Displaying 1 to 14 from 14 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.

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.

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