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Machine learning in Angular
Role Play
Rating: 3.6 out of 5(12 ratings)
7,632 students

Machine learning in Angular

Learn to build machine learning algorithms for biomedical datasets using TensorFlow.js in Typescript
Role Play
Created byJorge Guerra Pires, TensorFlow.js Academy
Last updated 4/2025
English
English [Auto],

What you'll learn

  • Building a neural model using TensorFlowjs
  • Learn some basics about machine learning
  • Learn basics from Angular
  • Learn basics about reading a training process
  • Learn to use some tools on TensorFlowjs for data visualization and training

Course content

5 sections • 21 lectures • 3h 27m total length

  • Getting to know our dataset5:02

    Explore building a machine learning app in Angular using TensorFlow.js, trained on a public diabetes dataset, focusing on one variable to achieve high accuracy.

  • AI Assistant | new feature3:00

    Now we have an AI Assistant, this assistant will help you to learn about TensorFlow.js, Diabetes, Kaggle and more.


  • AI Assistant | new feature | using the AI assistant11:38

    We are going to use the AI assistant

  • Learn about Kaggle Datasets (assistant)
  • HbA1c Levels accounts for 70% of accuracy on diabetes detection6:48

    HbA1c levels account for 70% of diabetes detection accuracy, and a simple model with a few features, like bmi, can achieve strong predictions with 2000 samples and one hidden layer.

  • Creating our very first app in Angular2:17

    Note. The GitHub repository link is attached to this lecture. I have attached also an deployed version of the app


  • Installing TensorFlow.js and visualization library10:23
  • Visualizing the dataset9:40

Requirements

  • I tried to explain all the theory when presented. A knowledge of programming, and Angular, may be advantageous, but not required

Description

Unleash the Power of TensorFlow.js: Build Smart Medical Apps with Ease!

Discover the astonishing world of neural models where building powerful models is now within reach, without breaking the bank. Gone are the days of expansive alternatives like Matlab or specialized coding and machine learning theory. Welcome to TensorFlow.js, the game-changer that allows you to create robust models effortlessly.

In this course, we'll dive into the realm of TensorFlow.js and explore its immense potential for medical applications. Whether you want to leverage pre-trained models from TensorFlow.js hubs or develop your own cutting-edge smart apps, you'll learn how to do it all in no time.

Machine learning, particularly through neural networks, offers a powerful and versatile approach to handle vast amounts of data. The truly astonishing part is how neural models uncover hidden patterns within datasets without explicit guidance. No need to point out relationships or provide specific instructions – these models do it all.

Join us as we delve into the captivating Diabetes prediction dataset. This collection of medical and demographic data, including age, gender, BMI, hypertension, heart disease, and more, allows us to build advanced machine learning models. Predicting diabetes based on patients' history and personal information opens doors for healthcare professionals to identify at-risk individuals and create personalized treatment plans. Researchers can also explore the intricate connections between various factors and the likelihood of developing diabetes.

While Python and R dominate the machine learning landscape, TensorFlow.js shines as a promising alternative for web development enthusiasts. One interesting point about TensorFlow.js: you can use Python codes by manually converting the models since they have similra notations, or you can use public libraries to make the conversion. 

In this course, we cater to a special group: Angular programmers. Embrace the future with TensorFlow.js and revolutionize your medical app development journey.

Enroll now and harness the boundless possibilities of TensorFlow.js for groundbreaking medical applications!


Who this course is for:

  • Angular coders, like myself, could consider a new field of applications of their skills, like I did during my postdoc
  • Data scientists could benefit from analyzing biomedical datasets using JavaScript/Typescript
  • Web devs building apps that can be applied to medicine using websites
  • Applied mathematicians: machine learning is a possible way to model biological phenomena, called black box models
  • Bioinformatics: bioinformatics is already dominated by TensorFlow in Python. This is another spectrum of possibilities for bioinformaticians

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