Machine Learning with Javascript

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.

4.79 (3432 reviews)

Udemy

platform

English

language

Data Science

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

Course Gallery

Machine Learning with Javascript – Screenshot 1 — Screenshot 1 – Machine Learning with Javascript

Machine Learning with Javascript – Screenshot 2 — Screenshot 2 – Machine Learning with Javascript

Machine Learning with Javascript – Screenshot 3 — Screenshot 3 – Machine Learning with Javascript

Machine Learning with Javascript – Screenshot 4 — Screenshot 4 – Machine Learning with Javascript

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Comidoc Review

Our Verdict

Machine Learning with Javascript offers solid foundational knowledge in machine learning concepts and techniques. Despite the instructor's clear teaching style, up-to-date course content and consistent updates are crucial to ensure a quality learning experience. While some learners will find this course suitable for their background, others might struggle due to the reliance on Tensorflow 1.x and lack of up-to-date content. To maximize the benefits from this course, learners should ideally have a solid understanding of calculus, linear algebra, and statistics.

What We Liked

Instructor explains machine learning concepts clearly with ample examples and charts, making the course accessible to different learning styles
The course effectively breaks down complex topics into manageable sections, allowing learners to grasp fundamental principles before moving on to more advanced topics
Covers essential ES6 features in JavaScript, enabling learners to build applications with modern language features
Provides a strong theoretical and practical understanding of various machine learning algorithms, preparing learners for real-world projects

Potential Drawbacks

The course relies on Tensorflow 1.x, which could lead to confusion when adapting the learned concepts to Tensorflow 2.x or newer versions
Lacks clear examples of model prediction implementation, making it difficult for learners to fully understand how to apply the models in practice
Some learners may need additional background in linear algebra, calculus, and statistics, as certain sections assume a foundational understanding of these subjects
The course appears to be abandoned by the creator, with outdated information and little support for recent student questions