Helps my company solve many issues. We collect information from clients and employees. We conduct surveys and collect data. Very useful software. Helps form a common opinion, conduct analysis and analytics.
One of our customers said: Our small mining operation needed to go from paper based process to digital forms. At first, Google forms allowed us to use this Web-based platform that lets individuals and businesses of all sizes build customizable forms to conduct surveys and generate real-time response charts.
We saw that a small sample of our field workers quickly adopted the new way of working.
Step 1: accomplished.
Now unto step 2.
How do we deploy this unto our whole team? We needed email notifications, offline response collection when without wifi on the field. Our CIO and his director of operations needed deep data and trends analysis as well. Our inspectors, when doing their audits, needed to capture approx. 25 high definition pictures, some audio notes and a video which wasn't really possible with google forms.
So, we can 100% credit the use of google forms to our transition towards a paperless process, but as we navigated saashub.com a little more, we were able to discover a world of alternatives. We strongly suggest to start using google forms before undergoing a big implementation plan towards such enterprise level inspection tools like nspek or even cheaper solutions like prontoforms.
I am not sure if we would start with google's solution first if we would to do this digital transformation all over, but it did allow us to discover it's limits pretty quickly.
At some point, we needed custom fields and functions, and none of us was able to code, so the nSpek training that comes with the application definitely sets it's self apart, giving us full autonomy.
Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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.
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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