TAKE YOUR BUSINESS INTO THE FUTURE WITH ARTIFICIAL INTELLIGENCE TRAINING PROGRAMS.

Consulting and training programs that increase efficiency and maintain quality by properly integrating artificial intelligence in customer service, communication, sales, stress management, and leadership; measurable and sustainable service models where AI-powered chatbots/voicebots, quality automation, intelligent routing, information management, and CRM processes work together.

Artificial Intelligence Training

Designed for participants of varying levels, this comprehensive training program offers a wide range of topics, from fundamental concepts to advanced technologies, and from ethical issues to the future.

CHAPTER 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND BASIC CONCEPTS

1.1 What is Artificial Intelligence?

• Definition and scope of artificial intelligence.

• A brief history of AI: From the Dartmouth Conference to the present day.

• Examples of AI in daily life.

1.2 AI vs Automation vs Algorithms

• Conceptual differences

• Differences between traditional programming and AI.

• Decision trees and rule-based systems

  
  

CHAPTER 2: MACHINE LEARNING (ML)

2.1 Introduction to Machine Learning

• What is ML and why is it important?

• Traditional programming vs. ML approach

• Applications of ML in the business world.

2.2 ML Types

Supervised Learning

• Classification

• Regression

• Examples: Spam filtering, price estimation.

Unsupervised Learning

• Clustering

• Size reduction

• Examples: Customer segmentation, anomaly detection.

Reinforcement Learning

• Agent, environment, reward concepts

• Examples: Gaming AI, robotics

Semi-Supervised and Transfer Learning

2.3 Popular ML Algorithms

• Decision Trees

• Random Forest

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Naive Bayes

• Linear/Logistic Regression

2.4 Stages of the ML Project

• Data collection and preparation

• Feature Engineering

• Model selection and training

• Evaluation metrics (Accuracy, Precision, Recall, F1-Score)

• Overfitting and Underfitting

CHAPTER 3: DEEP LEARNING (DL)

3.1 Introduction to Deep Learning

• What is DL? How does it differ from ML?

• Why "deep" learning?

• The rise of DL: GPUs and big data

3.2 Artificial Neural Networks (ANN)

• From biological neurons to artificial neurons

• Perceptrons and multilayer networks

• Activation functions (ReLU, Sigmoid, Tanh)

• Backpropagation algorithm

• Gradient Descent and Optimization

3.3 Advanced DL Architectures

Convolutional Neural Networks (CNN)

• CNN for image processing

• Convolution, Pooling layers

• Applications: Facial recognition, medical image analysis, autonomous vehicles.

Recurrent Neural Networks (RNN) and LSTM

• Sequential data processing

• Long-term and short-term memory (LSTM)

• Applications: Text generation, time series forecasting.

Generative Adversarial Networks (GAN)

• Producer and distinguishing networks

• Deepfake technology

• Its use in art and design.

Transformer Architecture

• Attention mechanism

• BERT, GPT models

• The basis of modern language models

3.4 Transfer Learning and Fine-Tuning

• The use of pre-trained models

• High performance with minimal data.

CHAPTER 4: NATURAL LANGUAGE PROCESSING (NLP)

4.1 Introduction to NLP

• Languages and computers

• The challenge of NLP: Uncertainty, context, language diversity.

4.2 Basic NLP Techniques

• Tokenization

• Stemming and Lemmatization

• Part-of-Speech (POS) Tagging

• Named Entity Recognition (NER)

• Word Embeddings (Word2Vec, GloVe)

4.3 Advanced NLP Applications

• Sentiment Analysis

• Machine translation

• Question and answer systems

• Text summarization

• Chatbots and conversational systems

4.4 Major Language Models (LLM)

• Models like GPT, Claude, Gemini

• Prompt Engineering fundamental principles

• The use of LLMs in the business world.

• RAG (Retrieval Augmented Generation)

CHAPTER 5: COMPUTER VISION

5.1 Fundamentals of Image Processing

• Digital images and pixels

• Image preprocessing techniques

5.2 CV Applications

• Object recognition and detection

• Facial recognition and biometric systems

• Optical character recognition (OCR)

• Medical image analysis

• Vision systems in autonomous vehicles

• Quality control and defect detection

5.3 Image Generation

• Stable Diffusion, DALL-E, Midjourney

• Text-to-Image applications

• Creative AI applications in business.

CHAPTER 6: BIG DATA AND AI

6.1 What is Big Data?

• 5V: Volume, Velocity, Variety, Veracity, Value

• Traditional data vs. Big data

• Data lakes and data warehouses

6.2 Big Data Technologies

• Hadoop ecosystem

• Apache Spark

• NoSQL databases

• Distributed systems

6.3 The Relationship Between Big Data and AI

• Data is the fuel of AI.

• Data quality and preprocessing

• Feature stores and ML infrastructure

• Real-time data processing and ML

CHAPTER 7: INTERNET OF THINGS (IoT) AND AI

7.1 IoT Fundamentals

• What is IoT?

• Sensors, actuators, connectors

• IoT architecture and protocols

7.2 IoT and AI Integration

• Edge AI: Artificial intelligence at the edge.

• Predictive maintenance

• Smart cities and smart homes

• Industrial IoT (IIoT)

• IoT and AI in Agriculture

7.3 Real-World Examples

• Smart thermostats

• Wearable health devices

• Factory 4.0 and digital twins

CHAPTER 8: VIRTUAL REALITY (VR), AUGMENTED REALITY (AR), AND AI

8.1 VR and AR Fundamentals

• What is VR? What is AR? What is MR (Mixed Reality)?

• Technological infrastructure and devices

8.2 AI-Enhanced VR/AR

• Intelligent NPCs (Non-Player Characters)

• Real-time translation and AR overlay

• Virtual assistants

• Procedural content generation

8.3 Application Areas

• Training and simulation

• Health and medical education

• Architecture and design

• Retail and e-commerce (Virtual trial)

• Metaverse concept

CHAPTER 9: AI AND CYBERSECURITY

9.1 The Use of AI in Cybersecurity

• Anomaly detection

• Threat intelligence

• Phishing and malware detection.

• SIEM systems and AI

• Behavioral analysis

9.2 Security of AI Systems

• Adversarial attacks

• Model poisoning

• Data poisoning

• Privacy-preserving ML

• Federated Learning

9.3 Ethical Hacking and AI

• AI in penetration tests

• Automated vulnerability scanning

• Social engineering with AI

CHAPTER 10: INDUSTRIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE

10.1 Finance and Banking

• Algorithmic trading

• Credit scoring

• Fraud detection

• Robo-advisors

• Risk management

10.2 Health

• Disease diagnosis

• Drug discovery

• Personalized medicine

• Radiology and pathology analysis

• Robotic surgery

10.3 Retail and E-commerce

• Recommendation systems

• Pricing optimization

• Customer segmentation

• Chatbots and virtual assistants

• Demand forecast

10.4 Production

• Quality control

• Predictive maintenance

• Supply chain optimization

• Robotics and automation

• Digital twin

10.5 Transportation

• Autonomous vehicles

• Traffic management

• Route optimization

• Logistics

10.6 Human Resources

• CV screening

• Talent matching

• Employee retention analysis

• Performance evaluation

10.7 Marketing

• Personalized campaigns

• Customer lifetime value estimation

• Social media analysis

• Content creation

CHAPTER 11: THE LEGAL ASPECTS OF ARTIFICIAL INTELLIGENCE

11.1 AI and Legislation

• EU AI Act

• GDPR and data protection

• AI regulations in Türkiye

• Sectoral regulations (finance, health, etc.)

11.2 Responsibility and Accountability

• Responsibility for AI decisions

• Debates on legal personality

• Product responsibility

• Malpractice and medical errors

11.3 Intellectual Property

• Copyrights of AI-generated content

• Patents and AI

• Data ownership

• Open source models and licensing

11.4 Contracts and Compliance

• AI terms of use

• Vendor management

• SLAs (Service Level Agreements)

• Audit and compliance requirements

CHAPTER 12: THE ETHICAL ASPECT OF ARTIFICIAL INTELLIGENCE

12.1 Fundamentals of AI Ethics

• Why is ethics important?

• Core ethical principles: Transparency, justice, privacy.

• Ethical frameworks and standards

12.2 Prejudice and Discrimination (Bias)

• How does algorithmic bias develop?

• Historical examples (Amazon's CV screening system, COMPAS)

• Bias in datasets

• Fairness metrics

• Strategies to reduce prejudice

12.3 Transparency and Explainability

• The black box problem

• XAI (Explainable AI) methods

• Tools like LIME and SHAP

• Right to explain

12.4 Privacy and Data Protection

• Differential Privacy

• Data minimization

• Anonymization vs. De-anonymization

• Protection of personal data

12.5 Unemployment and Economic Impact

• Automation and job losses

• New job opportunities

• Skills transformation

• Universal basic income debates

12.6 Manipulation and Disinformation

• Deepfakes

• Propaganda and microtargeting

• Echo chambers

• Social media algorithms

12.7 Autonomous Weapons and Military Use

• Killer robots (Lethal autonomous weapons)

• Ethics of war

• International regulations

12.8 Sustainability

• Carbon footprint of AI models

• Energy consumption

• Green AI

CHAPTER 13: AI PROJECT MANAGEMENT AND IMPLEMENTATION

13.1 Starting the AI Project

• Business problem definition

• Is AI suitable? (When not to use AI)

• Data exploration and feasibility.

• ROI calculation

13.2 Team Building

• Required roles: Data scientist, ML engineer, data engineer

• The importance of domain specialists

• MLOps teams

13.3 AI Project Methodologies

• CRISP-DM

• Agile for ML

• Iterative development

13.4 MLOps: Taking it to Production

• Model versioning

• CI/CD for ML

• Model monitoring

• A/B testing

• Model retraining strategies

13.5 Common Mistakes and Prevention

• Data leakage

• Incorrect metric selection

• Production-training gap

• Technical debt

CHAPTER 14: AI TOOLS AND PLATFORMS

14.1 Programming and Frameworks

• Python ecosystem

• TensorFlow and Keras

• PyTorch

• Scikit-learn

• Hugging Face

14.2 Cloud AI Services

• AWS AI/ML services

• Google Cloud AI

• Microsoft Azure AI

• IBM Watson

14.3 No-Code / Low-Code Platforms

• AutoML tools

• Google AutoML

• DataRobot

• H2O.ai

14.4 Data Labeling and Annotation

• Labelbox

• Scale AI

• Amazon SageMaker Ground Truth

14.5 Model Deployment

• TensorFlow Serving

• ONNX

• Containerization (Docker, Kubernetes)

CHAPTER 15: THE FUTURE OF ARTIFICIAL INTELLIGENCE

15.1 Technological Trends

• Multimodal AI

• Foundation models and large models

• Quantum Machine Learning

• Neuromorphic computing

• Brain-computer interfaces

15.2 On the Path to General Artificial Intelligence (AGI)

• What is AGI and when can it occur?

• Superintelligence scenarios

• Alignment problem

• Existential risks

15.3 Social Transformations

• The evolution of the education system

• The future of work

• Democracy and governance

• A revolution in healthcare.

15.4 Regulatory Future

• Global AI governance

• Standards and certification

• International cooperation

15.5 Ethics and Social Preparation

• AI literacy

• Social dialogue

• Inclusive AI development

• Diversity and representation

CHAPTER 16: PRACTICAL WORKSHOPS AND EXAMPLES

16.1 Hands-on Exercises

• Creating a simple ML model (Jupyter Notebook)

• Prompt engineering with ChatGPT/Claude

• Training a model with the AutoML tool.

• Image classification demo

16.2 Case Studies

• Netflix recommendation system

• Tesla autonomous driving

• AlphaGo and gaming AI.

• GPT models and language revolution

16.3 Interactive Q&A and Scenarios

• Examples specific to the participants' sector.

• AI solutions to real-world problems.

CHAPTER 17: RESOURCES AND FURTHER LEARNING

17.1 Recommended Resources

• Online courses (Coursera, edX, Udacity)

• Books (Pattern Recognition, Deep Learning Book)

• Research resources (arXiv, Papers with Code)

• Podcasts and YouTube channels

17.2 Communities

• Kaggle

• GitHub

• Reddit ML community

• Local meetups

17.3 Certifications

• Google TensorFlow Certificate

• AWS ML Specialty

• Microsoft Azure AI Engineer

BONUS: AI DICTIONARY

Turkish-English equivalents and explanations of all technical terms used throughout the training.

Presentation Format Suggestions:

Total Duration: 1 day of basic, intensive training or 2 days of detailed, practical program.

Target Audience-Based Adaptation:

• For managers: Focus on Chapters 1, 10, 11, 12, and 15.

• For technical teams: Focus on sections 2, 3, 4, 5, 13, and 14.

• For mixed groups: Balanced selection from all sections.

Visual Material:

• Each section contains plenty of infographics.

• Real-world examples and videos

• Interactive demonstrations

• Live coding examples