Displaying 1 to 20 from 20 results

tutorials - 机器学习相关教程

  •    Python

我是 周沫凡, 莫烦Python 只是谐音, 我喜欢制作, 分享所学的东西, 所以你能在这里找到很多有用的东西, 少走弯路. 你能在这里找到关于我的所有东西. 这些 tutorial 都是我用业余时间写出来, 录成视频, 如果你觉得它对你很有帮助, 请你也分享给需要学习的朋友们. 如果你看好我的经验分享, 也请考虑适当的 赞助打赏, 让我能继续分享更好的内容给大家.

igel - a delightful machine learning tool that allows you to train, test, and use models without writing code

  •    Python

The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.




sklearn-evaluation - scikit-learn model evaluation made easy: plots, tables and markdown reports.

  •    Python

scikit-learn model evaluation made easy: plots, tables and markdown reports. Works with Python 2 and 3.

talentsprint-workshop - TalentSprint workshop on Machine Learning in November 2017

  •    Jupyter

Here's a brief plan of the four sessions of the workshop. Each of these sections will include exercises based on real-world datasets. While most of the workshop depends only on scikit-learn, there are a few other requirements too. An exhaustive list of Python packages required for the workshop is as follows. At most a couple more cursory packages might get added to this list as I proceed with creating the material, but those should be easily installable at the venue itself, assuming that the participants have a Python distribution like Enthought Canopy or Anaconda installed.

Word2VecAndTsne - Scripts demo-ing how to train a Word2Vec model and reduce its vector space

  •    Python

To use this code, you'll need to install some pretty hefty libraries. Luckily, they all install very easily.


open-solution-value-prediction - Open solution to the Santander Value Prediction Challenge :tropical_fish:

  •    Python

In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉. You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.

spampy - Spam filtering module with Machine Learning using SVM (Support Vector Machines).

  •    Python

Spam filtering module with Machine Learning using SVM. spampy is a classifier that uses Support Vector Machines which tries to classify given raw emails if they are spam or not. Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

KMeans_elbow - Code for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'

  •    Jupyter

"The idea behind k-Means Clustering is to take a bunch of data and determine if there are any natural clusters (groups of related objects) within the data. The k-Means algorithm is a so-called unsupervised learning algorithm. We don't know in advance what patterns exist in the data -- it has no formal classification to it -- but we would like to see if we can divide the data into groups somehow.

applied-machine-learning-intensive - Applied Machine Learning Intensive

  •    Jupyter

The Applied Machine Learning Intensive (AMLI) is a collection of content that can be used to teach machine learning. The original content was created for a 10-week, bootcamp-style course for undergraduate college students. Designed for students who weren’t necessarily majoring in computer science, the goal was to enable participants to apply machine learning to different fields using high-level tools. The content primarily consists of slides, Jupyter notebooks, and facilitator guides. The slide decks are written in marp markdown syntax, which can be exported to other formats. The Jupyter notebooks were written in and targeted to run in Colab. The instructor guide as an odt document.

A-B-testing-with-Machine-Learning - Implemented an A/B Testing solution with the help of machine learning

  •    Jupyter

Recently, I was reading through A/B Testing with Machine Learning - A Step-by-Step Tutorial written by Matt Dancho of Business Science. I have been always fascinated by the idea of A/B Testing and the amount of impact it can bring in businesses. The tutorial is very definitive and Matt has explained each and every step in the tutorial. He has detailed about each and every decision taken while developing the solution. Even though the tutorial is written in R, I was able to scram through his code and my knowledge of Data Science helped me to understand the concepts very quickly. I will have to thank Matt for putting together all the key ingredients of the Data Science world and or using them to solve a real problem.

RecommenderSystems - Recommender Systems and Collaborative Filtering

  •    Jupyter

Collaborative filtering is a method of recommending products to customers using their past behavoirs or ratings as well as similar decisions by other customers to predict which items might be appealing to the original customers. Content-based filtering suggests products to customers by using the characteristics of an item in order to recommend additional items with similar properties. I'll just be touching on collaborative filtering in this blog post since it is very popular and has the ability to accurately recommend complex items without the need to understand the item itself. Collaborative filtering is also much more popular for web-based recommendations where the data is sparse, i.e., where there is a limited number of reviews by each user or for a particular product. The data we will use comes from Amazon and can be found here. I chose the Amazon Instant Video 5 core file. The 5 core implies that each video/item has atleast 5 ratings and each users has rated atleast 5 videos/items.

skpro - Supervised domain-agnostic prediction framework for probabilistic modelling

  •    Python

A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points. The full documentation is available here.

xpandas - Universal 1d/2d data containers with Transformers functionality for data analysis.

  •    Python

XPandas (extended Pandas) implements 1D and 2D data containers for storing type-heterogeneous tabular data of any type, and encapsulates feature extraction and transformation modelling in an sklearn-compatible transformer interface. The full documentation is available at https://alan-turing-institute.github.io/xpandas/.

trt_pose_hand - Real-time hand pose estimation and gesture classification using TensorRT

  •    Jupyter

Pretrained models for hand pose estimation capable of running in real time on Jetson Xavier NX. Make sure to follow all the instructions from trt_pose and install all it's depenedencies. Follow step 1 and step 2 from https://github.com/NVIDIA-AI-IOT/trt_pose.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.