Last Updated on May 15, 2023 by GeeksGod
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.
