Artificial Intelligence is empowering modern medical science to help treat critical ailments. Incorporating AI into breast cancer screening allows radiologists to detect cancer more accurately while dramatically reducing their workloads. Using this application you can do early stage detection for breast cancer and help in identifying it as malignant (cancerous) or benign(non-cancerous). You can build predictive analytic based applications with this ready to deploy template application. Fully modifiable source code is provided to enable you to modify for your requirements.
After completing this course you will:
- Have a good working knowledge of the Fundamentals of Predictive Analysis.
- Learn various concepts involved in building the Breast Cancer Prediction Engine such as Exploratory Data Analysis, and Support Vector Classification.
- Have a fully functional prototype that you can customize, showcase, share, using the existing source code.
- Be able to train the model on your custom dataset and fine tune the model to enhance its performance.
Complete this course in 3 easy steps to earn your certificate!
STEP 1 : Watch the below self-guided tutorial.
STEP 2 : Practice as you watch the video by installing and working with the kandi 1-click solution kit.
STEP 3 : Complete the assessment to receive your certificate.
STEP 1 : TUTORIAL
Watch this self-guided tutorial on how you can use Dataset to train the model, Exploratory Data Analysis, and Vector Classification to build your own AI Powered Breast Cancer Detection Engine.
STEP 2 : PRACTICAL EXERCISE
Click the below button to access the breast cancer detector kandi kit. This kit has all the required dependencies and resources you need to build your application.
kandi 1click kits include Python, Jupyter notebook and helps you learn to apply & compare different machine learning algorithms to build your own Breast Cancer Prediction Model, check out the model’s performance using different metrics like accuracy, precision, recall, and confusion matrix and use scikit-learn framework to work with toy datasets and also how to tune model parameters.
Click on the 1-Click Installer button on the kandi kit page to install the breast cancer detector kit. On installing and running this kit, you will have a working model that you can customize and use in your project.
After completing this step, proceed to STEP 3.
Code Snippet Exercises
Below are two coding exercises that will help you advance in your journey in AI Breast Cancer Predictor. To get started, use the relevant keywords to search for simple code snippets in the search bar on kandi .
Exercise 1 - Train test split python: Train test split is a model validation procedure that allows you to simulate how a model would perform on new/unseen data. Empirical studies show that the best results are obtained if we use 20-30% of the data for testing, and the remaining 70-80% of the data for training.
Exercise 2 - ConfusionMatrixDisplay: A Confusion Matrix is an N X N matrix that is used to evaluate the performance of a classification model, where N is the number of target classes. It compares the actual target values against the ones predicted by the ML model.
STEP 3 : ASSESSMENT
Your assessment will be reviewed and you will receive a verified certificate via email within a week.
Reach out to us by replying below for any help you may need with this course.
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