AiLearning - AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnn

tutorials - 机器学习相关教程

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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.

machine-learning neural-network tensorflow sklearn theano threading multiprocessing numpy

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

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

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

matploblib sklearn scikit-learn

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

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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.

sklearn data-science machine-learning tutorial jupyter-notebook

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

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

machine-learning word2vec tsne language words gensim sklearn

MLatImperial2017 - Materials for the course of machine learning at Imperial College organized by Yandex SDA

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Materials for the course of machine learning at Imperial College organized by Yandex SDA

machine-learning lectures practice imperial-college yandex deep-learning sklearn scikit-learn keras theano

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

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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.

machine-learning deep-learning data-science reproducibility reproducible-experiments open-source education training lightgbm xgboost sklearn pipeline-framework ensemble-model neptune santander competition banking-applications

python-flask-sklearn-docker-template - A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker

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A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker

docker scikit-learn flask sklearn

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

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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.

machine-learning support-vector-machines sklearn sklearn-classify numpy scipy spam-classification enron-spam-dataset

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

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"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.

sklearn scikit-learn kmeans-clustering kmeans machine-learning

machine-learning - 从零基础开始机器学习之旅

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从零基础开始机器学习之旅

tensorflow ml machine-learning awesome anomalydetection kaggle pandas nlp sklearn deep-learning

data-formatter - Takes raw csv input and formats it to be ready for neural networks

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Formats and cleans your data to get it ready for machine learning!

neural-network machine-learning data-formatting normalization min-max-normalization min-max-normalizing brain.js automated-machine-learning bestbrain data-science kaggle scikit-learn sklearn scikit-neuralnetworks lasagne nolearn nolearn.lasagne data-cleaning data-munging data-preparation imputing-missing-values filling-in-missing-values dataset data-set training testing random-forest vectorization categorization one-hot-encoding dictvectorizer preprocessing feature-selection feature-engineering

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

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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.

machine-learning data-science python3 tensorflow tensorflow-tutorials tensorflow-examples sklearn