So after facing a lot of problem in learning Machine Learning, I started contemplating on how to make the whole learning experience great. Then I came across the concept of Active Recall and Flashcards. So I thought of making Machine Learning Flashcards for our personal use.
But soon I found that the problem I was trying to solve is a very common problem among students and learners. So I thought of creating this ML flashcards product for our fellow students in universities and anyone else who is trying to learn Machine Learning.
Last week Me and Ankit wrapped it's preparation. We created the first set of ML Flashcards. We also finished building the website, sorted out the hosting and started running it. After this we enabled pre-orders on the product page for the early birds and also rolled out discount coupons for them.
And I am ecstatic to say that we got our first 50 paying customers. A total of 5k views on our product page with less that 40% bounce rate according to google analytics within couple of days. We have our first few buyers from Spain, USA and India.
Out product got featured on the Product hunt and received a positive feedback from the Product Hunt community. We also received 20 sales through Product Hunt as well. We are using the community to get honest feedback on our product and make sure we keep improving.
If you have started learning machine learning and want some something that help you memorize the concept then this is for you!!
Check: https://mlcards.xpertup.com
These are deck of flashcards with basic machine learning concepts to help you learn and revise fast and efficiently using one of the most scientific ways to learn, i.e ACTIVE RECALL.
Topics Covered in Flashcards:
AI vs Machine Learning vs Supervised Learning vs Unsupervised Learning vs Reinforcement Learning Machine Learning Pipeline Supervised Learning Flow Unsupervised Learning Flow Reinforcement Learning Flow Linear Regression Logistic Regression Support Vector Machine KNN Classifier Naive Bayes Classifier Decision Tree Random Forest Ensemble Method K-Means Clustering Perceptron Activation Function Model Fitting Bias and Variance Bias-Variance Tradeoff Data Splitting K-Fold Validation Data Augmentation L1 / L2 Regularization Principal Component Analysis Confusion Matrix
Download it now from https://mlcards.xpertup.com
Hope these ML Flashcards will be helpful for you as well. Do let me know your feedback so that we can improve our next version