Learning Lab.

Unsupervised learning allows machine learning algorithms to work with unlabeled data to predict outcomes. Both supervised and unsupervised models can be trained without human involvement, but due to the lack of labels in unsupervised learning, these models may produce predictions that are highly

TensorFlow is an open-source library that is used to provide software engineers and data scientists with a tool for building, training and working with deep learning models in order to draw predictions from data. The platform features a comprehensive, flexible ecosystem of tools and community

Tableau is amongst the most popular business intelligence and data visualization tools available. Currently owned by Salesforce, Tableau offers enough power and speed to simplify raw data and make it easily accessible to employees at all levels of a company. Tableau facilitates the use of techniques

Supervised learning is an approach to machine learning that utilizes labeled data sets to train algorithms in order to properly classify data and predict outcomes accurately. By labeling outputs and matching inputs to corresponding outputs fed into the algorithm, machine learning models are able to

A subset of deep learning, reinforcement learning relies on a model’s agent learning how to determine accurate solutions from its own actions, as well as the results they produce in different states within a contained environment. This self-interpreting model is trained on a system of rewards and

Python is very popular in machine learning and offers developers of all skill sets exceptional versatility and power. It can be used to develop a variety of applications, integrates well with other software, and its simple syntax makes it a good choice for coding algorithms and collaborating across

Python is a general-purpose, object-oriented programming language that is very popular in data science thanks to its rich libraries and frameworks offering deep learning capabilities and structured machine learning, and its ability to deal with large volumes of data. Python’s simple syntax and ease

Predictive analytics is a form of advanced analytics that uses historical data, statistical modeling, data mining techniques and machine learning to uncover patterns and form predictions about future outcomes. Predictive analytics allows companies to utilize multidimensional factors to identify

Natural language processing (NLP) is a branch of artificial intelligence that provides a framework for computers to understand, interpret and manipulate human language. NLP is particularly useful in helping AI understand language contextually, making extraction of data from text-based documents

Machine learning algorithms fuel mass data analysis and decision making in machine learning models. Machine learning algorithms consist of three parts: a decision process that makes classifications based on input data, an error function to evaluate predictions and adjust for accuracy and a model

Machine learning is an advanced form of data analysis and a branch of artificial intelligence that replicates human learning through the use of large data sets and algorithms. Machine learning is designed to gradually improve over time through repeated actions that train algorithms on how to produce

Inferential statistics is the practice of using sampled data to draw conclusions and make predictions about how a hypothesis will play out or to determine a general parameter about a larger sample. This process will often be used to compare two groups of subjects to make greater generalizations

Hadoop is a collection of open-source software from Apache that allows a network of many computers to solve problems that require massive amounts of data and computational power. The framework’s massive storage capabilities allow it to store data across commodity hardware as needed and can be

Deep learning is a sophisticated type of machine learning dedicated to training computers to discern information from complex data sources, such as images and videos. Deep learning models are created through the use of complex, multi-layered networks that allow data to be passed between nodes in non

A decision tree is a supervised machine learning algorithm that creates a series of sequential decisions made to reach a specific result. Decision trees combine multiple data points and weigh degrees of uncertainty to determine the best approach to making complex decisions, allowing companies to

Data wrangling is the process of transforming raw data into easily understandable formats, organizing sets from multiple sources into a single structure to be further processed. Data wrangling gives data a coherent shape, making it more usable. More than 80 percent of existing data is raw data, and

Data visualization is a method of displaying data in graphs, charts and maps to make it easily understandable to those without knowledge of the data set. Visualization efforts must include the insights received from data, trends and patterns found within the data, as well as a way to discern complex

A data structure is a format for organizing, processing, retrieving and storing data so it can be accessed and utilized effectively. Several basic and advanced data structures exist and are created to arrange data to make it useful for a specific purpose. At their core, data structures are intended

Data modeling is the process of creating visual representations of information in order to draw connections between data points and structures, while illustrating the relationships between data and grouping possibilities. Data models are created to help provide solutions to business needs, with

Data mining is the process of transforming large batches of raw data into usable information through the use of software. Data mining can be utilized to uncover insights that lead to better marketing strategies, increased sales, decreased costs and reduced churn, and is dependent on proper data