitative analyst, as it introduces the use of machine learning techniques to develop financial models and to work with financial data. The course's focus on mathematical applications, like linear algebra, may be particularly useful. This course may be helpful as the use of machine learning techniques becomes more common in the financial sector. A quantitative analyst should take this course to learn how to integrate machine learning into their models. This course also teaches how to work with data and how to perform analysis.
See salaries and explore the career path for Quantitative Analyst
Data Visualization Specialist
A Data Visualization Specialist creates graphical representations of data to help people understand complex information. While this course is not directly focused on data visualization, it can help a data visualization specialist understand the underlying data they work with, and how to prepare it for visualization. This course may also help them to build applications that use machine learning outputs to generate insightful visualizations. This course also teaches how to plot results using a custom-built graphing library, which may help a data visualization specialist to develop new ways of presenting their data. This course can also improve their ability to interpret their visualizations. A data visualization specialist may find this course useful for a new way of understanding the data that they represent. This course also reviews how to load data and process it.
See salaries and explore the career path for Data Visualization Specialist
Algorithm Developer
An Algorithm Developer creates new algorithms or modifies existing ones to perform specific tasks. This course may be helpful for someone interested in this role. The course emphasizes the understanding of the math and programming behind common algorithms, and also how to implement them from scratch. This is important for those who want to create new algorithms. This course is also useful to understand how to implement and test algorithms. The course also teaches how to work with TensorflowJS to improve algorithm performance. An algorithm developer should take this course to better understand how to build and optimize their code. This course also focuses on developing programs that work both in the browser and in Node JS.
See salaries and explore the career path for Algorithm Developer
Product Manager
A product manager is responsible for the strategy, roadmap, and execution of a product. This course may be useful for a product manager because it provides a high level understanding of the capabilities of Machine Learning. This will help them to better design and plan the next generation of products, as more and more products make use of machine learning techniques. This course teaches machine learning best practices, and highlights the issues that engineers consider when planning and implementing them. This may allow a product manager to have more informed discussions with their engineers. A product manager may take this course to better understand the landscape of machine learning applications, and to guide their product strategy. This course focuses on the applications of machine learning.
See salaries and explore the career path for Product Manager
Technical Writer
A technical writer creates documentation for technical products. This course may be helpful to a technical writer who wants to produce documentation for machine learning systems. By taking this course, a technical writer can get insight into how machine learning algorithms work, and how to use libraries like TensorflowJS. They can use this knowledge to better understand and document these systems for others. Since this course focuses on Javascript and TensorflowJS, a technical writer can also focus on documenting software applications which make use of these technologies. A technical writer may find this course useful to better understand the subject matter they will be writing about. This course also emphasizes clean and understandable code, which is an important part of documentation.
See salaries and explore the career path for Technical Writer
Systems Analyst
A Systems Analyst studies an organization’s computer systems and procedures and designs solutions to improve efficiency. This course may be useful for this role, as machine learning becomes more integrated into computer systems. The course emphasizes how to implement machine learning techniques and how to build applications that make use of these techniques. This course can help a system analyst to understand the performance implications of integrating machine learning into a system. A systems analyst may take this course to analyze the impact of machine learning on the organizations that they analyze. The course also covers the performance implications of how to use the TensorflowJS library.
See salaries and explore the career path for Systems Analyst
Computer Science Professor
A Computer Science Professor teaches computer science concepts and how to apply them. This course may be useful for professors who want a better understanding of machine learning. The course emphasizes understanding common algorithms and how to implement them from scratch. It also focuses on the specific best practices that are relevant to Javascript and TensorflowJS. A professor should take this course to better understand how to teach these concepts. This course also emphasizes how to explain complex topics, and how to provide clear examples of complex algorithms. This course also presents clear math explanations.
See salaries and explore the career path for Computer Science Professor
Statistician
A Statistician is responsible for applying statistical theories and methods to collect, interpret, and summarize data. This course may be helpful for a statistician since machine learning can be considered applied statistics. This course covers the underlying math and programming techniques that are used in machine learning. This knowledge can be helpful in developing new statistical methods, or in understanding an unfamiliar dataset. The course also discusses how to perform data analysis and how to work with data in Javascript and TensorflowJS. A statistician may find this course useful to implement new statistical methods, and apply them to a wider range of data. This course also emphasizes how to use statistical concepts in practical applications.
See salaries and explore the career path for Statistician

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning with Javascript.
Cover image
Deep Learning with JavaScript
Save
Provides a comprehensive guide to deep learning using JavaScript and TensorFlow.js. It covers various deep learning models and techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). It is particularly useful for understanding how to implement deep learning algorithms in a web browser environment. This book expands on the course material by providing more in-depth coverage of deep learning concepts and their practical applications.
Deep Learning with JavaScript: Neural networks in...
Paperback
$$
Cover image
Programming Machine Learning
Save
Provides a practical, hands-on approach to machine learning, complementing the course's focus on building algorithms from scratch. It covers a range of ML techniques and provides code examples. It is useful for solidifying your understanding of the practical aspects of implementing machine learning algorithms. This book good reference for those who want to see how ML concepts translate into real-world code.
Programming Machine Learning
Paperback
Check
Programming Machine Learning
Kindle Edition
Check

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Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
17.5 total hours
Via
Udemy
Instructor
Stephen Grider
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Uses Javascript, which allows developers to build single-page applications and browser extensions that run machine learning algorithms, opening up novel use cases
Focuses on the underlying math and programming techniques used in common machine learning algorithms, which helps learners understand how to apply these techniques effectively
Teaches advanced memory profiling to enhance the performance of algorithms, which is a valuable skill for optimizing Javascript code in various applications
Explores the low-level features of Tensorflow JS, which can supercharge algorithms and provide a deeper understanding of machine learning implementation
Covers the basics of Linear Algebra to speed up code with matrix-based operations, with special care to make the math easy to understand for those without a strong math background
Employs Tensorflow JS, which may require learners to familiarize themselves with the library's specific syntax, functions, and updates, potentially adding a learning curve
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