ioBroker
openHAB
Home-Assistant.io
Google Home
Domoticz
Home
IP-Symcon
mediola IQONTROL
machine-learning in Python
Scikit-learn
BigML
Google Cloud TPU
python-recsys
Qubole
Amazon Forecast
Microsoft Bing Image Search API
More than 500 different modules(adapters) that can be interconnected with each other. E.g. Homematic or KNX can control HUE or sonos and vice versa.
ioBroker machine-learning in PythonioBroker is recommended for tech-savvy users who are comfortable with DIY configurations and those looking for a cost-effective solution to integrate various smart home devices. It's also suitable for developers interested in extending its capabilities through custom scripts and adapters.
No machine-learning in Python videos yet. You could help us improve this page by suggesting one.
ioBroker's answer
ioBroker's answer
Compatibility: ioBroker supports a vast range of devices and protocols, making it one of the most versatile smart home automation systems available. It is compatible with many popular brands and can integrate with virtually any smart device, offering a level of flexibility that might not be available with other platforms.
Open Source: As an open-source platform, ioBroker is free to use and continuously updated and improved by a community of developers. This allows for greater customization, transparency, and control over your home automation setup.
Scalability: ioBroker is designed to handle complex smart home setups. No matter how many devices you have or plan to add in the future, the platform is designed to scale and manage a large and diverse range of devices efficiently.
JavaScript and Blockly support: For those comfortable with programming, ioBroker offers the option to write scripts in JavaScript. For users who prefer a graphical interface, Blockly is available. This versatility can be appealing for both beginners and experienced users.
Data Logging: ioBroker has extensive data logging capabilities, allowing users to store, analyze, and visualize data from their smart devices over long periods of time. This can be incredibly valuable for monitoring energy usage, tracking trends, and optimizing your smart home setup.
Community and Support: ioBroker has a strong and active community of users and developers who can provide assistance, share ideas, and help troubleshoot any issues you may encounter.
ioBroker's answer
Mostly users are german speaking, but all documentation is primary in english.
ioBroker's answer
ioBroker is an open-source Internet of Things (IoT) platform that was developed with the aim to provide a unified and flexible solution for smart home automation. It's primarily driven by a community of enthusiasts and developers contributing to its ongoing development and expansion.
The project was initiated to overcome the limitations of existing smart home platforms, particularly the inability of many platforms to integrate with a wide variety of devices and brands. ioBroker was designed with a focus on compatibility, scalability, and flexibility, aiming to provide a solution that can integrate a vast array of smart devices, regardless of manufacturer or protocol, and handle complex home automation setups.
While the platform was initially more popular among the tech-savvy due to its need for more technical involvement, over time, efforts have been made to make it more user-friendly and accessible to a wider audience.
As an open-source project, the ongoing development of ioBroker is dependent on the contributions of its community, including software developers and end-users, who continually work on refining the software, expanding its compatibility with different devices, and improving its features.
ioBroker's answer
JavaScript, Redis, Mqtt, MUI-UI.
Based on our record, machine-learning in Python seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 3 years ago
Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 3 years ago
MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally wonโt make you hireable unless youโre doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: about 4 years ago
Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 4 years ago
Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 4 years ago
openHAB - "empowering the smart home" - vendor and technology agnostic open source home automation
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Home-Assistant.io - Home Assistant is an open-source home automation platform running on Python 3.
BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
Google Home - Set up, manage, and control your Chromecast, Chromecast Audio and Google Home devices.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.
