Advanced AI/ML – Hybrid Classroom + Online Training [AI101]

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About Course

Course Description

Advanced AI/ML [AI101] is a 36-clock-hour, short-term, non-degree training program that builds upon foundational Python, data, and machine-learning concepts. The course introduces students to advanced machine-learning techniques, deep learning, neural networks, natural language processing, generative AI, and practical AI/ML model development.

Students participate in instructor-led instruction, coding exercises, model-development activities, and hands-on projects designed to apply AI/ML concepts to practical problems.

Learning Objectives

Upon successful completion, students should be able to:

• Apply intermediate and advanced machine-learning concepts.
• Prepare and transform data for machine-learning applications.
• Apply feature-engineering and model-selection concepts.
• Explain neural networks and deep-learning fundamentals.
• Apply introductory natural-language-processing techniques.
• Explain generative AI and large language model concepts.
• Evaluate and improve machine-learning models.
• Apply AI/ML concepts through instructor-guided projects.

Program Information

Course Code: AI101
Duration: 36 Clock Hours
Delivery Method: Hybrid – Classroom + Online
Credential: AITE Certificate of Completion

Program Disclosure

This is a short-term, non-degree training program. Successful completion results in an AITE Certificate of Completion. AITE does not guarantee employment, job placement, salary, professional licensure, or third-party certification.

Prerequisites

Foundational knowledge of Python, data analysis, and machine-learning concepts is recommended. Completion of AI100 or equivalent knowledge is recommended.

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What Will You Learn?

  • Applied machine-learning techniques
  • Data preparation and feature engineering
  • Model selection and hyperparameter-tuning concepts
  • Machine-learning model evaluation and improvement
  • Neural-network and deep-learning fundamentals
  • Natural-language-processing concepts
  • Generative AI and large language model fundamentals
  • Prompt engineering and LLM applications
  • Responsible AI, model evaluation, and limitations
  • Hands-on Python and AI/ML model-development exercises
  • Instructor-guided advanced AI/ML project

Course Content

Weekend 1: Applied Machine Learning – 12 Clock Hours
Goal: Get exposure to real-world AI applications.

  • Data Preparation & Feature Engineering (3 hrs)
  • Applied Machine Learning Exercise (3 hrs)
  • Advanced Supervised Learning Concepts (3 hrs)
  • Model Selection & Hyperparameter Tuning (3 hrs)

Weekend 2: Deep Learning & NLP – 12 Clock Hours
Goal: Understand and implement deep learning models.

Weekend 3: Generative AI & Applied AI/ML Project – 12 Clock Hours
Goal: Learn real-world deployment and AI integration.