All Projects → DistrictDataLabs → Machine Learning

DistrictDataLabs / Machine Learning

Licence: mit
Code & Data for Introduction to Machine Learning with Scikit-Learn

Projects that are alternatives of or similar to Machine Learning

Demo Fastbert Multi Label Classification
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Opam tip2018
Source code of our TIP 2018 paper "Object-Part Attention Model for Fine-grained Image Classification"
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Nd101
记录自己深度学习之路的点滴
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Peregrine
Peregrine: Fast Genome Assembler Using SHIMMER Index
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Pangeo Example Notebooks
Pangeo Example Notebooks
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Visual tracking api
A simple visual tracking interface using Python
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Kdd20 Hands On Tutorial
Scalable Graph Neural Networks with Deep Graph Library
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Captcha Decoder
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Multi Label Text Classification
Kaggle Toxic Comments Challenge
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Quickstart Python
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Cascade Rcnn tensorflow
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Quantum machine learning live
This is the code for "Quantum Machine Learning LIVE" By Siraj Raval on Youtube
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Alice
NIPS 2017: ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Fraud Detection Using Machine Learning
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Keras Segnet Basic
SegNet-Basic with Keras
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Exportify
Export Spotify playlists using the Web API. Analyze them in the Jupyter notebook.
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Data mining in action 2018 spring
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Depthprediction
A tool to predict the depth field of a 2-dimensional image
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Odscon Sf 2015
Material for ODSCON San Francisco 2015
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook
Tensorflow object detector
Tensorflow Object Detector
Stars: ✭ 80 (+0%)
Mutual labels:  jupyter-notebook

Introduction to Machine Learning with Scikit-Learn

Code & Data for Introduction to Machine Learning with Scikit-Learn

Scikit-Learn Cheat Sheet

Installing Scikit-Learn with pip

See the full installation instructions for more details; these are provided for convenience only.

Scikit-Learn requires:

  • Python >= 2.6 or >= 3.3
  • Numpy >= 1.6.1
  • SciPy >= 0.9

Once you have installed pip (the python package manager):

Mac OS X

This should be super easy:

pip install -U numpy scipy scikit-learn

Now just wait! Also, you have no excuse not to do this in a virtualenv.

Windows

Install numpy and scipy with their official installers. You can then use PyPi to install scikit-learn:

pip install -U scikit-learn

If you're having trouble, consider one of the unofficial windows installers or anacondas (see the Scikit-Learn page for more).

Ubuntu Linux

Unfortunately there are no official binary packages for Linux. First install the build dependencies:

sudo apt-get install build-essential python-dev python-setuptools \
    python-numpy python-scipy \
    libatlas-dev libatlas3gf-base

Then you can build (hopefully) Scikit-learn with pip:

pip install --user --install-option="--prefix=" -U scikit-learn

Keep in mind however, that there are other dependencies and might be issues with ATLAS and BLAS - see the official installation for more.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].