Congratulations on choosing to participate in the Virtual Internship Program on AI Engineering with Open Weaver! This 4-week program is designed to be an interactive & practical internship and will help you gain industry-ready skills through project-based learning. The internship will consist of 3 Bootcamps (1 per week), a few coding exercises, a project, and a final assessment.
WEEK 1 - AI ENGINEERING
Today, our focus will be on how Artificial Intelligence is playing a pivotal role in revolutionizing contemporary medical science, aiding in the treatment of critical illnesses. Through today’s bootcamp and this week’s exercises, we will learn how to apply Machine Learning techniques for Predictive Analysis (Building a Breast Cancer Prediction Engine) using data analysis and ML libraries such as Pandas and Scikit-learn.
Learning Objectives
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 model that you can customize as per your own dataset and fine tune the model to enhance its performance.
10-Min Tutorial
By integrating these AI concepts into breast cancer screening, radiologists can achieve more precise cancer detection while significantly alleviating their workload. This technology enables early-stage detection of breast cancer and facilitates the identification of whether a tumor is malignant or benign.
Watch this tutorial on building your own Breast Cancer Prediction Engine & learn how to train the model, do Exploratory Data Analysis, and Vector Classification. Revisit the concepts discussed during the live bootcamp session in this 10-min tutorial video. If you would like to watch the recording of the entire bootcamp please click HERE.
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.
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.
Week 1 Coding Exercises
Complete interesting coding exercises and receive your first badge for this internship! Submit your solutions by adding a screenshot of the code and the output in the form.
It is mandatory for you to complete your weekly Coding Exercises in order to receive your Internship Completion Certificate.
Below are two sample 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 .
Sample 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.
Sample 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.
Support
Reach out to us by clicking on the reply button below for any help you may need with this course. You may also use the chat feature for support. To access the reply and chat feature, please sign-in to the the Community.
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