Based on our record, Scikit-learn seems to be a lot more popular than BigML. While we know about 27 links to Scikit-learn, we've tracked only 2 mentions of BigML. 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.
Bigml.com — Hosted machine learning algorithms. Unlimited free tasks for development, limit of 16 MB data/task. - Source: dev.to / over 2 years ago
They know the website is bigml.com it's possible they have many magnitudes better algorithms to predict this shit. And it's also possible they paid some quants to come up with price action that just completely fucks with BigML's algorithm entirely to make it look flat. Source: almost 3 years 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 / 10 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: 11 months ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: 12 months ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
RapidMiner - RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
OpenCV - OpenCV is the world's biggest computer vision library
Alteryx - Alteryx provides an indispensable and easy-to-use analytics platform for enterprise companies making critical decisions that drive their business strategy and growth.
NumPy - NumPy is the fundamental package for scientific computing with Python