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AI Training 【Participants will gain a comprehensive overview of AI while deepening their technical understanding of Machine Learning and Deep Learning.】

Our AI trainings cover a comprehensive overview of artificial intelligence while deepening knowledge regarding machine learning.

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Addressing the shortage of AI talent and accelerating digital transformation

Who Benefits the Most

New Employee Young employees Experienced employees Managers Engineers Non engineers Contract workers Foreign employees Sales people Full time employees

For teams ready to master AI fundamentals and integrate Machine Learning and Deep Learning into their daily operations.
For teams who want to stop just 'collecting' data and start using it to drive growth.
For forward-thinking units looking to demystify AI and start applying it to solve real business challenges.

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Lineup Overview Feature Curriculum

2026/05/13 Information updated

Training in this category helps resolve further issues and concerns

I’m interested in AI, but I’m not sure how to practically integrate it into our company’s operations.
I want to explore the practical applications of artificial intelligence and data analysis.
I want to understand the specific applications of machine learning and deep learning.
I want to learn how to use AI effectively for generating ideas.

AI Training Overview

These programs are highly recommended for organizations seeking to improve workplace efficiency. For details on generative AI (such as ChatGPT, Gemini, and Copilot), please refer to our dedicated generative AI Training section.

What is AI training?

This AI training program covers generative AI fundamentals (such as ChatGPT), its practical applications in business including prompt engineering, risk management strategies and security.
In addition, this training provides a range of effective themes tailored to specific needs, from complete beginners with no prior AI knowledge to experienced users seeking to use it more effectively.

What is AI (Artificial Intelligence)?

AI refers to technology that enables computer systems to simulate human perception, cognition, reasoning, and learning. By utilizing data and algorithms, AI can identify optimal solutions and facilitate decision-making.

What is Generative AI (GenAI)?

GenAI is a branch of artificial intelligence that uses deep learning models to create new, original content rather than simply analyzing or classifying existing data. By identifying complex patterns and structures within massive datasets, these models can autonomously generate high-fidelity outputs, including text, images, code, and audio, that mimic human-like creativity and reasoning.

Differences between AI, Generative AI, and AGI

The following section outlines the key differences between AGI and ASI. They can be considered as a step further than simply "creating something new," as it is possible to see with currently popular chatGPT and Gemini.

	Explanation	Specific Example
Narrow AI (ANI)	A "Specialist." It is built to do one specific job really well but cannot do anything else.	Language Translation: Google Translate can convert text but cannot drive a car.
Gen AI (GenAI)	This refers to AI that learns from existing data to generate new content (text and images, etc.). GenAI uses deep learning and large language models (LLMs) to generate entirely new outputs.	Content Creation: ChatGPT (text) or Midjourney (images) based on your prompts.
General AI (AGI)	AGI is a hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task that a human can do. While GenAI can write a poem and another AI can drive a car, an AGI could do both, while also learning to perform surgery, fix a sink, or invent a new branch of physics without needing specific training for each. It’s a peer that has the same flexible thinking and learning ability as a human being.	Theoretical: An AI that could learn to be an accountant, a chef, and a coder all at once.
Super AI (ASI)	A "Mastermind." AI that is significantly smarter than the smartest human in every single field. It is capable of handling highly complex tasks and continuously improving its own capabilities over time.	Theoretical: A system that solves global climate change or invents new laws of physics instantly.

The benefits of mastering AI

Developing AI-proficient employees is a vital requirement for modern enterprises. The ability to use AI into business operations offers the following strategic advantages:

Increased productivity

By utilizing AI, you can achieve major results in a limited amount of time. Tasks that require human effort can be supported by AI to get optimal results quickly. While human verification remains essential, AI’s ability to speed the process enhances overall productivity.

Reducing mistakes

By using AI, it is possible to minimize human error significantly. Integrating AI into a company's systems is a concrete example. Reducing errors will also lead to the productivity improvements discussed above.

Focusing on high value tasks

Human resources are limited. For this reason, organizations and companies should prioritize their efforts on high value tasks that lead to innovation, improvement, and proposals, such as "thinking," "generating new ideas," and "developing strategies."

To achieve this, it's necessary to automate as much of the work as possible and use AI to free up time. Moreover, AI addresses labor shortages and offers long-term cost reduction, leading to its rapid adoption across companies.

What is the future of AI professional development?

Companies are required to develop capable workforce who can use AI with these benefits in their day-to-day work. These are known as “AI professionals” (individuals who possess knowledge of machine learning, deep learning and data science, and are able to apply it in practice). According to the Ministry of Economy, Trade and Industry’s ‘Survey on the Supply and Demand of IT Professionals (April 2019)’, the industry faces a projected shortage of 124,000 AI-skilled professionals, making AI professional development an urgent priority for companies.

These training programs include not only AI training, but also machine learning training , DX training , and ChatGPT training (understanding and application), offering a wide range of options to teach specific usage methods. These programs can be tailored to the organization's needs for those who want to develop AI professionals, so please feel free to contact us.

Features of AI Training

These AI training programs provide the following features:

High-quality teaching materials and curriculum structure

The program provides concise, easy-to-understand textbooks and practical workshops that can be used in a short amount of time, enabling participants to efficiently progress from foundational concepts to advanced applications.

Instruction tailored step-by-step

The scope of artificial intelligence covers a very broad range of topics. For this reason, this curriculum is designed systematically step-by-step, from fundamental technologies to practical applications. The program also includes algorithmic expressions and mathematical formulas as needed, allowing students to clarify specific processing steps and deepen their understanding of artificial intelligence.

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Lineup of AI Training

AI Training

Our AI trainings cover a comprehensive overview of artificial intelligence while deepening knowledge regarding machine learning.

Training Course Title		Training Duration
	In-company AI Fundamentals Training [Understanding Machine Learning & Deep Learning]	7 hours (Can be changed)

Training Curriculum

Here is an example of a curriculum for AI Training . Please use it as a reference for the flow of the training.

01Fundamentals of AI
Goal
Understand the overall landscape of AI
- Ice-breaker: Think of AI examples from everyday life
- Definition of AI: What is AI / Strong AI vs. Weak AI
- History of AI: First AI Boom (late 1950s–1960s) / Second AI Boom (1980s–1990s) / Third AI Boom (2000s–present)
- Overview of AI Technology
- Core Technologies: Bayesian Statistics / Machine Learning / Deep Learning / Generative AI
- Programming Languages and Frameworks for AI
- Activity: The difference between IT and AI
02Machine Learning
Goal
Understand the principles and various methods of machine learning
- What is Machine Learning: What machine learning can do / Before machine learning / The evolution of data-driven learning
- Problems Machine Learning Addresses: Regression / Classification
- 3 Types of Learning: Supervised Learning / Unsupervised Learning / Reinforcement Learning
- How Machine Learning Works: Basics of machine learning / Rental pricing for homestays / Regression / Feature variables
- The Importance of Data and How to Handle It: Importance of data volume / Data formatting / Test data/Types of Algorithms: K-Nearest Neighbors / Decision Trees / Support Vector Machines
- Machine Learning Examples: Selecting high-probability leads / Sales forecasting
- Activity: Where machine learning can be applied
03Deep Learning
Goal
Understand the principles and characteristics of deep learning
- What is Deep Learning: What deep learning is / Features and capabilities of deep learning
- Neural Networks: What is a neural network / Perceptrons
- Layered Neural Networks: Neural networks / Enabling complex computations
- How Deep Learning Works: Deep neural networks / Forward propagation / Backpropagation
- Activity: Try forward propagation
- Classification Output Data: Output overview / Probability distribution
- Transfer Learning: What is transfer learning / Benefits of transfer learning / What does it enable? / Implementation overview
04Natural Language Processing and Image Recognition
Goal
Understand the basics of natural language processing and image recognition
- Input Data for Deep Learning: Converting all electronic data into numbers for processing / Applications to NLP and image recognition
- Natural Language Processing: What is natural language / Natural language input / Algorithms / Use cases for NLP
- Image Recognition: What is image recognition / Input data / Convolutional Neural Networks (CNN) / Output results / Learning process / Use cases for image recognition
- Activity: Where deep learning can be applied
- Deep Learning Use Cases: Sentiment analysis via surveys / Product defect detection
05Generative AI (GAI)
Goal
Understand the basics of generative AI and how to apply it
- What is Generative AI (GAI): Using deep learning to create new content
- Text Generation AI: How it works: Predicting the next most probable word / How text generation AI learns
- Real-World Examples of Text Generation AI: ChatGPT / Key learning points / Usage information points
- Image Generation AI: How image generation AI learns / Generative Adversarial Networks
- Real-World Examples of Image Generation AI: Adding and subtracting image information
- Cautions When Using Generative AI: Probabilistic generation / Cautions regarding use cases
LASTThinking About AI Application in Your Organization
Goal
Consider how to apply AI within your own organization
- Cautions for Business Use of AI: Probability-based premise / Use IT when 100% accuracy is required
- Data Collection: The mindset of collecting data just in case / Sites that leverage big data / Internal data
- Activity: Think about how to apply AI in your organization: Identifying current challenges / Defining the problem / Solidifying specific AI ideas