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applied-supervised-learning-with-r

Use machine learning libraries of R to build models that solve problems and predict business trends

artificial-intelligence-and-machine-learning-fundamentals

Get started with the development of real-world applications that are powered by the latest AI advances

beginning-php

Master the latest features of PHP 7 and fully embrace modern PHP development

big-data-analysis-with-python

Combine Spark and Python to process large datasets and unlock the power of parallel computing and machine learning

capstone-project

clojure-site

clojure.org site

data-science-projects

game-development-projects-with-unreal-engine

Learn to build your first games and bring your ideas to life using UE4 and C++

github-slideshow

A robot powered training repository :robot:

hotspot-analysis

We will visualize the results of hotspot analysis and use kernel density estimation, which is the most popular algorithm for building distributions using a collection of observations. By the end of the course, you should be able to leverage Python libraries to build multi-dimensional density estimation models and work with geo-spatial data.

indentation-examples

introduction-to-scikit-learn

This module covers scikit-learn's syntax to solve simple data problem, which will be the starting point to develop machine learning solutions

key-concepts-of-supervised-learning

This module covers the main steps for working on a supervised machine learning problems, classification and regression. It will also cover how to effectively create unbiased models that perform well over unseen data.

learning-path-node.js-complete-reference-guide

python-random-quote

A file-based quote bot written in Python

the-computer-vision-demo-workshop

the-data-science-workshop-course

This course is suggested for individuals who have basic knowledge of Python and are willing to begin their journey as Data Scientist.

the-data-science-workshop-template

The template repository for the The-Data-Science-Workshop course on Learning Lab.

the-machine-learning-workshop

the-python-workshop

Become a proficient, productive and powerful programmer with Python

the-reinforcement-learning-demo-workshop

the-supervised-learning-demo-workshop

topic-modelling

You will evaluate latent Dirichlet allocation models and execute non-negative matrix factorization models. Finally, you will interpret the results of topic models and identify the best topic model for the given scenario. You will see how topic modelling provides insights into the underlying structure of documents. By the end of this course, you will be able to build fully functioning topic models to derive value and insights for your business.