Predicting the stock market is one of the most difficult things to do given all the variables. There are numerous factors involved – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict accurately.
In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM.
In this video Nina Zakharenko shows you how to configure Visual Studio Code as a productive Python development environment, and use integrations to easily create, debug, and deploy Python applications to the cloud with Azure Web Apps on Linux.
Want to ship code faster? Nina will teach you how, using Azure DevOps to automatically build and deploy your apps.
It slices! It dices! And it’s hard to imagine doing data science in Python without it. NumPy is a keystone Python library that’s crucial to learn. Fortunately, Giles McMullen has you covered with this five minute tutorial of NumPy!
Implementing and Training Predictive Customer Lifetime Value Models in Python are covered in this talk by Jean-Rene Gauthier and Ben Van Dyke. Customer lifetime value models (CLVs) are powerful predictive models that allow analysts and data scientists to forecast how much customers are worth to a business.
CLV models provide crucial inputs to inform marketing acquisition decisions, retention measures, customer care queuing, demand forecasting, etc. They are used and applied in a variety of verticals, including retail, gaming, and telecom.
Hands On Machine Learning with Scikit Learn and Tensorflow published by O’Reilly and written by Aurelien Geron could just be the best practical book on machine learning. In this review, Giles McMullen-Klein explains why and, having also read this book, I have to agree.
Ever wondered what breed that dog or cat is? In this show, you’ll learn how to train, optimize and deploy a deep learning model using Azure Notebooks, Azure Machine Learning Service, and Visual Studio Code using Python. Using transfer learning to retrain a mobilenet model via Tensorflow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset.
Next, watch how to optimize that model using the Azure Machine Learning Service HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
In a previous post, covering Python on Azure, Nina Zakharenko and Carlton Gibson showed us how to set up a Python application with Django REST Framework and develop with Visual Studio Code. Now they are back to teach us how to deploy a Python app built on Django to the cloud using Azure Web Apps.
One of the key attributes of a self-driving car is being able to detect what lane the car should be driving in. Here’s a great tutorial on how to do just that in Python with OpenCV.
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