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100daysofpython

The ultimate goal of the 100 Days of Code challenge is to become a better developer and to build coding as a habit. If you hope to become a more versatile, disciplined, and skilled developer, you should consider joining the challenge.

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

amalsebastian7

amalsebastian7.github.io

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-drug-discovery-knowledge-graphs

A collection of research papers, datasets and software related to knowledge graphs suitable for drug discovery.

awesome-for-beginners

A list of awesome beginners-friendly projects.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-oss

A list of open source projects with links to contribute or donate.

awesome-public-datasets

A topic-centric list of HQ open datasets.

awesome-python-projects

📱 ✅ Some awesome projects in python! 📱 ✅

basic-computer-games

An updated version of the classic "Basic Computer Games" book, with well-written examples in a variety of common programming languages

best_ai_papers_2021

A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. [work in progress]

bioinformatics

budgetapp

Firefly III: a personal finances manager

client

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

cms-sw.github.io

Documentation for CMSSW

code-dot-org

The code powering code.org and studio.code.org

code_snippets

computervision

cpython

The Python programming language

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 200 universities.

dash

Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.

data-collection-pipeline

An implementation of an industry grade data collection pipeline that runs scalably in the cloud. It uses Python code to automatically control your browser, extract information from a website, and store it on the cloud in a data warehouses and data lake. The system conforms to industry best practices such as being containerised in Docker and running automated tests.

data-science

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

data-science-for-beginners

10 Weeks, 20 Lessons, Data Science for All!

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.

data-science-projects

DataScience projects for learning : Kaggle challenges, Object Recognition, Parsing, etc.

data-scientist-roadmap

Toturials coming with the "data science roadmap" picture.

data-structures-and-algorithm-in-python

In daily life, we always go with that person who can complete the task in a short amount of time with efficiency and using fewer resources. The same things happen with Coding.Knowledge of data structures like Hash Tables, Trees, Tries, Graphs, and various algorithms goes a long way in solving these problems efficiently.Data Structures are a crucial part of several computer algorithms as they allow programmers to do data management efficiently. A wise selection of data structures can improve the performance of a computer program or algorithm in a more useful way. 1. Handling complexity: Increase in complexities in computer algorithms, the volume of data usage is rising; this can affect the execution of the application and can create remarkable areas of concern like processing speed, data search, and multiple requests. To counter these data structures are used. 2. Systematic memory use: Systematic application of data structure memory usage can be optimized, e.g., we can use linked list vs. arrays when we are not particular about the data size. When there is no longer use of memory, it can be cleared. 3. Ability to reuse: Once we have executed a particular data structure, we can reuse it in any distinct position. Implementation of data structures can be assembled into libraries that can be utilized by various clients. 4. Abstraction:  Data structure acts as the foundation of abstract data types; the data structure describes the physical form of Abstract Data Type. In ADT, the set of operations is supposed to be understood, and the data structure provides physicality to them. Data structures are the integral constituent of any programming language or complex computations. Mostly, structured data structures are crucial to designing efficient algorithms.