Artificial Intelligence Bootcamp

The UNC Charlotte Artificial Intelligence Bootcamp Certificate program, powered by Flatiron School, offers a comprehensive artificial intelligence curriculum that will help prepare you for a career in artificial intelligence.

UNC Charlotte x Flatiron School

20,000+
In Our Alumni Network
4:1
Student-teacher ratio
100%
Online instruction

From beginner to professional

Immerse yourself in the captivating world of AI and learn how to make the next internet-breaking AI model.

Kickstart a Career in AI

Learn to develop AI models with the world’s most popular languages, tools, and techniques.

From learning the basics of Python to creating your first artificial intelligence – whatever you want to create, we’ll get you there.

Program Features

  • Personalized job-focused training & career services
  • Small class sizes (max 5 students)
  • Weekly calls with your mentor + recorded video critiques
  • Supportive and active community of peers, alumni, and mentors
  • Flexible schedules and 100% online, study from anywhere!

AI Study Pathway

This comprehensive learning pathway will equip you for a future in artificial intelligence.

Delivery
100% online
Duration
36 weeks
Admits
Monthly

Curriculum

Industry-approved curriculum to support your journey into Artificial Intelligence.

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Introduction to Python  – 3 weeks

This introduction to Python course covers essential programming concepts for data science. You’ll learn scripting basics, algorithms, and data structures like tuples and dictionaries, and explore Python libraries and functions. The course ends with a project where you’ll develop a Python script to analyze data, preparing you to tackle real-world data science challenges.

What you’ll learn:

  • Apply the basics of programming language methodologies to real world scenarios
  • Demonstrate foundational skills for scripting with a programming language, Python

 

Cloud Computing, Generative AI, and Dashboards – 3 weeks

This course focuses on cloud computing for cost-effective, scalable data processing. You’ll master technical components like PySpark to integrate Python, SQL, and Spark for handling structured and semi-structured data. Using libraries such as Numpy, Pandas, and PySpark, you’ll work with big data and create stunning visualizations with Python libraries like Seaborn. The course also explores advanced data analysis using generative AI and interactive dashboards, culminating in a project that brings big data to life through visualizations.

What you’ll learn:

  • Create a dashboard using data science methodologies with industry standard tool(s)
  • Model exploratory data analysis with tools for multiple data sets with SQL and SQL table relations
  • Utilize programming techniques to process large data samples with large-scale data processing like PySpark with big data

 

Introduction to Machine Learning
– 3 weeks

This course introduces the fundamentals of AI and machine learning, covering core concepts like statistical learning theory and supervised learning. You’ll explore models such as logistic regression, decision trees, and support vector machines, and learn to evaluate them using metrics like ROC AUCs. The course concludes with a project where you’ll select and deploy the ideal model for a specific task, demonstrating your mastery of the data science pipeline.

What you’ll learn:

  • Utilize foundational machine learning modeling like decision trees and supervised learning
  • Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
  • Utilize mathematics, statistics, and probability for data science methodologies to derive insights

Machine Learning with Scikit-Learn
– 3 weeks

This course covers both supervised and unsupervised machine learning models. You’ll learn about distance metrics and k-Nearest Neighbors for classification, recommender systems using SVD, clustering techniques like k-means, and dimensionality reduction with PCA. The course concludes with a project where you’ll build and demonstrate both a supervised (k-Nearest Neighbors) and an unsupervised (k-means) learning model, showcasing your skills in classification and clustering tasks.

What you’ll learn:

  • Utilize foundational machine learning modeling like decision trees and supervised learning
  • Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
  • Integrate mathematics, statistics, and probability for data science methodologies to derive insights

 

Natural Language Processing, Time Series, and Neural Networks – 3 weeks

This course teaches skills to build advanced models, focusing on natural language processing (NLP) with techniques like text classification and vectorization, time series analysis for managing and visualizing trends, and neural networks using Keras. The course culminates in a project where you’ll build and showcase three models: a language model, a time series model, and a basic neural network.

What you’ll learn:

  • Develop insights from language, time, and image data using neural networks and Natural Language Processing (NLP)
  • Integrate mathematics, statistics, and probability for data science methodologies to derive insights

 

‍Neural Networks and Similar Models – 3 weeks

This course builds on neural network fundamentals, teaching optimization techniques like normalization and regularization. You’ll explore Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) for forecasting and sequence data, and advanced models like transformers and BERT. The course concludes with a project where you’ll demonstrate your expertise by building an advanced neural network application.

What you’ll learn:

  • Create an advanced neural network application
  • Integrate mathematics, statistics, and probability for data science methodologies to derive insights

 

Large Language Models
– 3 weeks

This course covers large language models (LLMs) and their practical applications. You’ll learn to extract insights from text, time, and image data using neural networks and natural language processing (NLP). The course also integrates key concepts from mathematics, statistics, and probability to enhance your data and AI skills.

What you’ll learn:

  • ML Models and the open-source MLOps Stack
  • Data-Centric Approaches for LLMs
  • Leveraging model fine-tuning and prompt engineering to optimize output

Artificial Intelligence Capstone – 15 weeks

In this intensive capstone you will be tasked with developing 3 different portfolio projects and you will be expected to frame your projects around solving a business problem. You will bring together all of your skills from Foundations together to build 3 different methods: a regression model, a classification supervised mode, and a classified unsupervised model.

What you’ll learn:

  • Integrate data science process using at least one method of regression
  • Integrate data science process using at least one method of non-regression supervised learning
  • Integrate data science process at least one method of non-regression unsupervised learning

Tuition

Upfront: $12,000

Pay as You Go: $13,000
12 monthly payments of $1,210

Financed Tuition: $13,500
Monthly payments as low as $298

Have questions? Schedule an info session to talk with a Flatiron School representative.

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FAQs

“Our programs are not currently set up to accept military benefits, such as the GI Bill, as a form of payment directly from the student at this time. However, if your military benefits can be arranged to pay the school directly, this may be an option in rare cases.”

“No, you do not need a college degree to enroll in our programs. A high school diploma or GED is the only educational requirement. Our programs are designed to be accessible to a wide range of students with diverse backgrounds.”

“No, we do not accept FAFSA or traditional financial aid for our programs. However, we do offer loans for full-time students, as well as interest-free installment plans and upfront payment options for everyone else. Please contact us for more details about these flexible payment options.”

“It is VERY occasionally possible to skip the essentials program and go directly to Foundations I. However, we highly recommend that most students do not skip Essentials as it covers a tremendous amount of information and skills that will be used throughout the entire career pathway program and will require some catching up if skipped. The essentials program is still difficult and covers a great deal of material that is necessary for proceeding in the following programs and won’t be reviewed in Foundations. All of the future program material will build upon the essentials. If you would like to be considered to enter directly into the Foundations-level programs, you’ll be required to submit materials demonstrating your proficiency in the materials covered in the Essentials program.”

“Nope! This program is designed for complete beginners—no experience required.”