Mímir, Keeper of the Well of Wisdom
The AI Engineer Course 2026: Complete AI Engineer Bootcamp
https://www.udemy.com/course/the-ai-engineer-course-complete-ai-
engineer-bootcamp/
Year : 2026
Language : English
Level : All Levels
Category : Development
Subcategory : Data Science
Duration : 29h 14m
Lectures : 437
Rating : 4.6/5 (14,819 reviews)
Students : 101,467
INSTRUCTOR(S)
HEADLINE
Complete AI Engineer Training: Python, NLP, Transformers,
LLMs, LangChain, Hugging Face, APIs
WHAT YOU'LL LEARN
* The course provides the entire toolbox you need to become an
AI Engineer
* Understand key Artificial Intelligence concepts and build a
solid foundation
* Start coding in Python and learn how to use it for NLP and
AI
* Impress interviewers by showing an understanding of the AI
field
* Apply your skills to real-life business cases
* Harness the power of Large Language Models
* Leverage LangChain for seamless development of AI-driven
applications by chaining interoperable components
* Become familiar with Hugging Face and the AI tools it offers
* Use APIs and connect to powerful foundation models
* Utilize Transformers for advanced speech-to-text
REQUIREMENTS
* No prior experience is required. We will start from the very
basics
* You?ll need to install Anaconda. We will show you how to do
that step by step
WHO IS THIS COURSE FOR
* You should take this course if you want to become an AI
Engineer or if you want to learn about the field
* This course is for you if you want a great career
* The course is also ideal for beginners, as it starts from
the fundamentals and gradually builds up your skills
DESCRIPTION
The Problem AI Engineers are best suited to thrive in the age
of
AI. It helps businesses utilize Generative AI by building AI-
driven applications on top of their existing websites, apps,
and
databases. Therefore, it?s no surprise that the demand for AI
Engineers has been surging in the job marketplace. Supply,
however, has been minimal, and acquiring the skills necessary
to
be hired as an AI Engineer can be challenging. So, how is this
achievable? Universities have been slow to create specialized
programs focused on practical AI Engineering skills. The few
attempts that exist tend to be costly and time-consuming. Most
online courses offer ChatGPT hacks and isolated technical
skills, yet integrating these skills remains challenging. The
Solution AI Engineering is a multidisciplinary field covering:
AI principles and practical applications Python programming
Natural Language Processing in Python Large Language Models
and
Transformers Developing apps with orchestration tools like
LangChain Vector databases using PineCone Creating AI-driven
applications Each topic builds on the previous one, and
skipping
steps can lead to confusion. For instance, applying large
language models requires familiarity with Langchain?just as
studying natural language processing can be overwhelming
without
basic Python coding skills. So, we created the AI Engineer
Bootcamp 2025 to provide the most effective, time-efficient,
and
structured AI engineering training available online. This
pioneering training program overcomes the most significant
barrier to entering the AI Engineering field by consolidating
all essential resources in one place. Our course is designed
to
teach interconnected topics seamlessly?providing all you need
to
become an AI Engineer at a significantly lower cost and time
investment than traditional programs. The Skills 1. Intro to
Artificial Intelligence Structured and unstructured data,
supervised and unsupervised machine learning, Generative AI,
and
foundational models?these are familiar AI buzzwords; what
exactly do they mean? Why study AI? Gain deep insights into
the
field through a guided exploration that covers AI
fundamentals,
the significance of quality data, essential techniques,
Generative AI, and the development of advanced models like
GPT,
Llama, Gemini, and Claude. 2. Python Programming Mastering
Python programming is essential to becoming a skilled AI
developer?no-code tools are insufficient. Python is a modern,
general-purpose programming language suited for creating web
applications, computer games, and data science tasks. Its
extensive library ecosystem makes it ideal for developing AI
models. Why study Python programming? Python programming will
become your essential tool for communicating with AI models
and
integrating their capabilities into your products. 3. Intro to
NLP in Python Explore Natural Language Processing (NLP) and
learn techniques that empower computers to comprehend,
generate,
and categorize human language. Why study NLP? NLP forms the
basis of cutting-edge Generative AI models. This program
equips
you with essential skills to develop AI systems that
meaningfully interact with human language. 4. Introduction to
Large Language Models This program section enhances your
natural
language processing skills by teaching you to utilize the
powerful capabilities of Large Language Models (LLMs). Learn
critical tools like Transformers Architecture, GPT, Langchain,
HuggingFace, BERT, and XLNet. Why study LLMs? This module is
your gateway to understanding how large language models work
and
how they can be applied to solve complex language-related
tasks
that require deep contextual understanding. 5. Building
Applications with LangChain LangChain is a framework that
allows
for seamless development of AI-driven applications by chaining
interoperable components. Why study LangChain? Learn how to
create applications that can reason. LangChain facilitates the
creation of systems where individual pieces?such as language
models, databases, and reasoning algorithms?can be
interconnected to enhance overall functionality. 6. Vector
Databases With emerging AI technologies, the importance of
vectorization and vector databases is set to increase
significantly. In this Vector Databases with Pinecone module,
you?ll have the opportunity to explore the Pinecone database?a
leading vector database solution. Why study vector databases?
Learning about vector databases is crucial because it equips
you
to efficiently manage and query large volumes of high-
dimensional data?typical in machine learning and AI
applications. These technical skills allow you to deploy
performance-optimized AI-driven applications. 7. Speech
Recognition with Python Dive into the fascinating field of
Speech Recognition and discover how AI systems transform
spoken
language into actionable insights. This module covers
foundational concepts such as audio processing, acoustic
modeling, and advanced techniques for building speech-to-text
applications using Python. Why study speech recognition?
Speech
Recognition is at the core of voice assistants, automated
transcription tools, and voice-driven interfaces. Mastering
this
skill enables you to create applications that interact with
users naturally and unlock the full potential of audio data in
AI solutions. What You Get $1,250 AI Engineering training
program Active Q&A support Essential skills for AI engineering
employment AI learner community access Completion certificate
Future updates Real-world business case solutions for job
readiness We're excited to help you become an AI Engineer from
scratch?offering an unconditional 30-day full money-back
guarantee. With excellent course content and no risk involved,
we're confident you'll love it. Why delay? Each day is a lost
opportunity. Click the ?Buy Now? button and join our AI
Engineer
program today.
COURSE CONTENT
Chapter 1: Intro to AI Module: Getting started
1. Building an AI tool in 5 minutes: A quick demo
2. What does the course cover
3. Natural vs Artificial Intelligence
4. Brief history of AI
5. Demystifying AI, Data science, Machine learning, and Deep
learning
6. Weak vs Strong AI
Chapter 2: Intro to AI Module: Data is essential for building
AI
7. Structured vs unstructured data
8. How we collect data
9. Labelled and unlabelled data
10. Metadata: Data that describes data
Chapter 3: Intro to AI Module: Key AI techniques
11. Machine learning
12. Supervised, Unsupervised, and Reinforcement learning
13. Deep learning
Chapter 4: Intro to AI Module: Important AI branches
14. Robotics
15. Computer vision
16. Traditional ML
17. Generative AI
Chapter 5: Intro to AI Module: Understanding Generative AI
18. The rise of Gen AI: Introducing ChatGPT
19. Early approaches to Natural Language Processing (NLP)
20. Recent NLP advancements
21. From Language Models to Large Language Models (LLMs)
22. The efficiency of LLM training. Supervised vs Semi-
supervised learning
23. From N-Grams to RNNs to Transformers: The Evolution of
NLP
24. Phases in building LLMs
25. Prompt engineering vs Fine-tuning vs RAG: Techniques for
AI optimization
26. The importance of foundation models
27. Buy vs Make: foundation models vs private models
Chapter 6: Intro to AI Module: Practical challenges in
Generative AI
28. Inconsistency and hallucination
29. Budgeting and API costs
30. Latency
31. Running out of data
Chapter 7: Intro to AI Module: The AI tech stack
32. Python programming
33. Working with APIs
34. Vector databases
35. The importance of open source
36. Hugging Face
37. LangChain
38. AI evaluation tools
Chapter 8: Intro to AI Module: AI job positions
39. AI strategist
40. AI developer
41. AI engineer
Chapter 9: Intro to AI Module: Looking ahead
42. AI ethics
43. Future of AI
Chapter 10: Python Module: Why Python?
44. Programming Explained in a Few Minutes
45. Why Python
Chapter 11: Python Module: Setting Up the Environment
46. Jupyter - Introduction
47. Jupyter - Installing Anaconda
48. Jupyter - Introduction to Using Jupyter
49. Jupyter - Working with Notebook Files
50. Jupyter - Using Shortcuts
51. Jupyter - Handling Error Messages
52. Jupyter - Restarting the Kernel
Chapter 12: Python Module: Python Variables and Data Types
53. Python Variables
54. Python Coding Exercises
55. Types of Data - Numbers and Boolean Values
56. Types of Data - Strings
57. Anaconda AI - Introduction
58. Using the Anaconda Assistant: Strings
Chapter 13: Python Module: Basic Python Syntax
59. Basic Python Syntax - Arithmetic Operators
60. Basic Python Syntax - The Double Equality Sign
61. Basic Python Syntax - Reassign Values
62. Basic Python Syntax - Add Comments
63. Basic Python Syntax - Line Continuation
64. Basic Python Syntax - Indexing Elements
65. Basic Python Syntax - Indentation
Chapter 14: Python Module: More on Operators
66. Operators - Comparison Operators
67. Operators - Logical and Identity Operators
Chapter 15: Python Module: Conditional Statements
68. Conditional Statements - The IF Statement
69. Conditional Statements - The ELSE Statement
70. Conditional Statements - The ELIF Statement
71. Conditional Statements - A Note on Boolean Values
Chapter 16: Python Module: Functions
72. Functions - Defining a Function in Python
73. Functions - Creating a Function with a Parameter
74. Functions - Another Way to Define a Function
75. Functions - Using a Function in Another Function
76. Functions - Combining Conditional Statements and
Functions
77. Functions - Creating Functions Containing a Few
Arguments
78. Functions - Notable Built-in Functions in Python
Chapter 17: Python Module: Sequences
79. Sequences - Lists
80. Sequences - Using Methods
81. Sequences - List Slicing
82. Sequences - Tuples
83. Sequences - Dictionaries
Chapter 18: Python Module: Iteration
84. Iteration - For Loops
85. Iteration - While Loops and Incrementing
86. Iteration - Creatie Lists with the range() Function
87. Iteraion - Use Conditional Statements and Loops Together
88. Iteration - Conditional Statements, Functions, and Loops
89. Using the Anaconda Assistant: Several Python Tools
90. Iteration - Iterating over Dictionaries
91. Using the Anaconda Assistant: Dictionaries
Chapter 19: Python Module: A Few Important Python Concepts and
Terms
92. Introduction to Object Oriented Programming (OOP)
93. Modules, Packages, and the Python Standard Library
94. Importing Modules
95. Python List Comprehensions
96. Python Anonymous Functions (Lambda Functions)
97. What is Software Documentation
98. The Python Documentation
Chapter 20: NLP Module: Introduction
99. Introduction to the course
100. Course materials and notebooks
101. Introduction to NLP
102. NLP in everyday life
103. Supervised vs unsupervised NLP
Chapter 21: NLP Module: Text Preprocessing
104. The importance of data preparation
105. Setting up the environment
106. Setting up the environment and exploring the packages
(text article)
107. Lowercasing text
108. Removing stop words
109. Regular expressions (regex)
110. Tokenization
111. Stemming
112. Lemmatization
113. N-grams
114. The pandas library explanation
115. A note on the practical task
116. Practical task: Text preprocessing
117. Additional notes: Text preprocessing with pandas and
NLTK
Chapter 22: NLP Module: Identifying Parts of Speech and Named
Entities
118. Text tagging
119. Parts of Speech (POS) tagging
120. Named Entity Recognition (NER)
121. A note on the practical task
122. Practical task: POS and NER
Chapter 23: NLP Module: Sentiment Analysis
123. What is sentiment analysis?
124. Rule-based sentiment analysis
125. Pre-trained transformer models
126. A note on the practical task
127. Practical Task: Sentiment analysis
Chapter 24: NLP Module: Vectorizing Text
128. Numerical representation of text
129. Bag of Words model
130. TF-IDF
Chapter 25: NLP Module: Topic Modelling
131. What is topic modelling?
132. When to use topic modelling?
133. Latent Dirichlet Allocation (LDA)
134. A note on the following lesson
135. LDA in Python
136. Latent Semantic Analysis (LSA)
137. LSA in Python
138. Determining the number of topics
Chapter 26: NLP Module: Building Your Own Text Classifier
139. Building a custom text classifier
140. Logistic regression
141. Naive Bayes
142. Linear support vector machine
Chapter 27: NLP Module: Categorizing Fake News (Case Study)
143. A note on the case study
144. Introducing the project
145. Exploring our data through POS tags
146. Extracting named entities
147. Processing the text
148. Does sentiment differ between news types?
149. What topics appear in fake news? (Part 1)
150. What topics appear in fake news? (Part 2)
151. Categorizing fake news with a custom classifier
Chapter 28: NLP Module: The Future of NLP
152. What is deep learning?
153. Deep learning for NLP
154. Non-English NLP
155. What's next for NLP?
Chapter 29: LLMs Module: Introduction to Large Language Models
156. Introduction to the course
157. Course materials and notebooks
158. What are LLMs?
159. How large is an LLM?
160. General purpose models
161. Pre-training and fine tuning
162. What can LLMs be used for?
Chapter 30: LLMs Module: The Transformer Architecture
163. Deep learning recap
164. The problem with RNNs
165. The solution: attention is all you need
166. The transformer architecture
167. Input embeddings
168. Multi-headed attention
169. Feed-forward layer
170. Masked multihead attention
171. Predicting the final outputs
Chapter 31: LLMs Module: Getting Started With GPT Models
172. What does GPT mean?
173. The development of ChatGPT
174. Setting up the environment
175. OpenAI API
176. Generating text
177. Customizing GPT output
178. Key word text summarization
179. Coding a simple chatbot
180. Introduction to LangChain in Python
181. LangChain
182. Adding custom data to our chatbot
Chapter 32: LLMs Module: Hugging Face Transformers
183. Hugging Face package
184. The transformer pipeline
185. Pre-trained tokenizers
186. Special tokens
187. Hugging Face and PyTorch/TensorFlow
188. Saving and loading models
Chapter 33: LLMs Module: Question and Answer Models With BERT
189. GPT vs BERT
190. BERT architecture
191. Loading the model and tokenizer
192. BERT embeddings
193. Calculating the response
194. Creating a QA bot
195. BERT, RoBERTa, DistilBERT
Chapter 34: LLMs Module: Text Classification With XLNet
196. GPT vs BERT vs XLNET
197. A note on the following lecture
198. Preprocessing our data
199. XLNet Embeddings
200. Fine tuning XLNet
201. Evaluating our model
Chapter 35: LangChain Module: Introduction
202. Introduction to the course
203. Course materials and notebooks
204. Business applications of LangChain
205. What makes LangChain powerful?
206. What does the course cover?
Chapter 36: LangChain Module: Tokens, Models, and Prices
207. Tokens
208. Models and Prices
Chapter 37: LangChain Module: Setting Up the Environment
209. Setting up a custom anaconda environment for Jupyter
integration
210. Obtaining an OpenAI API key
211. Setting the API key as an environment variable
Chapter 38: LangChain Module: The OpenAI API
212. First Steps
213. System, user, and assistant roles
214. Creating a sarcastic chatbot
215. Temperature, max tokens, and streaming
Chapter 39: LangChain Module: Model Inputs
216. The LangChain framework
217. ChatOpenAI
218. System and human messages
219. AI messages
220. Prompt templates and prompt values
221. Chat prompt templates and chat prompt values
222. Few-shot chat message prompt templates
Chapter 40: LangChain Module: Output Parsers
223. String output parser
224. Comma-separated list output parser
225. Datetime output parser
Chapter 41: LangChain Module: LangChain Expression Language
(LCEL)
226. Piping a prompt, model, and an output parser
227. Batching
228. Streaming
229. The Runnable and RunnableSequence classes
230. Piping chains and the RunnablePassthrough class
231. Graphing Runnables
232. RunnableParallel
233. Piping a RunnableParallel with other Runnables
234. RunnableLambda
235. The @chain decorator
Chapter 42: LangChain Module: Retrieval Augmented Generation
(RAG)
236. How to integrate custom data into an LLM
237. Introduction to RAG
238. Introduction to document loading and splitting
239. Introduction to document embedding
240. Introduction to document storing, retrieval, and
generation
241. Indexing: Document loading with PyPDFLoader
242. Indexing: Document loading with Docx2txtLoader
243. Indexing: Document splitting with character text
splitter (Theory)
244. Indexing: Document splitting with character text
splitter (Code along)
245. Indexing: Document splitting with Markdown header text
splitter
246. Indexing: Text embedding with OpenAI
247. Indexing: Creating a Chroma vectorstore
248. Indexing: Inspecting and managing documents in a
vectorstore
249. Retrieval: Similarity search
250. Retrieval: Maximal Marginal Relevance (MMR) search
251. Retrieval: Vectorstore-backed retriever
252. Generation: Stuffing documents
253. Generation: Generating a response
Chapter 43: LangGraph Module: Introduction
254. Welcome to the course!
255. What does the course cover?
256. Course prerequisites
Chapter 44: LangGraph Module: Setting Up the Environment
257. Setting up the environment
Chapter 45: LangGraph Module: Graph Components and
Implementation
258. States, nodes, and edges
259. First graph: Importing relevant classes
260. First graph: Defining a state and a node
261. First graph: Building the graph
262. Conditional edges: Defining nodes and a routing
function
263. Conditional edges: Building the graph
Chapter 46: LangGraph Module: Message Management
264. The Annotated construct and reducer functions
265. Reducer functions in action
266. The MessagesState class
267. The RemoveMessages class
268. Trimming messages
269. Summarizing messages
Chapter 47: LangGraph Module: Thread-Level Persistence
270. Checkpointers and threads
271. Short-term memory with the InMemorySaver class
272. The StateSnapshot class
273. Long-term memory with SQLite
Chapter 48: Vector Databases Module: Introduction
274. Introduction to the course
275. Course materials and notebooks
276. Database comparison: SQL, NoSQL, and Vector
277. Understanding vector databases
Chapter 49: Vector Databases Module: Basics of Vector Space
and High-Dimensional Data
278. Introduction to vector space
279. Distance metrics in vector space
280. Vector embeddings walkthrough
Chapter 50: Vector Databases Module: Introduction to The
Pinecone Vector Database
281. Vector databases, comparison
282. Pinecone registration, walkthrough and creating an
Index
283. Connecting to Pinecone using Python
284. Assignment
285. Creating and deleting a Pinecone index using Python
286. Upserting data to a pinecone vector database
287. Getting to know the fine web data set and loading it to
Jupyter
288. Upserting data from a text file and using an embedding
algorithm
Chapter 51: Vector Databases Module: Semantic Search with
Pinecone and Custom (Case Study)
289. Introduction to semantic search
290. Introduction to the case study ? smart search for data
science courses
291. Getting to know the data for the case study
292. Data loading and preprocessing
293. Pinecone Python APIs and connecting to the Pinecone
server
294. Embedding Algorithms
295. Embedding the data and upserting the files to Pinecone
296. Similarity search and querying the data
297. How to update and change your vector database
298. Data preprocessing and embedding for courses with
section data
299. Assignment 2
300. Upserting the new updated files to Pinecone
301. Similarity search and querying courses and sections
data
302. Assignment 3
303. Using the BERT embedding algorithm
304. Vector database for recommendation engines
305. Vector database for semantic image search
306. Vector database for biomedical research
Chapter 52: Speech Recognition Module: Introduction
307. Welcome to the world of Speech Recognition
308. Module Resources
309. Course Approach
310. How it all started: Formants, harmonics, and phonemes
311. Development and Evolution
Chapter 53: Speech Recognition Module: Sound and Speech Basics
312. How do humans recognize speech?
313. Fundamentals of sound and sound waves
314. Properties of sound waves
Chapter 54: Speech Recognition Module: Analog to Digital
Conversion
315. Key concepts: Sample Rate, bit depth, and bit rate
316. Audio signal processing for Machine Learning and AI
Chapter 55: Speech Recognition Module: Audio Feature
Extraction for AI Applications
317. Time-domain audio features
318. Frequency-domain and time-frequency-domain audio
features
319. Time-domain feature extraction: Framing and feature
computation
320. Frequency-domain feature extraction: Fourier transform
Chapter 56: Speech Recognition Module: Technology Mechanics
321. Acoustic and language modeling
322. Hidden Markov Models (HMMs) and traditional neural
networks
323. Deep learning models: CNNs, RNNs, and LSTMs
324. Advanced speech recognition systems: Transformers
325. Building a speech recognition model part I
326. Building a speech recognition model part II
327. Selecting the appropriate speech recognition tool
328. Expanding beyond the tools we've covered
Chapter 57: Speech Recognition Module: Setting Up the
Environment
329. Installing Anaconda
330. Setting up a new environment
331. Installing packages for speech recognition
332. Importing the relevant packages in Jupyter
Chapter 58: Speech Recognition Module: Transcribing Audio with
Google Web Speech API
333. Audio file formats for speech recognition
334. Importing audio files in Jupyter Notebook
335. The SpeechRecognition library: Google Web Speech API
336. Evaluation metrics: WER and CER
337. Calculating WER and CER in Python
Chapter 59: Speech Recognition Module: Background Noise and
Spectrograms
338. Understanding noise in audio files
339. Creating a spectrogram with Python
340. Dealing with background noise
Chapter 60: Speech Recognition Module: Transcribing Audio with
OpenAI's Whisper
341. 9.1 Whisper AI: Transformer-based speech-to-text
342. A note on variability
343. Transcribing multiple audio files from a directory
344. Saving audio transcriptions to CSV for easy analysis
345. Reversing the process: AI-powered text-to-speech
Chapter 61: Speech Recognition Module: Final Discussion and
Future Directions
346. Modern practices and applications
347. Challenges and limitations
348. The future of speech recognition with AI
Chapter 62: LLM Engineering Module: Introduction
349. Introduction to the Course
350. What does the course cover?
351. The Interview Tool?s Specifics
Chapter 63: LLM Engineering Module: Planning stage
352. Hosting an LLM vs Using an API
353. Open-Source vs Closed-Source Models
354. Tokens
355. Pricing: Hosting an LLM vs Pay-by-Token
356. Initial Prompt Development: Part 1
357. Initial Prompt Development: Part 2
358. Database Design and Schema Development
359. What Is an Activity Diagram
360. Creating an Activity Diagram
361. Concluding the Planning Stage
Chapter 64: LLM Engineering Module: Crafting and Testing AI
Prompts
362. Adding Funds to Your OpenAI API Account
363. The OpenAI Playground
364. Optimizing Temperature and Top P for Different Use
Cases
365. Prompt Engineering for Software Development
366. How to Test Out a Prompt Template
Chapter 65: LLM Engineering Module: Getting to Know Streamlit
367. Setting up environment
368. Streamlit's Pros and Cons
369. Streamlit Elements: Titles, Headers, and Formatting
370. Streamlit Elements: Text Methods
371. Streamlit Elements: Chat Elements
372. Sessin State
Chapter 66: LLM Engineering Module: Developing the prototype
373. Initializing an OpenAI Client
374. Implementing the Chat Functionality
375. Building the Setup Page
376. Enhancing Chatbot Interaction with Session State
377. Refining Our Project
378. Implementing Feedback Functionality: Part 1
379. Implementing Feedback Functionality: Part 2
380. Uploading Your Project in GitHub
381. Deploying Your Streamlit App
Chapter 67: LLM Engineering Module: Solving Real-World AI
Challenges
382. Introduction
383. Application Structure
384. Prompt Structure of HR Interviews
385. Prompt Structure of Technical Interviews
386. Additional Protection From Errors
387. Hallucinations
388. Prompt Injection
389. Counting Tokens
390. Cost Reduction
391. Scaling
392. Conclusion
Chapter 68: AI Ethics Module: Introduction to AI and Data
Ethics
393. What does the course cover
394. The AI Lifecycle: From data collection to model
application
395. Why AI Ethics matter more than ever
396. Ethics vs laws
Chapter 69: AI Ethics Module: The Core Principles of AI Ethics
397. Privacy
398. Transparency
399. Accountability
400. Fairness
Chapter 70: AI Ethics Module: Ethical Data Collection
401. Ethical sourcing and types of data
402. Proprietary data
403. Public data
404. Web-scraped data
405. Dealing with sensitive and protected information
406. Data bias and fair representation
Chapter 71: AI Ethics Module: Ethical AI Development
407. Ethical challenges in working with labeled data
408. Ethical considerations for unlabeled data
409. Ethical challenges in unsupervised training
410. Ethical considerations for supervised Fine-tuning
411. RLHF and ethical AI behavior
412. Inclusive and fair AI development practices
Chapter 72: AI Ethics Module: Ethical AI Deployment
413. Intellectual property and user consent in AI
interactions
414. Ethical responsibilities of foundation model developers
415. Common issues in foundation models: Open-source data
416. Inconsistency
417. Hallucination
418. Ongoing monitoring and risk mitigation for deployed AI
Chapter 73: AI Ethics Module: Ethical AI for End-Users:
Businesses
419. Access to AI technology for businesses of all sizes
420. Transparency in AI decision-making processes
421. Ethical use of AI outputs in business
422. Responsible AI adoption and risk management for
businesses
Chapter 74: AI Ethics Module: Ethical AI for End-Users:
Individuals
423. Equity in access to AI technology
424. Ethical considerations in human-AI collaboration
425. Responsible use of AI-generated outputs
Chapter 75: AI Ethics Module: ChatGPT Ethics
426. Understanding ChatGPT
427. Privacy concerns with ChatGPT
428. OpenAI?s privacy policies and data handling
429. Misinformation and AI-generated content
430. ChatGPT plagiarism
431. ChatGPT and the environment
Chapter 76: AI Ethics Module: Data and AI Regulatory
Frameworks
432. Global AI and data regulations
433. European Union: GDPR and the EU Artificial Intelligence
Act
434. United States: AI regulation across states
435. Asia-Pacific region: Strong government control
436. Africa's push for AI governance
Chapter 77: Bonus
437. Bonus lecture
DATES
Published : 2024-08-27
Last Updated : 2026-01-03
If you fear the truth, don?t come to my well.