10 Essential Repositories for Data Science & Machine Learnings

With the advancement of technology, companies are making full use of data and analytical tools to create new products and services. Innovative products, infrastructure systems, etc are being designed to meet the needs of the customer.

Data science and machine learning have been in use for quite some time now. These two fields are closely related. Data science is the field that analyses and studies data to extract some meaning from it. Machine learning is a branch of artificial intelligence and is used to understand and build methods that utilize the data in order to make predictions and improve performance. Since the field is growing, many people are also opting for these online IT certifications to excel in the field.

Since it is still a growing field, one may need management tools and external support. Some of the biggest contributors when it comes to Data Science & Machine Learnings repositories are as follows:

Awesome-machine-learning:

This repository contains a curated list of machine-learning libraries, frameworks, software, etc. All the frameworks and libraries are available in different languages. One can find just the right tool for the task if he/she is starting with machine learning bookmarking.

Project-based learning:

Practical knowledge is always a step ahead of theoretical knowledge. Though theory needs to be understood, a great amount of learning comes when people get hands-on practice with a concept. This is why the potential of learning from a project is higher. This repository has a good collection of projects that one can refer to in the future. The content is available in various languages.

DeepLearning-500-questions:

This is yet another amazing collection of questions and adhered knowledge. It contains a collection of articles on the technical as well as the mathematical side of Deep learning and AI. It offers a strong foundation of knowledge.

The Algorithm:

This source has a website for running and viewing the code in more than 10 popular languages. It is a piece of the pie when it comes to repositories and collections. One can get a lot of information

100-Days-of-ML-Code:

It is one of the best sources to learn machine learning. It contains 100 days plan to learn the technology and has some of the most amazing contributors. The repository contains datasets included with repo as well as open source enthusiasts. One thing that keeps you motivated and on track is the daily learning plan which is the graphical poster.

Data science:

This is the best source for people who want to complete the Data Science Undergraduate curriculum at their own pace. It is best suited for professionals as the repository can be accessed from anywhere and at any time. It contains some of the best Data Science certifications and courses in the world. It is a method of collective learning and open world free education initiative.

Public-APIs:

It contains some of the best collections of the APIs which are categorized in a comprehensive manner. It contains tables listing the API, HTTP & CORS status, regarding Auth. In this way, one does not have to read the whole document. It is very handy.

Complete-python-3-Bootcamp:

Python is a widely used language when it comes to data science and machine learning. This repository contains one of the most popular and highly rated Python 3 Bootcamp courses.

Awesome-python:

This repository contains a plethora of frameworks, libraries, and resources which are specific to python. It contains a large amount of information right from working with web sockets to building admin dashboards. It is one of the best repositories to refer to.

Tensorflow:

This repository was developed by engineers and researchers while working on the Google Brain. It was used within Google’s machine intelligence research organization to conduct deep neural network research and machine learning. This repository is an end-to-end open-source platform when it comes to machine learning. It is one of the most famous platforms developed so far. The flexible ecosystem, comprehensive tools, and libraries help the researchers to push the state-of-the-art in the machine. It helps developers to build and deploy ML-powered applications.

These repositories can work wonders when it comes to offering knowledge. The fields like data science and machine learning have huge scope. Many professionals are also opting for it as their full-time career and going for Machine Learnings online certification. These repositories not only help in acquiring better knowledge and clarity but can also be used to solve problems while working on live projects.