abhinav-chouhan's Stars
google/styleguide
Style guides for Google-originated open-source projects
donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
academic/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
fastai/fastbook
The fastai book, published as Jupyter Notebooks
datasciencescoop/Data-Science--Cheat-Sheet
Cheat Sheets
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
instillai/TensorFlow-Course
:satellite: Simple and ready-to-use tutorials for TensorFlow
ml-tooling/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
ctgk/PRML
PRML algorithms implemented in Python
iamtrask/Grokking-Deep-Learning
this repository accompanies the book "Grokking Deep Learning"
MrMimic/data-scientist-roadmap
Toturials coming with the "data science roadmap" picture.
rushter/data-science-blogs
A curated list of data science blogs
tirthajyoti/Data-science-best-resources
Carefully curated resource links for data science in one place
Machine-Learning-Tokyo/Interactive_Tools
Interactive Tools for Machine Learning, Deep Learning and Math
justmarkham/scikit-learn-tips
:robot::zap: 50 scikit-learn tips
ianozsvald/data_science_delivered
Observations from Ian on successfully delivering data science products
laurencium/Causalinference
Causal Inference in Python
bradleyboehmke/data-science-learning-resources
A collection of data science and machine learning resources that I've found helpful (I only post what I've read!)
mrdbourke/m1-machine-learning-test
Code for testing various M1 Chip benchmarks with TensorFlow.
alteryx/open_source_demos
A collection of demos showcasing automated feature engineering and machine learning in diverse use cases
alteryx/Automated-Manual-Comparison
Automated vs Manual Feature Engineering Comparison. Implemented using Featuretools.
john-science/scipy_con_2019
Tutorial Sessions for SciPy Con 2019
llSourcell/Data_Science_Interview_Guide
These are the tips for "5 Steps to Pass Data Science Interviews" By Siraj Raval on Youtube
chrisluedtke/data-science-journal
Personal repository of data science demonstrations and references
ikding/pycon_time_series
PyCon 2017 tutorial on time series analysis
sean-mcclure/machine_flow
Machine Flow enables visual execution and tracking of machine learning workflows. Users dynamically create dependency graphs, with each node responsible for executing a task and displaying results.
anurag-code/Survival-Analysis-Intuition-Implementation-in-Python
Quick Implementation in python
shawlu95/Data-Science-Toolbox
Examples and illustration of basic statistic concepts, probability distribution, Monte Carlo simulation, preprocessing and visualization techniques, and statistical testing.
shawlu95/Lookalike-Model
Finding similar, high-valued users based on seed users. The model includes 1805 features using Hive HQL and AWS Redshift.