Asana
Trello
Basecamp
Wrike
monday.com
ClickUp
Jira
Smartsheet
machine-learning in Python
Scikit-learn
BigML
Google Cloud TPU
python-recsys
Qubole
Amazon Forecast
Microsoft Bing Image Search API
Asana machine-learning in PythonNo machine-learning in Python videos yet. You could help us improve this page by suggesting one.
Asana helps me keep my projects organized and ensures I donโt miss deadlines. Itโs straightforward to use and works well for team coordination.
Convenient. It helps to stay organized and track task progress.
While Asana is a robust task management and project planning tool, in my experience, it falls slightly short when compared to Trello, particularly in terms of user-friendliness and simplicity. Asana offers a variety of features such as multiple project views (list, board, timeline, calendar), custom fields, and reporting tools, which can be highly beneficial for complex project management. However, I found that the learning curve can be steep, especially for team members not familiar with this type of software. The interface, while feature-rich, can feel a bit cluttered and overwhelming for new users. On the other hand, Trello shines in its simplicity and straightforward design. The visual card and board system is intuitive and easy to grasp, making it a more accessible tool for team members of varying tech proficiency levels. Additionally, Trello's user interface is cleaner and more streamlined, which contributes to an overall more enjoyable user experience.
In terms of collaboration, both tools provide good collaborative features like commenting, tagging, and task assignment. However, I appreciate Trello's flexibility with its Power-Ups, allowing integration with a wide array of apps which enhances its functionality. In conclusion, while Asana is a powerful tool with extensive features, I prefer Trello for its ease of use, simplicity, and intuitive design. However, I do see the value of Asana for larger teams or more complex projects.
Based on our record, Asana seems to be a lot more popular than machine-learning in Python. While we know about 99 links to Asana, we've tracked only 7 mentions of machine-learning in Python. 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.
Product teams shift from designing navigation flows to designing API surfaces and tool definitions. If the primary interaction is a text field, the quality of experience depends on the quality of tool schemas exposed via MCP, not the arrangement of buttons on a screen. Shopify, Figma, and Asana have already deployed remote MCP servers as HTTP endpoints, letting AI agents interact with their platforms... - Source: dev.to / 2 months ago
Popular Tools: Asana, ClickUp, Motion (for AI scheduling and task automation). - Source: dev.to / 11 months ago
Asana transforms team collaboration into a seamless experience with AI-generated insights and workload balancing. - Source: dev.to / 11 months ago
As trust and organization improve, gradually scale back the frequency of updates. For example, transition from daily to thrice-weekly check-ins, then to twice-weekly, and eventually to a single weekly update if the team proves reliable. This approach respects the teamโs ability to self-manage while ensuring nothing slips through the cracks. Pay attention to the teamโs culture - some may thrive with informal Slack... - Source: dev.to / 12 months ago
Asana. Asana Tasks will need to be configured with a Custom ID field, as ticket IDs via the API are all long UUIDs. - Source: dev.to / about 1 year ago
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
Trello - Infinitely flexible. Incredibly easy to use. Great mobile apps. It's free. Trello keeps track of everything, from the big picture to the minute details.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Basecamp - A simple and elegant project management system.
BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
Wrike - Wrike is a flexible, scalable, and easy-to-use collaborative work management software that helps high-performance teams organize and accomplish their work. Try it now.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.