Machine Learning Specialist Diploma Program

COMPREHENSIVE CURRICULUM

All lesson plans and syllabi are subject to change based upon program choice

INTRODUCTION

The Machine Learning Specialist program offers an extensive and immersive learning experience tailored to prepare students for a successful career in the dynamic field of AI language model engineering. This curriculum encompasses a broad range of critical topics, ensuring a solid foundation in Programming, Natural Language Processing (NLP), Machine Learning, Data Analysis, Data Visualization, API Integration, Version Control, Experimentation and Evaluation, Optimization Techniques, and Software Development Best Practices. Through a combination of theoretical instruction and practical, hands-on exercises, students will develop a comprehensive understanding and skill set in prompt engineering, empowering them to address real-world challenges and excel in their professional endeavors at DSDT is an excellent choice.

 

OBJECTIVES

dTo prepare students for post-program success by providing a rich learning environment utilizing research-based methods of instruction and providing access to relevant and current resources and materials. Students will participate in a challenging and worthwhile diploma program based on current industry/academic expectations. The Machine Learning Specialist Program will provide students with a roadmap to gainful employment by instruction in 4.5 courses and practical hands-on experience with a capstone project.

COURSES

  • Introduction to Computer Programming
  • Introduction to Machine Learning
  • Deep Learning Foundations
  • Data Engineering, Deployment, & Management
  • Health and Safety and Job Readiness
  • Capstone Project

• Personal Computer with stable access to internet (highly recommended)
• 8-16GB USB Flash Drive or Portable USB or FireWire Hard Drive Dropbox account (Highly recommended)
• Notebook and/or sketch book

• Instructor Led Lecture
• Discussion/critique
• Interactive applications
• Virtual Lab time sessions

Many of the course research requirements and assignments will be fulfilled during lab time, a one-hour session during in class
meetings will be dedicated to projects and task assessments.

*Please reference Academic Policies and Procedures full Policy*

Aesthetic Evaluation Relates to your composition, design, and aesthetic approach as well as the originality and degree of
creativity demonstrated in the execution of your project.

Encompasses how well you understood the technical issue followed the parameters given and attention to the details necessary
for achieving the best solution. Evaluation on troubleshooting skills for hardware and software.

You will be evaluated on how well you apply your technical skills, follow through, your attention to finer details of your
execution including how well the project is technically put together and the appropriateness of those choices.

• 320 Clock Hours- Core Curriculum
• 40 Clock Hours- Health & Safety and Job Readiness
• 240 Clock Hours- Capstone Project

Each week will feature an interactive assignment and/or project that illustrates the section they have learned.

Pyhton, Scala,TensorFlow, PyTorch, spaCy, Django, Streamlit, Flask, Apache NiFi, Microsoft, Cengage, Canvas LMS and Campus Café

Attendance on a daily basis is a mandatory requirement for all students. Any class session or activity missed, regardless of
cause, reduces the opportunity for learning and may adversely affect a student’s achievement. Students are responsible to
instructors for class attendance and for any class work missed during an absence. Student is responsible to catch up on the
missing material on his/her own by contacting fellow classmates or instructor.

Tuition:  $17,995.00
600 Clock Hours = $29.99 clock/hour

There is a $25 returned/bounced check fee

The artificial intelligence industry needs YOU!

DSDT trains and pairs qualified students for high-demand  careers. To learn more about how you may enroll into our next Machine Learning Specialist Diploma Program, please fill out and submit the accompanying form.

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