Machine learning in Angular_1

Machine learning in Angular, available at $44.99, has an average rating of 4.44, with 19 lectures, based on 8 reviews, and has 6021 subscribers.

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

Enroll now: Machine learning in Angular

Summary

Title: Machine learning in Angular

Price: $44.99

Average Rating: 4.44

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Getting to know our dataset

Lecture 2: HbA1c Levels accounts for 70% of accuracy on diabetes detection

Lecture 3: Creating our very first app in Angular

Lecture 4: Installing TensorFlow.js and visualization library

Lecture 5: Visualizing the dataset

Chapter 2: A crash view on TensorFlow.js

Lecture 1: Introduction

Lecture 2: Some strongs points from TensorFlow.js

Lecture 3: A couple of example I have built using TensorFlow.js

Lecture 4: Building a model and reading suggestions

Chapter 3: A crash view on neural networks

Lecture 1: Initial thoughts

Lecture 2: A crash view on artificial intelligence

Lecture 3: The looks of a neural network

Lecture 4: Learning neural models from a sandbox: having fun and learning

Chapter 4: A crash view on Angular

Lecture 1: Getting to know Angular

Chapter 5: Building our TensorFlow.js model

Lecture 1: Explaining basic functions: from visualization to dataset loading

Lecture 2: Building our model, part I

Lecture 3: Creating a service

Lecture 4: Finally, building our model

Lecture 5: Taking a look at the training

Instructors

Jorge Guerra Pires
Independent Researcher, PhD

TensorFlow.js Academy
Machine learning in JavaScript

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4 stars: 3 votes

5 stars: 4 votes

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