Explore our University, College, and Community College Courses
- Intro to AI
- Machine Learning I
- Machine Learning II
- Deep Learning I
- Deep Learning II
- Computer Vision
- Natural Language Processing
Introduction to Artificial Intelligence
Full semester for credit
Learn foundational AI with theoretical knowledge, applications and hands-on programming for desktop, mobile and web
Prerequisites
- Introduction to programming
- Database Development
Key Topics
Functioning of Hardware, Software and Networks
Python Programming
Data Structures
Algorithm Analysis
Software Development Methodologies, Testing and Version Control
Web Applications and APIs Creation and Deployment
Database – SQL, NOSQL
Future Career Opportunities
Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Machine Learning I
Full semester for credit
Learn the key concepts and applications of supervised and unsupervised learning; evaluate, select, test and program Machine Learning models
Prerequisites
- Introduction to Artificial Intelligence
- Python Programming
- Basic Probability
Key Topics
Machine Learning Fundamentals
Linear Regression
Logistic Regression
Regularization
Neural Network
Decision Trees
Text and Image Processing
Naïve Bayes
k-Nearest Neighbors
Deployment of Machine Learning Models
Future Career Opportunities
Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Data Analyst
Machine Learning II
Full semester for credit
Learn advanced Machine Learning concepts, such as reinforcement algorithms and how to evaluate, build, test and deploy models
Prerequisites
- Machine Learning I
- Linear Algebra and Calculus
- Probability and Statistics
Key Topics
Data Cleaning
Machine Learning Workflow
Linear Regression
Decision Tress
Ensemble Methods
k-Nearest Neighbors
Support Vector Machines
Probabilistic Models
Clustering
Dimensionality Reduction
Time Series Analysis
Reinforcement Learning
Future Career Opportunities
Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Deep Learning I
Full semester for credit
Master Deep Learning concepts and applications, build, train, test, and deploy Deep learning models
Prerequisites
- Machine Learning I
Key Topics
Deep Learning Mathematical and Programming Concepts ap Applications
Deep Learning Components, such as Vanishing and Exploding Gradiep Algorithms, Optimization Methods, Normalization and Regularization
Convolutional Neural Networks
Recurrent Neural Networks
Unsupervised Deep Learning
Deep Learning Model Development, Test and Deployment
Future Career Opportunities
Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Deep Learning II
Full semester for credit
Master advanced Deep learning concepts and applications; build and deploy advanced models in production
Prerequisites
- Deep Learning I
Key Topics
Computational Graphs and Back Propagation
Advanced Concepts and Applications of Activation Function, Weigp Initialization, Optimization Method, Adam Optimizer, Normalization and Hyperparameter Tuning
Convolutional Neural Networks
Recurrent Neural Networks
Attention and Neural Computation
Deep Unsupervised Learning
Applied Deep Learning
Deep Reinforcement Learning
Future Career Opportunities
Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Computer Vision
Full semester for credit
Learning theoretical knowledge and applications of Computer Vision, build and deploy models in productions
Prerequisites
- Machine Learning I
- Deep Learning I
Key Topics
Image Formation and Representation
Object Recognition
Image Processing
Segmentation
Feature Detection and Matching
Video Processing
Image classification
Deep Learning Methods in Computer Vision
Future Career Opportunities
Computer Vision Engineer, Machine Learning Engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Natural Language Processing
Full semester for credit
Learning the key concepts and applications Natural Language Processing, build and deploy models in production
Prerequisites
- Machine Learning II
- Deep Learning II
Key Topics
Natural Language Processing Fundamentals
Machine Learning for NLP
Language models
Word Embeddings
Markov models
Sequence Models
Syntax and parsing
Attention
Semantics, Pragmatics and Discourse
Contextual Embedding and Pretraining
Future Career Opportunities
Natural Language Processing engineer, Machine Learning engineer, Data Scientist, Software Developer, Software Engineer, Software Architect, Robotics Programmer, Data Analyst, Full Stack Developer
Ready to deploy within 4-8 weeks lead time, based on partner school’s technology requirements
For more information about our education programs