Machine learning in Angular

Learn to build machine learning algorithms for biomedical datasets using TensorFlow.js in Typescript
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
Requirements
I tried to explain all the theory when presented. A knowledge of programming, and Angular, may be advantageous, but not required
Description
Machine learning, here represented by neural networks, is a very powerful and generic way to handle massive amount of data.
What is most surprising about neural models is how they can grasp hidden patterns in datasets, no need to tell the model where are the relationships, or even what kind.
The dataset we are going to explore
The Diabetes prediction dataset we are going to use is a collection of medical and demographic data from patients, along with their diabetes status (positive or negative). The data includes features such as age, gender, body mass index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level [eight features in total]. This dataset can be used to build machine learning models to predict diabetes in patients based on their medical history and demographic information. This can be useful for healthcare professionals in identifying patients who may be at risk of developing diabetes and in developing personalized treatment plans. Additionally, the dataset can be used by researchers to explore the relationships between various medical and demographic factors and the likelihood of developing diabetes.
On this course, we shall apply TensorFlow.js to this dataset.
The machine learning community is dominated by Python and R. However, TensorFlow.js is a promising replacement for people specialized in web development. On this course, I have focused on small but significant group: Angular programmers.

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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