INTRODUCTION TO WEEK 1
In week 1 of the internship, 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 the Week 1 Live Bootcamp and Coding Project, 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.
LIVE BOOTCAMP
Unlock the power of AI by integrating it 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 Live Bootcamp (recording available below after live session) on building your own Breast Cancer Prediction Engine & learn how to train the model, do Exploratory Data Analysis, and Vector Classification.
NOTE :: You must sign in/sign up (top right corner of this page) to record your attendance while watching this live or recorded bootcamp. Just sign in and then watch - it’s as easy as that!
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.
Below are two sample coding exercises that will help you advance in your journey in Predictive Analysis. 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.
CODING PROJECT
Complete and submit this interesting Coding Project and receive your AI Engineering Enthusiast Badge!
It is mandatory for you to complete your weekly Coding Project in order to receive your final Internship Completion Certificate.
NOTE :: For questions 6-10, please ensure you upload screenshots of your code and outcome in PNG format ONLY. We will not be able to consider answers provided in text or any other format. You will receive your Badge #3 if you meet the minimum requirement of 60% correct answers. If not, you will receive an email from us requesting you to retake the Coding Project.
Showcase your progress & win exciting prizes!
- Share your badge on social media. You can save the image and share.
- Add a short note on why you are excited to learn new digital skills.
- Ensure you tag #Ready2GoDigital @ICTAcademy @OpenWeaver.
Win goodies every week! Share badges every week and stand to win Airpods at the end of the internship!
SUPPORT
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