American Innovation University
BootcampExciting AIU Artificial Intelligence and Data Science Bootcamp Offering!
Why AIU Bootcamp?
AIU Bootcamp Commitment . . .
American Innovation University’s Bootcamps give you the resources and knowledge you need to gain success in the tech industry. Bootcamps are accelerated courses that combine lectures with real-world problem sets to train students in Computer Science, Data Science, and Artificial Intelligence in a short amount of time.
AIU Bootcamp Pre-requisites . . .
AIU Bootcamp requires applicants to be at least 18 years old and provide a transcript with the applicant’s academic record. Some applicants may be asked to meet with the AIU’s Academic Advisor to review recommended qualifications and the expectations of the applicant.
The AIU Bootcamps enable those with or without a Computer Science background to further advance their knowledge on a subject, provide professional development, make a career change, or expand their skillset.
For non-Computer Science and non-Technical students, AIU recommends the following AIU courses to help provide a Computer Science foundation:
- Computer Science Infrastructure.
- Object Oriented Design and Data Structure.
With the Artificial Intelligence and Data Science Bootcamp Track, AIU recommends some additional background or work experiences in the areas listed below. Without it, the bootcamp course would be more challenging and the student probably would need to spend additional time to master the skillset.
The student is recommended to have background or work experience in:
- Technology.
- Statistics, Linear Algegra, or Calculus.
- Programming.
AIU Bootcamp Training . . .
In-demand Artificial Intelligence and Data Science training in ReactJS/Angular, OpenCV, Django, JavaScript, Python, Machine Learning, and Deep Learning.
In-demand Computer Science training in Java, JavaScript, Python, Linux, Computer Networks, Network Security, Database Systems, Operating Systems, and Software Engineering.
Popular Artificial Intelligence and Data Science Courses
Interested in Artificial Intelligence?
Order of courses:
1) Python, OpenCV, and Django
2) Machine Learning Using Python
3) Deep Learning
Interested in Data Science?
Order of courses:
1) Data Science Using Python
2) Machine Learning Using Python
3) Deep Learning
Popular Computer Science Courses
Interested in Programming?
o Python
o Java
o Javascript
o Object-Oriented Design and Data Structures
Interested in Computer Science?
o Introduction to UNIX/Linux
o Computer Science Infrastructure
o Operating Systems
o Computer Networks
o Computer Architecture
o Database System Principles
o Software Engineering
o Embedded Software Design
o Algorithms
Interested in Web Development?
o Java
o Javascript
o Python
o Python, OpenCV, and Django
o ReactJS / Angular
Submit Application?
Hours Per Session
Sessions Per Week
Weeks
Spring 2024
1 CourseSpring 2024
2 CoursesSpring 2024
3 Courses| BT100 | Python, OpenCV, and Django | Wednesday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Saturday | 05:00 PM – 08:00 PM | |||
| BT101 | ReactJS / Angular | Monday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Saturday | 10:00 AM – 01:00 PM | |||
| BT102 | Machine Learning Using Python | Tuesday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Friday | 06:30 PM – 09:30 PM | |||
| BT103 | Data Science Using Python | Thursday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Saturday | 01:30 PM – 04:30 PM | |||
| BT104 | Deep Learning | TBD | TBD | Jan 08 – Mar 02 |
| TBD | TBD | |||
| BT332 | Programming in Java | Monday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Wednesday | 06:30 PM – 09:30 PM | |||
| BT334 | Programming in JavaScript | Tuesday | 06:30 PM – 09:30 PM | Jan 08 – Mar 02 |
| Thursday | 06:30 PM – 09:30 PM |
In Person or Remote
You have the option to attend classes face-to-face, or attend live-online lectures. This allows for a greater deal of flexibility for our students.
American Innovation University Instructors
Instructors at American Innovation University are subject matter experts in their field, who have experience working in the tech industry. As a result, courses reflect the most recent practices in the tech industry.
Meet Our Instructors
We want you to succeed!
AIU’s Bootcamp courses are designed to give you valuable knowledge and skills. If you feel you have not gotten as much out of a bootcamp course as you hoped, you may retake that bootcamp course for free. That is, a bootcamp course can be taken for a second time at no cost, within one year of completing the course the first time. AIU’s bootcamps are rigorous and fast paced. If you feel you did not get all that you could out of a course, we want you to have the opportunity to learn more.
Have questions or interested in other courses?
Email AIU at info@aiuca.us or call AIU at 408-955-0855.
All AIU Courses
ReactJS / Angular
Facebook ReactJS is a GUI Library based on JavaScript and ECMA Script. It is arguably the most popular GUI Library of JavaScript since 2013. Google Angular is the most complete GUI Framework since 2016. The course will discuss the following subjects of ReactJS: HTML, CSS, JavaScript, Typescript online coding, ECMA, JSX/Babel components, React props/state/class/refs/memo/fragment components, React functional component, parent/child components, data communication, HOC, Hooks, Redux Data Center between components, HTTP Axios and Ajax libraries, Middleware framework logger, thunk for production optimizations.
Python, OpenCV, and Django
In this course, students will work on a project using Python, OpenCV, and Django/ReactJS. OpenCV is C++ Parallel Programming libraries for Image Processing and Deep Learning. Django is most popular server framework for Frontend React/Angular and Backend Python/Data Science/Machine Learning. This course will be divided into three parts:
Part 1: Python Programming.
Part 2: Python/OpenCV.
Part 3: Integrate frontend to the Backend.
Data Science Using Python
This course will use the Python programming language for Data Analysis and Visualization. There will be a practical introduction to the Python programming language syntax and data structures. This course is divided into three parts.
Part 1: ETL (Extract, Transform, and Load) and Data Preparation.
Part 2: Data Analysis and Visualization.
Part 3: Predictive Model and Project Study.
Machine Learning Using Python
This course introduces the fundamentals of Machine Learning using the Python programming language. There will be a practical introduction to the Python programming language syntax and data structures. This course is divided into five parts.
Part 1: Supervised Vs. Unsupervised Learning.
Part 2: Recommender or Recommendation System.
Part 3: Data Mining and Data Handling.
Part 4: Big Data.
Part 5: Experimental Design.
Deep Learning
Deep Learning technique is a subset of Machine Learning in artificial intelligence, influenced by the way a human thinks and learns. This course covers the fundamental methods of Deep Learning or Artificial Neural Networking, using TensorFlow and PyTorch. TensorFlow is an open source Deep Learning library developed by Google. PyTorch is an open source Machine Learning library developed by Facebook’s AI Research Lab.
Programming in JavaScript
This course introduces JavaScript programming language fundamentals as well as advanced language features such as events and callbacks. This course also introduces modern web development using JavaScript, HTML, and CSS. Students will learn how browsers represent a web page data using the Document Object Model (DOM) and how to develop dynamic, interactive web pages using JavaScript. Finally, this course includes introduction to server-side JavaScript development with web frameworks such as Node.js.
Programming in Java
Java is currently one of the most popular programming languages in use, and is widely used from application software to web applications. It was originally developed by James Gosling to be a simple, object-oriented, robust, secure, architecture neutral, portable, concurrent, and dynamic language. This course first introduces basic programming constructs such as loops, methods, and arrays followed by object-oriented programming concepts and the rich GUI API of Java. Topics include: elementary programming, selections, loops, methods, arrays, objects and classes, strings and text I/O, inheritance and polymorphism, abstract classes and interfaces, object-oriented design and patterns, GUI basics, graphics, event-driven programming, exception handling.
Object-Oriented Programming and Data Structures
Based on the Java programming language, this course first introduces fundamental programming techniques with selections, loops, methods, and arrays. The second part of the course focuses on object-oriented programming concepts such as classes, inheritance, polymorphism, abstract classes, and interfaces. The course concludes with an overview of the Java Collection Framework, which defines a set of useful API for data structures. Topics include: elementary programming, selections, loops, methods, arrays, objects and classes, strings and text I/O, inheritance and polymorphism, abstract classes and interfaces, generics, Java Collection Framework.
Introduction to UNIX/Linux
This course is a practical introduction to UNIX and Linux operating systems. Topics include: user accounts, the visual editor, file system and access control, process management, system calls, system utilities, UNIX handling of files and processes, basic shell utilities and shell scripting.
Computer Science Infrastructure
Computer Science is the study of the theoretical foundations of information and computation. This is an introductory course for students to review or build a foundation in Computer Science. Topics include: history of computing, the basics of hardware and software, operating systems, computer networks, Internet technologies, programming, and software applications.
Computer Networks I
This course is intended to give you an overview of the computer network architecture, discussion includes the key principles of computer networking. It focuses on the underlying concepts and technologies that make the Internet work.
Topics covered include network design and architecture; the ways users can connect to a network; the concepts of switching, routing, and internetworking; end-to-end protocols; congestion control and resource allocation; end-to-end data; network security; and network applications such as e-mail and the Web, IP telephony and video streaming, and peer-to-peer file sharing.
Operating Systems
An operating system (OS) is a set of system software programs in a computer that regulate the ways application software programs use the computer hardware and the ways that users control the computer. This class introduces the basic facilities provided in modern operating systems. Topics include: principles of operating system design and implementation; concurrent processes; inter-process communication; job and process scheduling; deadlock handling; issues in memory management (virtual memory, segmentation, paging); and auxiliary storage management (files systems, directory structuring, protection mechanisms); performance issues; and case studies.
Computer Architecture I
The goal of this course is to provide the students with a working knowledge of how computers operate and the general principles that affect their performance. The topics of this course include an in-depth presentation on major functional units of small to medium-scale digital computers, on machine instruction set characteristics, pipelining and caching, design of arithmetic and logic data path, and the detailed control units. The key aspects of CPU performance, RISC processor design and instruction-level implication will be also addressed.
Database System Principles
Students will learn relational database design both at the physical and at the logical levels. An overview of relational algebra and will cover the SQL programming language. Special topics to be covered include constraints and triggers, views and indexes. In addition, we cover SQL in the server environment including embedded SQL, stored procedure, CLI, and JDBC. We close by covering an overview for query processing and high-level overview of SQL compiler design.
Computer Networks II
For students with CS440 or equivalent background, this course provides detailed coverage of advanced topics in computer networks. Topics include: layer 2 switching and spanning tree protocol, VLAN, TCP/IP, VLSM and subnet, IP routing protocols (RIP, OSPF, BGP, and ISIS), advanced network IPV6 Addressing scheme and static routing, switch/router testing methodology, enterprise network design. The course learning will be aided by regular GNS3 Lab sessions.
Software Engineering
The need to produce efficient, reliable and maintainable software requires the use of engineering principles in specification, creation, verification, validation and management. This course introduces the student to the principles of software engineering as they apply to each stage in the development of a software product. Topics include: software process, requirement engineering, analysis methods, architectural design, component-level design, user interface design, design patterns, software quality assurance, and overview of project management.
Embedded Software Design
Embedded software is computer software which plays an integral role inside the electronics. Embedded software is usually written for special purpose hardware. This course deals with advanced embedded software programming concepts, interfacing techniques, hardware organization and software development using embedded systems. Topics covered in this course include: embedded device drivers, embedded operating systems, networking, error handling and debugging, hardware and software co-verification, DSP in embedded systems, techniques for embedded processing, development technologies and trends, and practical embedded coding techniques.
Design and Analysis of Algorithms
An algorithm is an effective method for solving a problem expressed as a finite sequence of instructions. This course provides students with balanced introduction on computational models for asymptotic time-space complexity analyses as well as algorithmic design techniques with performance and cost implications. Topics include: growth of functions, recurrences, probabilistic analysis and randomized algorithms, sorting algorithms, binary search trees, red-black trees, dynamic programming, greedy algorithms, B-trees, heaps, graph algorithms, minimum spanning trees, shortest paths, maximum flow, sorting networks.
Tuition
The course tuition must be paid by the first meeting or session of the course. If the course tuition is not paid, you will not be allowed to physically attend an onsite course or participate in the online video conferencing.
Buyer's Right to Cancel / Refund Schedule
You have the right to withdraw from the course and obtain a refund. Cancellation shall occur when you submit a written notice of cancellation to AIU by mail. The Post Office stamped date is the date of cancellation. The refund will be a pro-rata refund as shown in the following refund schedule.
| Week of the Course | % of Refund |
| 1 | 100% |
| 2 | 80% |
| 3 | 70% |
| 4 | 60% |
| 5 | 0% |
| 6 | 0% |
| 7 | 0% |