Tools and Tips on Creating Strong Content and Branding by Leveraging
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And I’ll show you how to easily extend those machine learning ideas from a standalone python script, into a fully automated pipeline that doesn’t only end in an awesome, fully interactive web application, but doesn’t end at all. I’ll go over simple metrics (Google Analytics), to get the feedback needed to iterate new versions and also reach back to your users and keep extending that relationship and making your ML product better. We’ll even integrate simple monetization techniques to gauge if users are willing to covert into buyers using PayPal donations and Stripe purchases!!!

So, don’t wait for magical inspiration or a partner in white shining armor to invite you - instead, start building today!!!

We'll work with real stock-market data to create a market forecasting web application using the powerful model XGBoost. Here is a screenshot of what you'll get to build:

Forecast the stock market using real data and XGBoost!
Forecast the stock market using real data and XGBoost!

Analyzing and Monetizing Prototypes

This is what this applicaiton is all about, quickly iterate through prototypes, check if anything sticks and move on if it doesn't. With this pipeline you can create as many web application as you want without any financial risk. We'll use additional tools such as Google Analtyics, Stripe and PayPal to measure interest and conversion.

Thanks for reading! And please share this article with a friend or colleague or two :-)

Manuel Amunategui,,
Curator @ and ViralML

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