The AI Engineer Course 2026 Complete AI Engineer Bootcamp

415 lectures29h 19m total179 articlesSubtitles & transcript search77 sections

Full lifetime access · picks up where you left off

Curriculum

77 sections · 594 lectures

01Intro to AI Module Getting started7 · 26m

Building an AI tool in 5 minutes A quick demoPreview10:17
What does the course cover3:18
Natural vs Artificial Intelligence2:07
Brief history of AI4:43
Demystifying AI, Data science, Machine learning, and Deep learning2:28
Weak vs Strong AI2:44
Quiz 1

02Intro to AI Module Data is essential for building AI5 · 10m

Structured vs unstructured data1:47
How we collect data4:02
Labelled and unlabelled data2:07
Metadata Data that describes data1:43
Quiz 2

03Intro to AI Module Key AI techniques4 · 20m

Machine learning6:16
Supervised, Unsupervised, and Reinforcement learning5:35
Deep learning8:27
Quiz 3

04Intro to AI Module Important AI branches5 · 15m

Robotics4:35
Computer vision4:35
Traditional ML1:19
Generative AI4:05
Quiz 4

05Intro to AI Module Understanding Generative AI10 · 38m

The rise of Gen AI Introducing ChatGPT2:09
Early approaches to Natural Language Processing (NLP)2:43
Recent NLP advancements3:02
From Language Models to Large Language Models (LLMs)6:12
The efficiency of LLM training. Supervised vs Semi-supervised learning3:35
From N-Grams to RNNs to Transformers The Evolution of NLP5:23
Phases in building LLMs4:41
Prompt engineering vs Fine-tuning vs RAG Techniques for AI optimization4:25
The importance of foundation models2:50
Buy vs Make foundation models vs private models2:36

06Intro to AI Module Practical challenges in Generative AI4 · 10m

Inconsistency and hallucination2:44
Budgeting and API costs2:59
Latency1:27
Running out of data2:25

07Intro to AI Module The AI tech stack7 · 21m

Python programming2:07
Working with APIs1:35
Vector databases3:11
The importance of open source6:11
Hugging Face1:47
LangChain2:55
AI evaluation tools3:08

08Intro to AI Module AI job positions3 · 14m

AI strategist5:09
AI developer4:28
AI engineer3:54

09Intro to AI Module Looking ahead2 · 10m

AI ethics5:40
Future of AI4:40

10Python Module Why Python2 · 10m

Programming Explained in a Few Minutes5:29
Why Python4:33

11Python Module Setting Up the Environment8 · 32m

Jupyter - Introduction3:29
Jupyter - Installing Anaconda3:34
Jupyter - Introduction to Using Jupyter4:53
Jupyter - Working with Notebook Files4:30
Jupyter - Using Shortcuts7:25
Jupyter - Handling Error Messages5:53
Jupyter - Restarting the Kernel2:04
Setting Up the Environment - Jupyter

12Python Module Python Variables and Data Types23 · 27m

Python Variables3:38
Python Coding Exercises7:54
Python Variables - Exercise #1
Python Variables - Exercise #2
Python Variables - Exercise #3
Python Variables - Exercise #4
Python Variables
Types of Data - Numbers and Boolean Values3:06
Numbers and Boolean Values - Exercise #1
Numbers and Boolean Values - Exercise #2
Numbers and Boolean Values - Exercise #3
Numbers and Boolean Values - Exercise #4
Numbers and Boolean Values - Exercise #5
Types of Data - Numbers and Boolean Values
Types of Data - Strings5:41
Strings - Exercise #1
Strings - Exercise #2
Strings - Exercise #3
Strings - Exercise #4
Strings - Exercise #5
Types of Data - Strings
Anaconda AI - Introduction2:28
Using the Anaconda Assistant Strings4:08

13Python Module Basic Python Syntax30 · 12m

Basic Python Syntax - Arithmetic Operators3:24
Arithmetic Operators - Exercise #1
Arithmetic Operators - Exercise #2
Arithmetic Operators - Exercise #3
Arithmetic Operators - Exercise #4
Arithmetic Operators - Exercise #5
Arithmetic Operators - Exercise #6
Arithmetic Operators - Exercise #7
Arithmetic Operators - Exercise #8
Basic Python Syntax - Arithmetic Operators
Basic Python Syntax - The Double Equality Sign1:34
The Double Equality Sign - Exercise #1
Basic Python Syntax - The Double Equality Sign
Basic Python Syntax - Reassign Values1:09
Reassign Values - Exercise #1
Reassign Values - Exercise #2
Reassign Values - Exercise #3
Reassign Values - Exercise #4
Basic Python Syntax - Reassign Values
Basic Python Syntax - Add Comments1:35
Basic Python Syntax - Add Comments
Basic Python Syntax - Line Continuation0:50
Line Continuation - Exercise #1
Basic Python Syntax - Indexing Elements1:18
Indexing Elements - Exercise #1
Indexing Elements - Exercise #2
Basic Python Syntax - Indexing Elements
Basic Python Syntax - Indentation1:45
Indentation - Exercise #1
Basic Python Syntax - Indentation

14Python Module More on Operators14 · 8m

Operators - Comparison Operators2:11
Comparison Operators - Exercise #1
Comparison Operators - Exercise #2
Comparison Operators - Exercise #3
Comparison Operators - Exercise #4
Operators - Comparison Operators
Operators - Logical and Identity Operators5:36
Logical and Identity Operators - Exercise #1
Logical and Identity Operators - Exercise #2
Logical and Identity Operators - Exercise #3
Logical and Identity Operators - Exercise #4
Logical and Identity Operators - Exercise #5
Logical and Identity Operators - Exercise #6
Operators - Logical and Identity Operators

15Python Module Conditional Statements11 · 14m

Conditional Statements - The IF Statement3:02
The IF Statement - Exercise #1
The IF Statement - Exercise #2
Conditional Statements - The IF Statement
Conditional Statements - The ELSE Statement2:45
The ELSE Statement - Exercise #1
Conditional Statements - The ELIF Statement5:34
The ELIF Statement - Exercise #1
The ELIF Statement - Exercise #2
Conditional Statements - A Note on Boolean Values2:14
Conditional Statements - A Note on Boolean Values

16Python Module Functions22 · 19m

Functions - Defining a Function in Python2:02
Functions - Creating a Function with a Parameter3:50
Creating a Function with a Parameter - Exercise #1
Creating a Function with a Parameter - Exercise #2
Functions - Another Way to Define a Function2:36
Another Way to Define a Function - Exercise #1
Functions - Using a Function in Another Function1:49
Using a Function in Another Function - Exercise #1
Functions - Combining Conditional Statements and Functions3:07
Combining Conditional Statements and Functions - Exercise #1
Functions - Creating Functions Containing a Few Arguments1:17
Functions - Notable Built-in Functions in Python3:56
Notable Built-in Functions in Python - Exercise #1
Notable Built-in Functions in Python - Exercise #2
Notable Built-in Functions in Python - Exercise #3
Notable Built-in Functions in Python - Exercise #4
Notable Built-in Functions in Python - Exercise #5
Notable Built-in Functions in Python - Exercise #6
Notable Built-in Functions in Python - Exercise #7
Notable Built-in Functions in Python - Exercise #8
Notable Built-in Functions in Python - Exercise #9
Python Functions

17Python Module Sequences34 · 19m

Sequences - Lists4:02
Lists - Exercise #1
Lists - Exercise #2
Lists - Exercise #3
Lists - Exercise #4
Lists - Exercise #5
Sequences - Lists
Sequences - Using Methods3:19
Using Methods - Exercise #1
Using Methods - Exercise #2
Using Methods - Exercise #3
Using Methods - Exercise #4
Sequences - Using Methods
Sequences - List Slicing4:31
List Slicing - Exercise #1
List Slicing - Exercise #2
List Slicing - Exercise #3
List Slicing - Exercise #4
List Slicing - Exercise #5
List Slicing - Exercise #6
List Slicing - Exercise #7
Sequences - Tuples3:11
Tuples - Exercise #1
Tuples - Exercise #2
Tuples - Exercise #3
Tuples - Exercise #4
Sequences - Dictionaries4:04
Dictionaries - Exercise #1
Dictionaries - Exercise #2
Dictionaries - Exercise #3
Dictionaries - Exercise #4
Dictionaries - Exercise #5
Dictionaries - Exercise #6
Sequences - Dictionaries

18Python Module Iteration22 · 26m

Iteration - For Loops2:57
For Loops - Exercise #1
For Loops - Exercise #2
Iteration - For Loops
Iteration - While Loops and Incrementing2:26
While Loops and Incrementing - Exercise #1
Iteration - Creatie Lists with the range() Function3:50
Create Lists with the range() Function - Exercise #1
Create Lists with the range() Function - Exercise #2
Create Lists with the range() Function - Exercise #3
Iteration - Create Lists with the range() Function
Iteraion - Use Conditional Statements and Loops Together3:12
Conditional Statements and Loops - Exercise #1
Conditional Statements and Loops - Exercise #2
Conditional Statements and Loops - Exercise #3
Iteration - Conditional Statements, Functions, and Loops2:27
Conditional Statements, Functions, and Loops - Exercise #1
Using the Anaconda Assistant Several Python Tools5:56
Iteration - Iterating over Dictionaries3:08
Iterating over Dictionaries - Exercise #1
Iterating over Dictionaries - Exercise #2
Using the Anaconda Assistant Dictionaries2:22

19Python Module A Few Important Python Concepts and Terms17 · 39m

Introduction to Object Oriented Programming (OOP)5:01
Modules, Packages, and the Python Standard Library4:25
Importing Modules3:25
Important Python Concepts and Terms
Python List Comprehensions8:30
Python List Comprehensions - Exercise #1
Python List Comprehensions - Exercise #2
Python List Comprehensions - Exercise #3
Python List Comprehensions - Exercise #4
Python List Comprehensions - Exercise #5
Python Anonymous Functions (Lambda Functions)7:00
Python Anonymous Functions - Exercise #1
Python Anonymous Functions - Exercise #2
Python Anonymous Functions - Exercise #3
Python Anonymous Functions - Exercise #4
What is Software Documentation3:58
The Python Documentation6:23

20NLP Module Introduction7 · 7m

Introduction to the course2:40
Course materials and notebooks
Introduction to NLP1:37
Introduction to NLP
NLP in everyday life1:15
Supervised vs unsupervised NLP1:46
Supervised vs unsupervised NLP

21NLP Module Text Preprocessing23 · 56m

The importance of data preparation1:45
Setting up the environment6:08
Setting up the environment and exploring the packages (text article)
Python environment for NLP
Lowercasing text3:18
Lowercasing text
Removing stop words4:31
Removing stop words
Regular expressions (regex)10:17
Regex
Tokenization3:13
Tokenization
Stemming3:48
Lemmatization2:46
Stemming and lemmatization
N-grams6:26
The pandas library explanation
N-grams and pandas
A note on the practical task
Practical task Text preprocessing14:16
Additional notes Text preprocessing with pandas and NLTK
The NLTK library
Lambda functions with apply()

22NLP Module Identifying Parts of Speech and Named Entities6 · 32m

Text tagging1:25
Parts of Speech (POS) tagging6:40
Named Entity Recognition (NER)8:01
POS tagging and NER
A note on the practical task
Practical task POS and NER16:14

23NLP Module Sentiment Analysis6 · 25m

What is sentiment analysis1:59
Rule-based sentiment analysis5:24
Pre-trained transformer models5:40
A note on the practical task
Practical Task Sentiment analysis11:34
Sentiment analysis

24NLP Module Vectorizing Text4 · 9m

Numerical representation of text1:33
Bag of Words model4:44
TF-IDF3:09
Vectorizing text

25NLP Module Topic Modelling9 · 24m

What is topic modelling2:56
When to use topic modelling1:34
Latent Dirichlet Allocation (LDA)2:20
A note on the following lesson
LDA in Python8:09
Latent Semantic Analysis (LSA)1:39
LSA in Python1:21
LDA and LSA
Determining the number of topics6:08

26NLP Module Building Your Own Text Classifier5 · 17m

Building a custom text classifier0:57
Logistic regression9:47
Naive Bayes2:30
Linear support vector machine3:26
Building a custom text classifier

27NLP Module Categorizing Fake News (Case Study)9 · 49m

A note on the case study
Introducing the project3:32
Exploring our data through POS tags9:25
Extracting named entities4:51
Processing the text8:30
Does sentiment differ between news types5:11
What topics appear in fake news (Part 1)6:12
What topics appear in fake news (Part 2)5:57
Categorizing fake news with a custom classifier5:49

28NLP Module The Future of NLP4 · 8m

What is deep learning3:05
Deep learning for NLP1:51
Non-English NLP1:49
What's next for NLP1:39

29LLMs Module Introduction to Large Language Models7 · 15m

Introduction to the course2:21
Course materials and notebooks
What are LLMs2:56
How large is an LLM2:56
General purpose models1:09
Pre-training and fine tuning2:39
What can LLMs be used for3:18

30LLMs Module The Transformer Architecture9 · 23m

Deep learning recap2:32
The problem with RNNs3:35
The solution attention is all you need2:50
The transformer architecture1:02
Input embeddings2:54
Multi-headed attention3:59
Feed-forward layer2:38
Masked multihead attention1:24
Predicting the final outputs1:44

31LLMs Module Getting Started With GPT Models12 · 33m

What does GPT mean1:28
The development of ChatGPT2:28
Setting up the environment
OpenAI API2:58
Generating text2:25
Customizing GPT output4:04
Important Update
Key word text summarization3:47
Coding a simple chatbot6:17
Introduction to LangChain in Python1:27
LangChain2:50
Adding custom data to our chatbot5:21

32LLMs Module Hugging Face Transformers6 · 26m

Hugging Face package2:41
The transformer pipeline5:50
Pre-trained tokenizers9:01
Special tokens2:54
Hugging Face and PyTorchTensorFlow4:32
Saving and loading models1:25

33LLMs Module Question and Answer Models With BERT7 · 31m

GPT vs BERT3:03
BERT architecture4:40
Loading the model and tokenizer1:48
BERT embeddings3:43
Calculating the response5:33
Creating a QA bot8:41
BERT, RoBERTa, DistilBERT3:06

34LLMs Module Text Classification With XLNet6 · 26m

GPT vs BERT vs XLNET4:17
A note on the following lecture
Preprocessing our data9:58
XLNet Embeddings4:25
Fine tuning XLNet3:55
Evaluating our model3:03

35LangChain Module Introduction5 · 20m

Introduction to the course4:54
Course materials and notebooks
Business applications of LangChain5:22
What makes LangChain powerful4:33
What does the course cover5:33

36LangChain Module Tokens, Models, and Prices2 · 10m

Tokens6:07
Models and Prices3:28

37LangChain Module Setting Up the Environment3 · 13m

Setting up a custom anaconda environment for Jupyter integration3:42
Obtaining an OpenAI API key2:05
Setting the API key as an environment variable7:11

38LangChain Module The OpenAI API4 · 17m

First Steps3:51
System, user, and assistant roles3:37
Creating a sarcastic chatbot2:46
Temperature, max tokens, and streaming6:27

39LangChain Module Model Inputs8 · 39m

The LangChain framework5:41
Note on LangChain updates and course notebooks
ChatOpenAI6:25
System and human messages4:30
AI messages5:08
Prompt templates and prompt values5:23
Chat prompt templates and chat prompt values6:06
Few-shot chat message prompt templates6:16

40LangChain Module Output Parsers4 · 9m

String output parser2:28
Comma-separated list output parser3:16
A note on using langchain-classic
Datetime output parser2:48

41LangChain Module LangChain Expression Language (LCEL)11 · 52m

Piping a prompt, model, and an output parser6:34
Batching4:36
Streaming4:18
A Note about the LangChain documentation
The Runnable and RunnableSequence classes4:53
Piping chains and the RunnablePassthrough class7:32
Graphing Runnables2:15
RunnableParallel6:24
Piping a RunnableParallel with other Runnables5:32
RunnableLambda5:24
The @chain decorator4:23

42LangChain Module Retrieval Augmented Generation (RAG)19 · 1h 26m

How to integrate custom data into an LLM3:49
Introduction to RAG3:40
Introduction to document loading and splitting3:56
Introduction to document embedding6:46
Introduction to document storing, retrieval, and generation3:49
A note on Chroma integration
Indexing Document loading with PyPDFLoader7:11
Indexing Document loading with Docx2txtLoader2:25
Indexing Document splitting with character text splitter (Theory)2:47
Indexing Document splitting with character text splitter (Code along)5:20
Indexing Document splitting with Markdown header text splitter5:53
Indexing Text embedding with OpenAI6:00
Indexing Creating a Chroma vectorstore5:42
Indexing Inspecting and managing documents in a vectorstore4:22
Retrieval Similarity search5:29
Retrieval Maximal Marginal Relevance (MMR) search6:48
Retrieval Vectorstore-backed retriever3:30
Generation Stuffing documents4:23
Generation Generating a response3:52

43LangGraph Module Introduction3 · 8m

Welcome to the course!2:42
What does the course cover3:27
Course prerequisites2:13

44LangGraph Module Setting Up the Environment1 · 5m

Setting up the environment4:37

45LangGraph Module Graph Components and Implementation6 · 29m

States, nodes, and edges5:25
First graph Importing relevant classes3:46
First graph Defining a state and a node4:19
First graph Building the graph4:56
Conditional edges Defining nodes and a routing function5:40
Conditional edges Building the graph5:03

46LangGraph Module Message Management6 · 27m

The Annotated construct and reducer functions4:49
Reducer functions in action3:38
The MessagesState class3:34
The RemoveMessages class2:56
Trimming messages3:58
Summarizing messages7:53

47LangGraph Module Thread-Level Persistence4 · 17m

Checkpointers and threads3:28
Short-term memory with the InMemorySaver class5:30
The StateSnapshot class3:10
Long-term memory with SQLite4:48

48Vector Databases Module Introduction4 · 12m

Introduction to the course2:59
Course materials and notebooks
Database comparison SQL, NoSQL, and Vector5:02
Understanding vector databases4:17

49Vector Databases Module Basics of Vector Space and High Dimensional3 · 15m

Introduction to vector space4:35
Distance metrics in vector space5:50
Vector embeddings walkthrough4:11

50Vector Databases Module Introduction to The Pinecone Vector Database8 · 28m

Vector databases, comparison6:59
Pinecone registration, walkthrough and creating an Index3:39
Connecting to Pinecone using Python2:56
Assignment
Creating and deleting a Pinecone index using Python3:26
Upserting data to a pinecone vector database3:51
Getting to know the fine web data set and loading it to Jupyter2:07
Upserting data from a text file and using an embedding algorithm5:21

51Vector Databases Module Semantic Search with Pinecone and Custom18 · 1h 1m

Introduction to semantic search3:45
Introduction to the case study – smart search for data science courses5:07
Getting to know the data for the case study2:09
Data loading and preprocessing4:28
Pinecone Python APIs and connecting to the Pinecone server4:17
Embedding Algorithms4:10
Embedding the data and upserting the files to Pinecone3:28
Similarity search and querying the data4:27
How to update and change your vector database3:35
Data preprocessing and embedding for courses with section data4:11
Assignment 2
Upserting the new updated files to Pinecone2:10
Similarity search and querying courses and sections data4:11
Using the BERT embedding algorithm3:44
Assignment 3
Vector database for recommendation engines3:41
Vector database for semantic image search4:08
Vector database for biomedical research3:53

52Speech Recognition Module Introduction5 · 17m

Welcome to the world of Speech Recognition4:51
Module Resources
Course Approach4:19
How it all started Formants, harmonics, and phonemes3:20
Development and Evolution4:07

53Speech Recognition Module Sound and Speech Basics4 · 13m

How do humans recognize speech3:16
How do humanz recognize speech
Fundamentals of sound and sound waves3:29
Properties of sound waves5:48

54Speech Recognition Module Analog to Digital Conversion2 · 10m

Key concepts Sample Rate, bit depth, and bit rate5:02
Audio signal processing for Machine Learning and AI4:46

55Speech Recognition Module Audio Feature Extraction for AI Applications4 · 22m

Time-domain audio features6:54
Frequency-domain and time-frequency-domain audio features6:20
Time-domain feature extraction Framing and feature computation4:43
Frequency-domain feature extraction Fourier transform4:28

56Speech Recognition Module Technology Mechanics8 · 36m

Acoustic and language modeling3:49
Hidden Markov Models (HMMs) and traditional neural networks6:31
Deep learning models CNNs, RNNs, and LSTMs6:36
Advanced speech recognition systems Transformers4:52
Building a speech recognition model part I4:27
Building a speech recognition model part II4:20
Selecting the appropriate speech recognition tool5:48
Expanding beyond the tools we've covered

57Speech Recognition Module Setting Up the Environment4 · 15m

Installing Anaconda2:24
Setting up a new environment2:47
Installing packages for speech recognition6:16
Importing the relevant packages in Jupyter3:11

58Speech Recognition Module Transcribing Audio with Google Web5 · 32m

Audio file formats for speech recognition7:04
Importing audio files in Jupyter Notebook7:48
The SpeechRecognition library Google Web Speech API8:33
Evaluation metrics WER and CER3:11
Calculating WER and CER in Python5:35

59Speech Recognition Module Background Noise and Spectrograms3 · 20m

Understanding noise in audio files4:00
Creating a spectrogram with Python7:23
Dealing with background noise8:51

60Speech Recognition Module Transcribing Audio with OpenAI's Whisper5 · 22m

9.1Whisper AI Transformer-based speech-to-text7:37
A note on variability
Transcribing multiple audio files from a directory5:22
Saving audio transcriptions to CSV for easy analysis5:06
Reversing the process AI-powered text-to-speech3:25

61Speech Recognition Module Final Discussion and Future Directions3 · 11m

Modern practices and applications5:10
Challenges and limitations2:36
The future of speech recognition with AI3:42

62LLM Engineering Module Introduction3 · 11m

Introduction to the Course3:29
What does the course cover2:23
The Interview Tool’s Specifics5:01

63LLM Engineering Module Planning stage10 · 44m

Hosting an LLM vs Using an API4:16
Open-Source vs Closed-Source Models6:35
Tokens4:55
Pricing Hosting an LLM vs Pay-by-Token3:48
Initial Prompt Development Part 15:00
Initial Prompt Development Part 24:59
Database Design and Schema Development3:28
What Is an Activity Diagram3:32
Creating an Activity Diagram5:09
Concluding the Planning Stage2:05

64LLM Engineering Module Crafting and Testing AI Prompts5 · 23m

Adding Funds to Your OpenAI API Account
The OpenAI Playground6:50
Optimizing Temperature and Top P for Different Use Cases5:22
Prompt Engineering for Software Development6:06
How to Test Out a Prompt Template4:25

65LLM Engineering Module Getting to Know Streamlit6 · 27m

Setting up environment6:27
Streamlit's Pros and Cons2:57
Streamlit Elements Titles, Headers, and Formatting3:27
Streamlit Elements Text Methods3:24
Streamlit Elements Chat Elements4:25
Sessin State6:25

66LLM Engineering Module Developing the prototype10 · 46m

Initializing an OpenAI Client4:16
Implementing the Chat Functionality6:06
Building the Setup Page7:23
Enhancing Chatbot Interaction with Session State6:10
Refining Our Project2:43
Implementing Feedback Functionality Part 13:53
Implementing Feedback Functionality Part 26:47
Keeping Your API Key Safe Before Uploading to GitHub
Uploading Your Project in GitHub4:46
Deploying Your Streamlit App3:58

67LLM Engineering Module Solving Real-World AI Challenges11 · 48m

Introduction1:41
Application Structure3:25
Prompt Structure of HR Interviews5:57
Prompt Structure of Technical Interviews7:35
Additional Protection From Errors2:35
Hallucinations7:04
Prompt Injection3:56
Counting Tokens2:36
Cost Reduction9:17
Scaling3:04
Conclusion1:18

68AI Ethics Module Introduction to AI and Data Ethics5 · 22m

What does the course cover4:38
The AI Lifecycle From data collection to model application5:42
Why AI Ethics matter more than ever6:50
Ethics vs laws4:23
Ethics vs laws

69AI Ethics Module The Core Principles of AI Ethics4 · 15m

Privacy3:36
Transparency3:17
Accountability3:37
Fairness4:51

70AI Ethics Module Ethical Data Collection6 · 17m

Ethical sourcing and types of data4:25
Proprietary data3:30
Public data1:48
Web-scraped data2:40
Dealing with sensitive and protected information2:23
Data bias and fair representation2:34

71AI Ethics Module Ethical AI Development6 · 23m

Ethical challenges in working with labeled data4:14
Ethical considerations for unlabeled data3:54
Ethical challenges in unsupervised training3:32
Ethical considerations for supervised Fine-tuning4:00
RLHF and ethical AI behavior3:06
Inclusive and fair AI development practices3:53

72AI Ethics Module Ethical AI Deployment7 · 22m

Intellectual property and user consent in AI interactions3:38
Ethical responsibilities of foundation model developers3:22
Common issues in foundation models Open-source data3:39
Inconsistency4:32
Hallucination4:22
Inconsistency and Hallucination
Ongoing monitoring and risk mitigation for deployed AI2:16

73AI Ethics Module Ethical AI for End-Users Businesses4 · 16m

Access to AI technology for businesses of all sizes4:37
Transparency in AI decision-making processes4:59
Ethical use of AI outputs in business3:26
Responsible AI adoption and risk management for businesses3:10

74AI Ethics Module Ethical AI for End-Users Individuals3 · 10m

Equity in access to AI technology3:47
Ethical considerations in human-AI collaboration2:43
Responsible use of AI-generated outputs3:03

75AI Ethics Module ChatGPT Ethics6 · 27m

Understanding ChatGPT4:34
Privacy concerns with ChatGPT4:06
OpenAI’s privacy policies and data handling5:05
Misinformation and AI-generated content4:04
ChatGPT plagiarism5:02
ChatGPT and the environment3:41

76AI Ethics Module Data and AI Regulatory Frameworks5 · 14m

Global AI and data regulations1:49
European Union GDPR and the EU Artificial Intelligence Act4:53
United States AI regulation across states2:32
Asia-Pacific region Strong government control2:35
Africa's push for AI governance2:39

77Bonus1 · 0m

Bonus lecture