The AI Engineer Blueprint

An end-to-end, industry-aligned training program designed to take you from core programming fundamentals to deploying scalable, production-grade artificial intelligence systems

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Course curriculum

1. Lesson 1 — Building an AI tool in 5 minutes_ A quick demo
2. Lesson 2 — What does the course cover
3. Lesson 3 — Natural vs Artificial Intelligence
4. Lesson 4 — Demystifying AI, Data science, Machine learning, and Deep learning
5. Lesson 13 — Robotics
6. Lesson 5 — Weak vs Strong AI
7. Lesson 6 — Structured vs unstructured data
8. Lesson 7 — How we collect data
9. Lesson 8 — Labelled and unlabeled data
10. Lesson 9 — Metadata_ Data that describes data
11. Lesson 10 — Machine learning
12. Lesson 11 — Supervised, Unsupervised, and Reinforcement learning
13. Lesson 12 — Deep learning
14. Lesson 14 — Computer vision
1. Lesson 15 — Traditional ML
2. Lesson 16 — Generative AI
3. Lesson 17 — The rise of Gen AI_ Introducing ChatGPT
4. Lesson 18 — Early approaches to Natural Language Processing (NLP)
5. Lesson 19 — Recent NLP advancements
6. Lesson 20 — From Language Models to Large Language Models (LLMs)
7. Lesson 21 — The efficiency of LLM training. Supervised vs Semi-supervised learning
8. Lesson 22 — From N-Grams to RNNs to Transformers_ The Evolution of NLP
9. Lesson 23 — Phases in building LLMs
10. Lesson 24 — Prompt engineering vs Fine-tuning vs RAG_ Techniques for AI optimization
11. Lesson 25 — The importance of foundation models
12. Lesson 26 — Buy vs Make_ foundation models vs private models
13. Lesson 28 — Budgeting and API costs
14. Lesson 29 — Latency
15. Lesson 30 — Running out of data
16. Lesson 27 — Inconsistency and hallucination
1. Lesson 31 — Python programming
2. Lesson 32 — Working with APIs
3. Lesson 33 — Vector databases
4. Lesson 34 — The importance of open source
5. Lesson 35 — Hugging Face
6. Lesson 36 — LangChain
7. Lesson 37 — AI evaluation tools
8. Lesson 38 — AI strategist
9. Lesson 39 — AI developer
10. Lesson 40 — AI engineer
11. Lesson 41 — AI ethics
12. Lesson 42 — Future of AI
13. Lesson 43 — Programming Explained in a Few Minutes
1. Lesson 44 — Why Python
2. Lesson 45 — Jupyter - Introduction
3. Lesson 46 — Jupyter - Installing Anaconda
4. Lesson 47 — Jupyter - Introduction to Using Jupyter
5. Lesson 48 — Jupyter - Working with Notebook Files
6. Lesson 49 — Jupyter - Using Shortcuts
7. Lesson 50 — Jupyter - Handling Error Messages
8. Lesson 51 — Jupyter - Restarting the Kernel
9. Lesson 52 — Python Variables
10. Lesson 53 — Python Coding Exercises
11. Lesson 54 — Types of Data - Numbers and Boolean Values
12. Lesson 55 — Types of Data - Strings
13. Lesson 56 — Anaconda AI - Introduction
14. Lesson 57 — Using the Anaconda Assistant_ Strings
15. Lesson 58 — Basic Python Syntax - Arithmetic Operators
16. Lesson 59 — Basic Python Syntax - The Double Equality Sign
17. Lesson 60 — Basic Python Syntax - Reassign Values
18. Lesson 61 — Basic Python Syntax - Add Comments
19. Lesson 62 — Basic Python Syntax - Line Continuation
20. Lesson 63 — Basic Python Syntax - Indexing Elements
21. Lesson 64 — Basic Python Syntax - Indentation
22. Lesson 65 — Operators - Comparison Operators
23. Lesson 66 — Operators - Logical and Identity Operators
1. Lesson 67 — Conditional Statements - The IF Statement
2. Lesson 68 — Conditional Statements - The ELSE Statement
3. Lesson 69 — Conditional Statements - The ELIF Statement
4. Lesson 70 — Conditional Statements - A Note on Boolean Values
5. Lesson 71 — Functions - Defining a Function in Python
6. Lesson 72 — Functions - Creating a Function with a Parameter
7. Lesson 73 — Functions - Another Way to Define a Function
8. Lesson 74 — Functions - Using a Function in Another Function
9. Lesson 75 — Functions - Combining Conditional Statements and Functions
10. Lesson 76 — Functions - Creating Functions Containing a Few Arguments
11. Lesson 77 — Functions - Notable Built-in Functions in Python
1. Lesson 78 — Sequences - Lists
2. Lesson 79 — Sequences - Using Methods
3. Lesson 80 — Sequences - List Slicing
4. Lesson 81 — Sequences - Tuples
5. Lesson 82 — Sequences - Dictionaries
6. Lesson 83 — Iteration - For Loops
7. Lesson 84 — Iteration - While Loops and Incrementing
8. Lesson 85 — Iteration - Create Lists with the range() Function
9. Lesson 86 — Iteraion - Use Conditional Statements and Loops Together
10. Lesson 87 — Iteration - Conditional Statements, Functions, and Loops
11. Lesson 88 — Using the Anaconda Assistant_ Several Python Tools
12. Lesson 89 — Iteration - Iterating over Dictionaries
13. Lesson 90 — Using the Anaconda Assistant_ Dictionaries
14. Lesson 91 — Introduction to Object Oriented Programming (OOP)
15. Lesson 92 — Modules, Packages, and the Python Standard Library
16. Lesson 93 — Importing Modules
17. Lesson 94 — What is Software Documentation
18. Lesson 95 — The Python Documentation
1. Lesson 96 — Introduction to NLP
2. Lesson 97 — NLP in everyday life
3. Lesson 98 — Supervised vs unsupervised NLP
4. Lesson 99 — The importance of data preparation
5. Lesson 100 — Text tagging
6. Lesson 101 — What is sentiment analysis
7. Lesson 102 — What is topic modelling
8. Lesson 103 — When to use topic modelling
9. Lesson 104 — Latent Dirichlet Allocation (LDA)
10. Lesson 105 — Latent Semantic Analysis (LSA)
1. Lesson 106 — Building a custom text classifier
2. Lesson 107 — Introducing the project
3. Lesson 108 — Exploring our data through POS tags
4. Lesson 109 — Extracting named entities
5. Lesson 110 — Processing the text
6. Lesson 111 — Does sentiment differ between news types
7. Lesson 112 — What topics appear in fake news_ (Part 1)
8. Lesson 113 — What topics appear in fake news_ (Part 2)
9. Lesson 114 — Categorizing fake news with a custom classifier
1. Lesson 115 — What is deep learning
2. Lesson 116 — Deep learning for NLP
3. Lesson 117 — Non-English NLP
4. Lesson 118 — What's next for NLP
5. Lesson 119 — Introduction to the course
6. Lesson 120 — What are LLMs
7. Lesson 121 — How large is an LLM
8. Lesson 122 — General purpose models
9. Lesson 123 — Pre-training and fine tuning
10. Lesson 124 — What can LLMs be used for
11. Lesson 125 — Deep learning recap
12. Lesson 126 — The problem with RNNs
13. Lesson 127 — The solution_ attention is all you need
14. Lesson 128 — The transformer architecture
15. Lesson 129 — Input embeddings
16. Lesson 130 — Multi-headed attention
17. Lesson 131 — Feed-forward layer
18. Lesson 132 — Masked multihead attention
19. Lesson 133 — Predicting the final outputs
1. Lesson 134 — What does GPT mean
2. Lesson 135 — The development of ChatGPT
3. Lesson 136 — OpenAI API
4. Lesson 137 — Generating text
5. Lesson 138 — Customizing GPT output
6. Lesson 139 — Key word text summarization
7. Lesson 140 — Coding a simple chatbot
8. Lesson 141 — Introduction to LangChain in Python
9. Lesson 142 — LangChain
10. Lesson 143 — Adding Custom Data to Our Chatbot
11. Lesson 144 — Hugging Face Package
12. Lesson 145 — The Transformer Pipeline
13. Lesson 146 — Pre-trained Tokenizers
14. Lesson 147 — Special Tokens
15. Lesson 148 — Hugging Face and PyTorch/TensorFlow
16. Lesson 149 — Saving and Loading Models
1. Lesson 150 — GPT vs BERT
2. Lesson 151 — BERT Architecture
3. Lesson 152 — Loading the Model and Tokenizer
4. Lesson 153 — BERT Embeddings
5. Lesson 154 — Calculating the Response
6. Lesson 155 — Creating a QA Bot
7. Lesson 156 — BERT, RoBERTa, DistilBERT
8. Lesson 157 — GPT vs BERT vs XLNET
9. Lesson 158 — Preprocessing Our Data
10. Lesson 159 — XLNet Embeddings
11. Lesson 160 — Fine Tuning XLNet
12. Lesson 161 — Evaluating Our Model
1. Lesson 162 — Introduction to the Course
2. Lesson 163 — Business Applications of LangChain
3. Lesson 164 — What Makes LangChain Powerful
4. Lesson 165 — What Does the Course Cover?
5. Lesson 166 — Tokens
6. Lesson 167 — Models and Prices
7. Lesson 168 — Setting Up a Custom Anaconda Environment for Jupyter Integration
8. Lesson 169 — Obtaining an OpenAI API Key
9. Lesson 170 — Setting the API Key as an Environment Variable
10. Lesson 171 — First Steps
11. Lesson 172 — System, User, and Assistant Roles
12. Lesson 173 — Creating a Sarcastic Chatbot
13. Lesson 174 — Temperature, Max Tokens, and Streaming
14. Lesson 175 — The LangChain Framework
15. Lesson 176 — ChatOpenAI
16. Lesson 177 — System and Human Messages
17. Lesson 178 — AI Messages
1. Lesson 179 — Prompt Templates and Prompt Values
2. Lesson 180 — Chat Prompt Templates and Chat Prompt Values
3. Lesson 181 — Few-shot Chat Message Prompt Templates
4. Lesson 182 — String Output Parser
5. Lesson 183 — Comma-separated List Output Parser
6. Lesson 184 — Datetime Output Parser
7. Lesson 185 — Piping a Prompt, Model, and an Output Parser
8. Lesson 186 — Batching
9. Lesson 187 — Streaming
10. Lesson 188 — The Runnable and RunnableSequence Classes
11. Lesson 189 — Piping Chains and the RunnablePassthrough Class
12. Lesson 190 — Graphing Runnables
13. Lesson 191 — RunnableParallel
14. Lesson 192 — Piping a RunnableParallel with Other Runnables
15. Lesson 193 — RunnableLambda
16. Lesson 194 — The @chain Decorator
1. Lesson 195 — How to Integrate Custom Data into an LLM
2. Lesson 196 — Introduction to RAG
3. Lesson 197 — Introduction to Document Loading and Splitting
4. Lesson 198 — Introduction to Document Embedding
5. Lesson 199 — Introduction to Document Storing, Retrieval, and Generation
6. Lesson 200 — Indexing — Document Loading with PyPDFLoader
7. Lesson 201 — Indexing — Document Loading with Docx2txtLoader
8. Lesson 202 — Indexing — Document Splitting with Character Text Splitter (Theory)
9. Lesson 203 — Indexing — Document Splitting with Character Text Splitter (Code Along)
10. Lesson 204 — Indexing — Document Splitting with Markdown Header Text Splitter
11. Lesson 205 — Indexing — Text Embedding with OpenAI
12. Lesson 206 — Indexing — Creating a Chroma Vectorstore
13. Lesson 207 — Indexing — Inspecting and Managing Documents in a Vectorstore
14. Lesson 208 — Retrieval — Similarity Search
15. Lesson 209 — Retrieval — Maximal Marginal Relevance (MMR) Search
16. Lesson 210 — Retrieval — Vectorstore-backed Retriever
17. Lesson 211 — Generation — Stuffing Documents
18. Lesson 212 — Generation — Generating a Response
1. Lesson 213 — Welcome to the Course!
2. Lesson 214 — What Does the Course Cover?
3. Lesson 215 — Course Prerequisites
4. Lesson 216 — Setting Up the Environment
5. Lesson 217 — States, Nodes, and Edges
6. Lesson 218 — First Graph — Importing Relevant Classes
7. Lesson 219 — First Graph — Defining a State and a Node
8. Lesson 220 — First Graph — Building the Graph
9. Lesson 221 — Conditional Edges — Defining Nodes and a Routing Function
10. Lesson 222 — Conditional Edges — Building the Graph
11. Lesson 223 — The Annotated Construct and Reducer Functions
12. Lesson 224 — Reducer Functions in Action
13. Lesson 225 — The MessagesState Class
14. Lesson 226 — The RemoveMessages Class
15. Lesson 227 — Trimming Messages
16. Lesson 228 — Summarizing Messages
17. Lesson 229 — Checkpointers and Threads
18. Lesson 230 — Short-term Memory with the InMemorySaver Class
19. Lesson 231 — The StateSnapshot Class
20. Lesson 232 — Long-term Memory with SQLite
1. Lesson 233 — Introduction to the Course
2. Lesson 234 — Database Comparison — SQL, NoSQL, and Vector
3. Lesson 235 — Understanding Vector Databases
4. Lesson 236 — Introduction to Vector Space
5. Lesson 237 — Distance Metrics in Vector Space
6. Lesson 238 — Vector Embeddings Walkthrough
7. Lesson 239 — Vector Databases, Comparison
8. Lesson 240 — Pinecone Registration, Walkthrough and Creating an Index
9. Lesson 241 — Connecting to Pinecone Using Python
10. Lesson 242 — Creating and Deleting a Pinecone Index Using Python
11. Lesson 243 — Upserting Data to a Pinecone Vector Database
12. Lesson 244 — Getting to Know the Fine Web Data Set and Loading It to Jupyter
13. Lesson 245 — Upserting Data from a Text File and Using an Embedding Algorithm
1. Lesson 246 — Introduction to Semantic Search
2. Lesson 247 — Introduction to the Case Study — Smart Search for Data Science Courses
3. Lesson 248 — Getting to Know the Data for the Case Study
4. Lesson 249 — Data Loading and Preprocessing
5. Lesson 250 — Pinecone Python APIs and Connecting to the Pinecone Server
6. Lesson 251 — Embedding Algorithms
7. Lesson 252 — Embedding the Data and Upserting the Files to Pinecone
8. Lesson 253 — Similarity Search and Querying the Data
9. Lesson 254 — How to Update and Change Your Vector Database
10. Lesson 255 — Data Preprocessing and Embedding for Courses with Section Data
11. Lesson 256 — Upserting the New Updated Files to Pinecone
12. Lesson 257 — Similarity Search and Querying Courses and Sections Data
13. Lesson 258 — Using the BERT Embedding Algorithm
14. Lesson 259 — Vector Database for Recommendation Engines
15. Lesson 260 — Vector Database for Semantic Image Search
16. Lesson 261 — Vector Database for Biomedical Research
1. Lesson 262 — Welcome to the World of Speech Recognition
2. Lesson 263 — Course Approach
3. Lesson 264 — How It All Started — Formants, Harmonics, and Phonemes
4. Lesson 265 — Development and Evolution
5. Lesson 266 — How Do Humans Recognize Speech?
6. Lesson 267 — Fundamentals of Sound and Sound Waves
7. Lesson 268 — Properties of Sound Waves
8. Lesson 269 — Key Concepts — Sample Rate, Bit Depth, and Bit Rate
9. Lesson 270 — Audio Signal Processing for Machine Learning and AI
10. Lesson 271 — Time-domain Audio Features
11. Lesson 272 — Frequency-domain and Time-frequency-domain Audio Features
12. Lesson 273 — Time-domain Feature Extraction — Framing and Feature Computation
13. Lesson 274 — Frequency-domain Feature Extraction — Fourier Transform
1. Lesson 275 — Acoustic and Language Modeling
2. Lesson 276 — Hidden Markov Models (HMMs) and Traditional Neural Networks
3. Lesson 277 — Deep Learning Models — CNNs, RNNs, and LSTMs
4. Lesson 278 — Advanced Speech Recognition Systems — Transformers
5. Lesson 279 — Building a Speech Recognition Model Part I
6. Lesson 280 — Building a Speech Recognition Model Part II
7. Lesson 281 — Selecting the Appropriate Speech Recognition Tool
8. Lesson 282 — Installing Anaconda
9. Lesson 283 — Setting Up a New Environment
10. Lesson 284 — Installing Packages for Speech Recognition
11. Lesson 285 — Importing the Relevant Packages in Jupyter
12. Lesson 286 — Audio File Formats for Speech Recognition
13. Lesson 287 — Importing Audio Files in Jupyter Notebook
14. Lesson 288 — The SpeechRecognition Library — Google Web Speech API
15. Lesson 289 — Evaluation Metrics — WER and CER
16. Lesson 290 — Calculating WER and CER in Python
17. Lesson 291 — Understanding Noise in Audio Files
18. Lesson 292 — Creating a Spectrogram with Python
19. Lesson 293 — Dealing with Background Noise
1. Lesson 294 — Whisper AI — Transformer-based Speech-to-Text
2. Lesson 295 — Transcribing Multiple Audio Files from a Directory
3. Lesson 296 — Saving Audio Transcriptions to CSV for Easy Analysis
4. Lesson 297 — Reversing the Process — AI-powered Text-to-Speech
5. Lesson 298 — Modern Practices and Applications
6. Lesson 299 — Challenges and Limitations
7. Lesson 300 — The Future of Speech Recognition with AI
1. Lesson 301 — Introduction to the Course
2. Lesson 302 — What Does the Course Cover?
3. Lesson 303 — The Interview Tool's Specifics
4. Lesson 304 — Hosting an LLM vs Using an API
5. Lesson 305 — Open-Source vs Closed-Source Models
6. Lesson 306 — Tokens
7. Lesson 307 — Pricing — Hosting an LLM vs Pay-by-Token
8. Lesson 308 — Initial Prompt Development — Part 1
9. Lesson 309 — Initial Prompt Development — Part 2
10. Lesson 310 — Database Design and Schema Development
11. Lesson 311 — What Is an Activity Diagram
12. Lesson 312 — Creating an Activity Diagram
13. Lesson 313 — Conducting the Planning Stage
14. Lesson 314 — The OpenAI Playground
15. Lesson 315 — Optimizing Temperature and Top P for Different Use Cases
16. Lesson 316 — Prompt Engineering for Software Development
17. Lesson 317 — How to Test Out a Prompt Template
1. Lesson 318 — Setting Up Environment
2. Lesson 319 — Streamlit's Pros and Cons
3. Lesson 320 — Streamlit Elements — Titles, Headers, and Formatting
4. Lesson 321 — Streamlit Elements — Text Methods
5. Lesson 322 — Streamlit Elements — Chat Elements
6. Lesson 323 — Session State
7. Lesson 324 — Initializing an OpenAI Client
8. Lesson 325 — Implementing the Chat Functionality
9. Lesson 326 — Building the Setup Page
10. Lesson 327 — Enhancing Chatbot Interaction with Session State
11. Lesson 328 — Refining Our Project
12. Lesson 329 — Implementing Feedback Functionality — Part 1
13. Lesson 330 — Implementing Feedback Functionality — Part 2
14. Lesson 331 — Uploading Your Project in GitHub
15. Lesson 332 — Deploying Your Streamlit App
1. Lesson 333 — Introduction
2. Lesson 334 — Application Structure
3. Lesson 335 — Prompt Structure of HR Interviews
4. Lesson 336 — Prompt Structure of Technical Interviews
5. Lesson 337 — Additional Protection From Errors
6. Lesson 338 — Hallucinations
7. Lesson 339 — Prompt Injection
8. Lesson 340 — Counting Tokens
9. Lesson 341 — Cost Reduction
10. Lesson 342 — Scaling
11. Lesson 343 — Conclusion
1. Lesson 344 — What Does the Course Cover?
2. Lesson 345 — The AI Lifecycle — From Data Collection to Model Application
3. Lesson 346 — Why AI Ethics Matter More Than Ever
4. Lesson 347 — Ethics vs Laws
5. Lesson 348 — Privacy
6. Lesson 349 — Transparency
7. Lesson 350 — Accountability
8. Lesson 351 — Fairness
9. Lesson 352 — Ethical Sourcing and Types of Data
10. Lesson 353 — Proprietary Data
11. Lesson 354 — Public Data
12. Lesson 355 — Web-scraped Data
13. Lesson 356 — Dealing with Sensitive and Protected Information
14. Lesson 357 — Data Bias and Fair Representation
15. Lesson 358 — Ethical Challenges in Working with Labeled Data
16. Lesson 359 — Ethical Challenges in Unsupervised Training
17. Lesson 360 — Ethical Considerations for Supervised Fine-tuning
18. Lesson 361 — RLHF and Ethical AI Behavior
19. Lesson 362 — Inclusive and Fair AI Development Practices
20. Lesson 363 — Intellectual Property and User Consent in AI Interactions
21. Lesson 364 — Ethical Responsibilities of Foundation Model Developers
22. Lesson 365 — Common Issues in Foundation Models — Open-source Data
23. Lesson 366 — Inconsistency
24. Lesson 367 — Hallucination
25. Lesson 368 — Ongoing Monitoring and Risk Mitigation for Deployed AI
1. Lesson 369 — Access to AI Technology for Businesses of All Sizes
2. Lesson 370 — Transparency in AI Decision-making Processes
3. Lesson 371 — Ethical Use of AI Outputs in Business
4. Lesson 372 — Responsible AI Adoption and Risk Management for Businesses
5. Lesson 373 — Equity in Access to AI Technology
6. Lesson 374 — Ethical Considerations in Human-AI Collaboration
7. Lesson 375 — Responsible Use of AI-generated Outputs
8. Lesson 376 — Understanding ChatGPT
9. Lesson 377 — Privacy Concerns with ChatGPT
10. Lesson 378 — OpenAI's Privacy Policies and Data Handling
11. Lesson 379 — Misinformation and AI-generated Content
12. Lesson 380 — ChatGPT Plagiarism
13. Lesson 381 — ChatGPT and the Environment
14. Lesson 382 — Global AI and Data Regulations
15. Lesson 383 — European Union — GDPR and the EU Artificial Intelligence Act
16. Lesson 384 — United States — AI Regulation Across States
17. Lesson 385 — Asia-Pacific Region — Strong Government Control
18. Lesson 386 — Africa's Push for AI Governance

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£1,999.00
386 lessons
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The AI Engineer Blueprint

Course curriculum

CHAPTER 1 — AI FOUNDATIONS, DATA AND CORE TECHNIQUES

CHAPTER 2 — GENERATIVE AI, NLP, LLMS AND PRACTICAL CHALLENGES

CHAPTER 3 — THE AI TECHNOLOGY STACK, CAREERS AND THE FUTURE

CHAPTER 4 — PYTHON AND JUPYTER FUNDAMENTALS

CHAPTER 5 — PYTHON CONDITIONALS AND FUNCTIONS

CHAPTER 6 — PYTHON DATA STRUCTURES, LOOPS AND OBJECT-ORIENTED PROGRAMMING

CHAPTER 7 — NLP FOUNDATIONS, SENTIMENT AND TOPIC MODELLING

CHAPTER 8 — NLP TEXT CLASSIFICATION PROJECT

CHAPTER 9 — DEEP LEARNING, LLMS AND TRANSFORMER ARCHITECTURE

CHAPTER 10 — GPT, OPENAI, LANGCHAIN AND HUGGING FACE

CHAPTER 11 — BERT, XLNET AND MODEL EVALUATION

CHAPTER 12 — LANGCHAIN FOUNDATIONS, OPENAI SETUP AND CHAT MODELS

CHAPTER 13 — PROMPT TEMPLATES, OUTPUT PARSERS, CHAINS AND RUNNABLES

CHAPTER 14 — RAG, DOCUMENT PROCESSING AND VECTOR RETRIEVAL

CHAPTER 15 — LANGGRAPH: STATES, ROUTING, MESSAGES AND MEMORY

CHAPTER 16 — VECTOR DATABASES, EMBEDDINGS AND PINECONE

CHAPTER 17 — SEMANTIC SEARCH AND VECTOR DATABASE APPLICATIONS

CHAPTER 18 — SPEECH RECOGNITION AND AUDIO SIGNAL PROCESSING

CHAPTER 19 — SPEECH MODELS, PYTHON SETUP, TRANSCRIPTION AND EVALUATION

CHAPTER 20 — WHISPER AND MODERN SPEECH AI

CHAPTER 21 — LLM ENGINEERING PROJECT PLANNING AND PROMPT DEVELOPMENT

CHAPTER 22 — BUILDING AND DEPLOYING THE STREAMLIT AI APPLICATION

CHAPTER 23 — LLM APPLICATION ARCHITECTURE, SECURITY, COST AND SCALING

CHAPTER 24 — AI ETHICS, RESPONSIBLE DATA AND MODEL DEVELOPMENT

CHAPTER 25 — RESPONSIBLE AI ADOPTION, CHATGPT AND GLOBAL GOVERNANCE

About this course

Discover your potential, starting today