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2017-slides

Slides and talk assets from PyCon 2017

30-seconds-of-python

Short Python code snippets for all your development needs

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

analytics-vidhya-india-ml-hiring-hackathon-2019

Loan Delinquency Prediction For Upcoming Month.#Evaluation:- Submissions are evaluated on F1-Score between the predicted class and observed. #F1-Score:- 0.35 #Rank 8 On Public Leaderboard

awesome-mlops

A curated list of references for MLOps

aws-modern-application-workshop

A tutorial for developers that want to learn about how to build modern applications on top of AWS. You will build a sample website that leverages infrastructure as code, containers, serverless code functions, CI/CD, and more.

brent-oil-price-forecasting

The aim of this dataset and work is to predict future Crude Oil Prices based on the historical data available in the dataset. The data contains daily Brent oil prices from 17th of May 1987 until the 25th of February 2020.

complex_networks

A large number of real-world systems like Facebook, air-transport, metabolic reactions inside a living cell, and the Internet can be modelled as networks. Also several phenomena like traffic jams, rumour spreading and genetic regulations can be modelled as processes on networks making it an indispensable tool to study ‘complex systems’.

data-engineer-roadmap

Roadmap to becoming a data engineer in 2021

data-science-from-scratch

code for Data Science From Scratch book

deep_learning

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

javascript-in-one-pic

Learn javascript in one picture.

machine_learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

machine_learning_competition

Competition is a great first challenge to get started. This repository contains different participated competition codes & description.

machine_learning_complete

modeling_and_simulation

Mathematical Modeling And Simulation

msds621

Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning

notebook

Jupyter Interactive Notebook

numerical-mooc

A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

petrophysics-python-series

A series of Jupyter notebooks showing how to load well log and petrophysical data in python.

predictive_maintenance

This repository demonstrates the steps in building a predictive maintenance solution.

python3-in-one-pic

Learn python3 in one picture.

scikit-learn

scikit-learn: machine learning in Python

solution-architecture-patterns

Reusable, vendor-neutral, industry-specific, vendor-specific solution architecture patterns for enterprise

stochastic_optimization

Stochastic optimization methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints.

time-series-analysis

transfer-learning-in-keras---custom-data

Implementing Transfer Learning for custom data using VGG-16 and Resnet-50