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Artificial Intelligence Graduate Certificate Programs

Oct 28, 2019

This article gives an overview of the graduate certificate in artificial intelligence. Read further to find out about program requirements, some of the core courses of the curriculum, and admission requirements.

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A graduate certificate in artificial intelligence(AI) deals with significant concepts of AI used in industries to automate complex business processes. These programs are appropriate for working professionals seeking advancement in careers as well as students exploring job opportunities in this field or planning graduate studies in artificial intelligence.

Program Overview and Core Courses in Artificial Intelligence

The graduate certificate in artificial intelligence typically requires students to complete 12-15 credits with a minimum GPA of 2.7 to 3.0. Some of the general courses included in this program are listed below.

Advanced Programming Concepts

This course explores fundamental and advanced topics in software programming, and it can introduce high-level programming languages like Python and Java. Students can explore primary topics such as data types and iterative programming. Object-oriented programming, data structures and algorithms, as well as using these concepts to solve high-level mathematical problems could be a vital part of this course.

Big Data Architecture, Management & Text Processing

In this course, students can explore fundamentals related to big data and it's architecture, as well as software tools and technologies to process unstructured and semi-structured data. Additionally, this course might offer methods to design solutions for collecting, processing, analyzing, and securely storing big data. The course might also explore various statistical and programming practices, as well as algorithms for text processing and data analysis.

Machine Learning Concepts

Machine learning develops systems that learn from data fed to them and analyzes the relations and patterns within the data. This course could discuss different machine learning models like supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms like naïve Bayes, clustering, and decision trees might be a part of this course. It might offer hands-on practice to build a predictive model using machine learning concepts.

Deep Learning Algorithms

Students can explore the fundamentals of deep learning and mathematical concepts for deep learning within this course. Various neural network algorithms used for problem-solving in deep learning might be covered. The course may provide hands-on practice to plan, build, and train deep learning designs to solve various problems, such as image classifications and predicting results based on data provided. Students can also learn techniques and methods to analyze machine learning models for better performance and accurate prediction.

Data Mining & Visualization

Concepts related to handling and processing data to draw useful insights for decision-making could be a part of this course. The course might introduce various data analysis tools for data processing and transformation, in addition to classification and analysis techniques. This type of course can also include data visualization concepts and software packages used for presentation of information derived from data mining and analysis.

Artificial Intelligence Concepts

This course can teach the fundamentals of artificial intelligence(AI), as well as advanced concepts and methodologies required to design and develop intelligent applications for automated tasks related to complex business processes. Concepts related to neural networks, self-learning methods from raw data, and transferring existing learning to other AI systems could be a part of this course. Students can also get an idea of common challenges related to AI solutions. Hands-on programs to understand real-time working with relevant technologies and algorithms to develop AI solutions could be part of this course.

Admission Requirements

Applicants must have a bachelor's degree to be eligible for these programs, and some schools may require a prerequisite certificate. A few institutes require a technical background in undergraduate studies; students with no technical background might need to take introductory courses first. Some schools prefer candidates with previous work experience in this field. Additionally, official transcripts of previous studies, resume, and two letters of recommendation are usually needed.

Graduate certificate in artificial intelligence programs are available to help professionals or students expand their expertise in this area. Typically, these certificates require 12-15 credits and cover topics related to software programming, data analysis and management, machine learning, deep learning, and artificial intelligence.

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