nmarwen's repositories
ML-DL-in-production
Repository, with some blogposts and code for deploying machine and deep learning-based models in production.
forecasting
Time Series Forecasting Best Practices & Examples
MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
gists
Easily find my gists
1on1-questions
Mega list of 1 on 1 meeting questions compiled from a variety to sources
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
ml-projects
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
algorithmic-examples
Algorithmic Marketing Models
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 ;)
timeseries_demo
A short introduction to time series methods
Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
python_for_scientists
Python Open Courseware for Scientists and Engineers
scipy_con_2019
Tutorial Sessions for SciPy Con 2019
dowhy
DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
statrethinking_winter2019
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019
HandsOn-Unsupervised-Learning-with-Python
HandsOn-Unsupervised-Learning-with-Python, Published by Packt
machine_learning_refined
Notes, examples, and Python demos for the textbook "Machine Learning Refined" (Cambridge University Press).
blog
Source code and other material for my blog posts.
cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
atree
Just a simple Christmas tree, based on reddit story
homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
auto-py-to-exe
Converts .py to .exe using a simple graphical interface
hello_tf_c_api
Neural Network TensorFlow C API.
state_saver
State Saver C++
semver
Semantic Versioning C++
Introduction-to-Time-Series-forecasting-Python
Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
scope_guard
Scope Guard & Defer C++