Associate to Bachelors in Machine Learning-Explore Potential of Machine Learning

Gain advanced skills in algorithms, data processing, and AI systems, transforming your associate degree into a competitive machine learning career

Pursue High-Demand Machine Learning Careers

Transitioning from an associate degree to a bachelor’s in machine learning significantly expands your professional scope. With a strong foundation in programming, statistics, and AI principles, you’ll be equipped to take on more specialized and high-paying roles. Graduates can explore diverse opportunities such as machine learning engineer, data scientist, AI research specialist, or business intelligence analyst. The growing adoption of AI technologies across industries—including healthcare, finance, e-commerce, and autonomous systems—ensures steady demand for skilled professionals. Your bachelor’s degree will not only make you more competitive in the job market but also prepare you for leadership positions and advanced research roles in the rapidly evolving field of machine learning.

Detailed Information About The Course

Introduction to E-Commerce

This course explains the fundamentals of analyzing, designing and building an E-Commerce website. The course is taught from the viewpoint of a web developer working with a small business client and covers such topics as proposals, contracts, site design, marketing, financial and billing, order processing, shipping and much more.

Introduction to Data Communication

This course is a thorough introduction to the concepts underlying networking technology, from physical carrier media to protocol. It includes historical material to show the logic behind the development of a given mechanism, and also includes comprehensive discussions of increasingly important material, such as B-ISDN and ATM.

Introduction to Computing

Introduction to Computing is designed to give you a better understanding of computing in order to give you a stronger foundation for later courses. The course is in two parts: (a) the underlying structure of a computer, and (b) operating systems and databases

Networking Basics

This course covers topics such as networking basics, network security, setting up TCP/IP and connecting to the Internet, and much more. It will greatly assist the Network administrators in knowing all the information that they need.

Introduction to Programming

This solid foundation in the basics of C++ programming will allow you to create efficient, elegant code ready for any production environment. This course will motivate you by demonstrating why you need to learn the concepts and skills presented.

Essentials of Computer Science

This course contains topics related to computer systems, architecture, Number Systems, Data Formats, The Little Man Computer, The Cpu And Memory, Computer Peripherals, Modern Computer Systems and Regular Languages And Regular Expressions.

Foundations of Information Technology

The goal of this course is to provide you with the foundations of information technologies and their use in knowledge-based organizations. It will also prepare you to understand and manage key technologies and the way they enable business initiatives that were inconceivable a few years ago.

Computer & Internet Search

The course lays emphasis on using the Internet as an educational tool including the use of web browsers, search engines and directories, email basics, acceptable use policies, equity, law and ethics, and evaluation and citation of Internet resources.

Fundamentals of Machine Learning

This course provides a comprehensive understanding of the fundamental principles of machine learning. Students will gain knowledge and skills in algorithm development, model training, and performance evaluation.

Introduction to Machine Learning

This introductory course covers the basics of machine learning, including essential concepts and practices for developing machine learning models. Students will learn about supervised and unsupervised learning, neural networks, and practical applications.

Machine Learning Principles

This course delves into the key principles of machine learning, emphasizing algorithm development, model optimization, and practical applications. Students will learn how to develop and deploy machine learning models effectively.

Machine Learning Analysis

This analytical course equips students with the skills to critically evaluate and improve machine learning models and practices. Students will learn how to use data and research to make informed decisions and optimize machine learning solutions.

Machine Learning Applications

This course offers practical experience in machine learning, focusing on real-world applications of machine learning principles. Students will engage in hands-on activities and projects to apply their knowledge effectively.

Machine Learning Strategies

This course focuses on strategic approaches to machine learning, providing students with advanced skills to develop and implement effective machine learning strategies. Topics include strategic planning, model deployment, and performance evaluation.

460+

Graduates past year

78%

Received Financial Assistance

90+

Job Opportunities

42

Expert Faculty Members

Why Choose Us

01

Industry-Relevant Curriculum

Our courses are designed with direct input from industry experts, ensuring that the skills you gain are current, practical, and aligned with real-world demands. You’ll graduate with knowledge that employers are actively seeking, giving you a competitive edge in the job market.

02

Flexible Learning Experience

We understand that every student has unique needs and commitments. That’s why our programs offer flexible schedules, online access, and personalized learning paths, allowing you to balance education with your career and personal life.

03

Expert Faculty & Support

Our dedicated faculty members bring both academic excellence and professional experience to the classroom. Combined with comprehensive student support services, you will have the guidance, mentorship, and resources you need to succeed at every stage of your learning journey.

We’ve Got Your Back, Every Step of the Way

At CUO, you’re never alone on your academic journey. From day one, you’ll have access to dedicated academic advisors, career counselors, writing and research support, peer tutoring, and mental health resources. Our online learning environments are designed to be interactive and collaborative, ensuring a smooth educational experience. Plus, you’ll benefit from networking events, industry guest lectures, and career workshops that prepare you for life beyond graduation.

Speak to a Consultant

Make Quality Education Affordable

We believe that financial constraints shouldn’t hinder your future. CUO offers a range of merit-based, need-based, and international student scholarships for accounting majors. You may qualify for up to 80% tuition coverage based on your academic achievements or financial need. Additional scholarships are available for working adults, women in business, and underrepresented communities. Apply early to secure your financial aid and make your accounting degree more accessible than ever.

Explore Scholarship Plans

What You Need to Enroll In This Program

Eligibility Criteria

High School Diploma, GED or equiv. International Education

Course Fee

$18,480

Credit Hours

Depends on subjects

Duration of Program

1 Year (Self-Paced) Program

What Our Students Say

From classroom learning to career breakthroughs, our students have seen it all. Their success stories highlight the impact of our teaching, support, and industry preparation.

“This course gave me a strong foundation and the flexibility to learn at my own pace. The faculty support was amazing!”
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Sarah Malik B.A. in Economics
“The course structure was practical and helped me apply knowledge directly in my job. Truly career-transforming!”
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Ahmed Khan Master’s in Finance
“Loved the flexibility and the focus on real-world examples. It made learning engaging and meaningful.”
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Ayesha Rauf Financial Analyst Trainee
“The instructors went above and beyond to ensure we understood concepts. I feel fully prepared for the next step in my career.”
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Daniel Joseph Associate in Marketing
“The resources and mentorship I received made this journey smooth. A brilliant learning experience!”
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Fatima Noor Ph.D. Candidate in Management
“The balance of theory and practical application was perfect. This course truly added value to my professional skillset.”
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Omar Shah Certificate in Data Analytics
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Course
Information

Copyright © 2025 Crestwood University of Ohio (CUO)

Introduction to E-commerce

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Overview of ec.
    E-commerce market mechanisms.
  In Section 2 of this course you will cover these topics:
    Reatailing in e-commerce: products and services.
    Consumer behavior, customer service, and advertising.
  In Section 3 of this exam you will be evaluated on below listed topics:
    Company-centric b2b and collaborative commerce.
    Public b2b exchanges.
  In Section 4 of this course you will cover these topics:
    Intrabusiness, e-government, c2c, e-learning, and more.
    Mobile commerce.
  In Section 5 of this course you will cover these topics:
    Laws, ethics, and cyber crime.
    Payments and order fulfillment.
    E-strategy, internet communities, and global ec.
    Infrastructure and application development.

Introduction to Data Communication

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Introduction and overview.
    Network classification.
    Network architectures and standards.
    Introduction to protocols.
  In Section 2 of this course you will cover these topics:
    Data coding.
    Data communication fundamentals.
    Data link control protocols.
    Transmission media.
  In Section 3 of this exam you will be evaluated on below listed topics:
    Communication circuits.
    Local area networks -lans-.
    Lan systems.
    Lan installation and operation.
  In Section 4 of this course you will cover these topics:
    Wide area networks -wans-.
    Internetworking.
    The internet.
    Internet applications.
  In Section 5 of this course you will cover these topics:
    Network security.
    Network design and implementation.
    Network management and operation.

Introduction to Computing

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Identifying types of computers
    Identifying computer hardware components and their functions
    Evaluating, purchasing, and maintaining computer equipment
    Identifying software and hardware interaction and types of software
  In Section 2 of this course you will cover these topics:
    Identifying operating system functions
    Using windows
    Taking a tour of windows applications
    Creating and editing files
  In Section 3 of this exam you will be evaluated on below listed topics:
    Formatting text and performing common printing functions
    Using graphic tools
    Working with tables
    Using outline, track changes, and language tools
  In Section 4 of this course you will cover these topics:
    Working with text and numbers in a spreadsheet
    Using formulas, functions, and charts
    Creating and delivering presentations
    Identifying network fundamentals
  In Section 5 of this course you will cover these topics:
    Identifying the relationships between computer networks, telephone networks, and the internet
    Understanding e-mail
    Identifying how to use e-mail
    Identifying information sources on the internet
    Using a web browser and searching the internet
    Identifying how computers are used at work, school, and home
    Using computers and the internet safely, legally, ethically, and responsibly

Networking Basics

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Introduction to computer networks.
    The physical layer -the media-.
    Computer fundamentals.
  In Section 2 of this course you will cover these topics:
    Interconnecting lans.
    Tcp/ip.
    Introduction to routers.
  In Section 3 of this exam you will be evaluated on below listed topics:
    Routing protocols.
    Wide area networking.
    Configuring and managing the campus network.
  In Section 4 of this course you will cover these topics:
    Network security.
    Wireless networking.
    Optical networks.
  In Section 5 of this course you will cover these topics:
    Voice over ip.
    The network server.
    Linux networking.

Introduction to programming

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Getting started with alice
    Program design and implementation
  In Section 2 of this course you will cover these topics:
    Programming: putting together the pieces
    Classes, objects, methods, and parameters
  In Section 3 of this exam you will be evaluated on below listed topics:
    Interaction: events and event handling
    Functions and if/else
  In Section 4 of this course you will cover these topics:
    Repetition: definite and indefinite loops
    Repetition: recursion
  In Section 5 of this course you will cover these topics:
    Lists and list processing
    Variables and revisiting inheritance

Essentials of computer science

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Introduction
    Data storage
  In Section 2 of this course you will cover these topics:
    Data manipulation
    Operating systems
  In Section 3 of this exam you will be evaluated on below listed topics:
    Networking and the internet
    Algorithms
    Programming languages
  In Section 4 of this course you will cover these topics:
    Software engineering
    Data abstractions
  In Section 5 of this course you will cover these topics:
    Database systems
    Artificial intelligence

Foundations of information technology

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Information technology: principles, practices, and opportunities.
    Essentials of computing.
  In Section 2 of this course you will cover these topics:
    Essentials of the internet and the worldwide web.
    The central processor and memory.
  In Section 3 of this exam you will be evaluated on below listed topics:
    Storage and input/output devices.
    Personal and pc databases.
  In Section 4 of this course you will cover these topics:
    Enterprise databases and warehouses.
    Enterprise and personal communications networks.
  In Section 5 of this course you will cover these topics:
    Electronic commerce and electronic business.
    Launching application development projects.
    Creating enterprise applications.
    Creating web-enabled applications.
    Information systems in the enterprise.
    Issues in information technology.

Computer & internet search

Topics Covered in This Course:
  In Section 1 of this course you will cover these topics:
    Lets get online already!
    The development of the internet
  In Section 2 of this course you will cover these topics:
    Finding resources on the world wide web
    Email
  In Section 3 of this exam you will be evaluated on below listed topics:
    Doing business on the web
    An introduction to html
  In Section 4 of this course you will cover these topics:
    An introduction to css, xhtml, and advanced web development concepts
    Html editors
    Graphics and multimedia on the world wide web
  In Section 5 of this course you will cover these topics:
    Mass communication tools
    Real-time communication
    Entertainment and education
    The internets impact on traditional media
    The internet and the law
    Keeping pace with innovations

Fundamentals of Machine Learning

Topics Covered in This Course:
  In Section 1 Principles of Machine Learning of this course you will cover these topics:
  • Core Concepts and Definitions
  • Machine Learning Frameworks
  • Role of Data Scientists
  In Section 2 Supervised Learning Techniques of this course you will cover these topics:
  • Advanced Supervised Learning Algorithms
  • Model Training and Validation
  • Performance Metrics for Supervised Learning
  In Section 3 Unsupervised Learning Techniques of this course you will cover these topics:
  • Advanced Unsupervised Learning Algorithms
  • Model Training and Validation
  • Performance Metrics for Unsupervised Learning
  In Section 4 Neural Networks and Deep Learning of this course you will cover these topics:
  • Deep Learning Fundamentals
  • Training Deep Neural Networks
  • Evaluating Deep Learning Models
  In Section 5 Applications of Machine Learning of this course you will cover these topics:
  • Natural Language Processing
  • Computer Vision
  • Predictive Analytics

Introduction to Machine Learning

Topics Covered in This Course:
  In Section 1 Overview of Machine Learning of this course you will cover these topics:
  • Introduction to Machine Learning
  • History and Evolution of Machine Learning
  • Key Components of Machine Learning
  In Section 2 Supervised Learning Basics of this course you will cover these topics:
  • Fundamentals of Supervised Learning
  • Common Algorithms in Supervised Learning
  • Evaluating Supervised Models
  In Section 3 Unsupervised Learning Basics of this course you will cover these topics:
  • Fundamentals of Unsupervised Learning
  • Common Algorithms in Unsupervised Learning
  • Evaluating Unsupervised Models
  In Section 4 Introduction to Neural Networks of this course you will cover these topics:
  • Basics of Neural Networks
  • Types of Neural Networks
  • Applications of Neural Networks
  In Section 5 Applications of Machine Learning of this course you will cover these topics:
  • Machine Learning in Different Industries
  • Benefits of Machine Learning
  • Real-World Applications

Machine Learning Principles

Topics Covered in This Course:
  In Section 1 Algorithm Development in Machine Learning of this course you will cover these topics:
  • Developing Machine Learning Algorithms
  • Optimizing Algorithm Performance
  • Implementing Machine Learning Models
  In Section 2 Model Optimization Techniques of this course you will cover these topics:
  • Hyperparameter Tuning
  • Model Selection and Evaluation
  • Overfitting and Underfitting
  In Section 3 Neural Networks and Advanced Learning of this course you will cover these topics:
  • Advanced Neural Network Architectures
  • Training and Optimizing Deep Learning Models
  • Transfer Learning and Pre-trained Models
  In Section 4 Practical Applications of Machine Learning of this course you will cover these topics:
  • Real-World Machine Learning Projects
  • Implementing Machine Learning Solutions
  • Case Studies in Machine Learning
  In Section 5 Ethics and Fairness in Machine Learning of this course you will cover these topics:
  • Addressing Bias in Machine Learning
  • Ensuring Fairness and Transparency
  • Ethical Considerations in Model Development

Machine Learning Analysis

Topics Covered in This Course:
  In Section 1 Machine Learning Data Analysis of this course you will cover these topics:
  • Collecting and Analyzing Data for Machine Learning
  • Using Data for Decision Making
  • Data Management Tools
  In Section 2 Model Performance Analysis of this course you will cover these topics:
  • Evaluating Model Accuracy and Precision
  • Identifying Model Inefficiencies
  • Implementing Improvements
  In Section 3 Algorithm Performance Analysis of this course you will cover these topics:
  • Assessing Algorithm Performance
  • Using Key Performance Indicators
  • Benchmarking Against Industry Standards
  In Section 4 Ethical and Regulatory Analysis of this course you will cover these topics:
  • Ensuring Ethical Model Development
  • Compliance with Data Protection Regulations
  • Improving Ethical Practices
  In Section 5 Research and Development in Machine Learning of this course you will cover these topics:
  • Conducting Machine Learning Research
  • Applying Research Findings
  • Developing Innovative Machine Learning Solutions

Machine Learning Applications

Topics Covered in This Course:
  In Section 1 Practical Machine Learning Projects of this course you will cover these topics:
  • Developing Machine Learning Models
  • Hands-On Machine Learning Activities
  • Real-World Machine Learning Challenges
  In Section 2 Algorithm Implementation Projects of this course you will cover these topics:
  • Implementing Machine Learning Algorithms
  • Training and Optimizing Models
  • Real-World Algorithm Challenges
  In Section 3 Industry-Specific Applications of this course you will cover these topics:
  • Applying Machine Learning in Healthcare
  • Machine Learning in Finance
  • Applications in Marketing and E-commerce
  In Section 4 Ethics and Compliance Projects of this course you will cover these topics:
  • Ensuring Ethical Model Development
  • Complying with Data Protection Laws
  • Real-World Ethical Challenges
  In Section 5 Professional Practice in Machine Learning of this course you will cover these topics:
  • Developing Professional Skills
  • Ethics and Standards in Practice
  • Building a Career in Machine Learning

Machine Learning Strategies

Topics Covered in This Course:
  In Section 1 Developing Machine Learning Strategies of this course you will cover these topics:
  • Strategic Planning Processes
  • Setting Machine Learning Objectives
  • Implementing Machine Learning Strategies
  In Section 2 Model Deployment and Scalability of this course you will cover these topics:
  • Deploying Machine Learning Models
  • Scaling Machine Learning Solutions
  • Managing Machine Learning Infrastructure
  In Section 3 Performance Evaluation and Monitoring of this course you will cover these topics:
  • Evaluating Model Performance
  • Continuous Monitoring and Improvement
  • Using Performance Metrics
  In Section 4 Integration with Business Processes of this course you will cover these topics:
  • Integrating Machine Learning into Business Operations
  • Aligning Machine Learning Goals with Business Objectives
  • Measuring Business Impact
  In Section 5 Innovation in Machine Learning of this course you will cover these topics:
  • Leveraging Cutting-Edge Technologies
  • Developing Innovative Machine Learning Solutions
  • Future Trends in Machine Learning