Scikit-learn Reviews and details

Social recommendations and mentions

• Must-Know 2025 Developer’s Roadmap and Key Programming Trends

  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

• 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖

  Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago

• Essential Deep Learning Checklist: Best Practices Unveiled

  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

• How to Build a Logistic Regression Model: A Spam-filter Tutorial

  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

• Link Prediction With node2vec in Physics Collaboration Network

  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

• WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point

  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: almost 2 years ago

• PSA: You don't need fancy stuff to do good work.

  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: almost 2 years ago

• Help on using R for Machine Learning?

  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 2 years ago

• Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service

  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 2 years ago

• Best Websites For Coders

  Scikit-learn : A Python module for machine learning build on top of SciPy. - Source: dev.to / over 2 years ago

• This is how to deploy a machine learning model with Anvil

  In this short tutorial, we'll use Anvil to turn an ML model into an interactive web application. We will use the classic iris classification problem, for which I have a pre-trained model using sklearn and joblib (if you want to see how I trained this model, check out this tutorial). - Source: dev.to / over 2 years ago

• Machine Learning Pipelines with Spark: Introductory Guide (Part 1)

  The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide. - Source: dev.to / over 2 years ago

• Talking Data: What do we need for engaging data analytics?

  Many data workers are complaining about the fierce competition in the data area. Fortunately, the situation seems to be improving. Data analysts had to manually analyze distribution charts for deep insights, but now they can use smart machine learning models to automate this process. Traditional data analysis and modeling skills have been gradually becoming easy. For instance, Power BI or Tableau allow users to... - Source: dev.to / over 2 years ago

• Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)

  Scikit-learn – Simple and efficient tools for predictive data analysis, built on NumPy, SciPy, and matplotlib. - Source: dev.to / over 2 years ago

• Don't Waste Data! An Experiment with Machine Learning

  Once we had determined the shape of the data and the features we should focus on, we set out to create a model. (There is a wealth of ML tools available across programming languages like Python and Julia.) We chose scikit-learn, one of the most popular ML libraries around, and plugged the data into a random forest regression. (Say what? Here’s a quick and dirty guide to random forest regression.) As input, we used... - Source: dev.to / almost 3 years ago

• Advice on how to go about learning Data science and more specifically ML

  For ml, I would look at scikit learn and tensor flow courses (an example for tensor flow would be google's crash course), kaggle is also a good resource. Source: almost 3 years ago

• Will it benefit me having a portfolio alongside my cv?

  I say 'usually' because it depends on what you're referring to as 'coding'. From what you're describing, it seems that you want to be able to take data, clean it up and perform a whole bunch of analysis/inferences on it. In that case, I think the coding skill there would be stuff that allows you to do data manipulation and data clean up (knowledge of R, knowing Python as it pertains to data stuff e.g. Scikit... Source: almost 3 years ago

• What to do with some data?

  Are you using scikit-learn for your training? If so, you may try running the models on one another. If you're using custom kernels, you may want to use a different set of them for the test set. Source: about 3 years ago

• Roadmap to self-learning AI

  My only gripe is that the Labs are in R and not Python, but honestly the [scikit-learn](https://scikit-learn.org/) user guide & docs have been straightforward enough to apply the same knowledge in Python for me with some trial and error. Source: over 3 years ago

• Learning python, what next?

  Machine learning and statistical analysis? http://scikit-learn.org. Source: over 3 years ago

• Identifying trolls and bots on Reddit with machine learning (Part 2) - Identificando trolls y bots en reddit con Machine Learning

  Our next step is to create a new machine learning model based on this list. We’ll use Python’s excellent scikit learn framework to build our model. We’ll store our training data into two data frames: one for the set of features to train in and the second with the desired class labels. We’ll then split our dataset into 70% training data and 30% test data. Source: over 3 years ago