📚 node [[main ai page]]
Go back to [[Master Contents Page]]
Introduction to AI
-
[[Week 1 - Introduction]]
- [[Introducing AI]]
- [[What is AI]]
- [[Tanmay's Journey and take on AI]]
- [[AI is not magic - Demystify and Apply]]
- [[Women leaders in AI]]
- [[AI and the time to be creative]]
- [[AI in the media - Key questions to consider]]
- [[AI needs barriers to be successful]]
- [[AI requires buy-in from leadership]]
- [[AI's Ratchet effect]]
- [[AI-specific pain points]]
- [[Comparison of not having an AI plan in 2020]]
- [[Diversity lowers the risk of AI]]
- [[Examples and impact of AI (LIST)]]
- [[Expert Insights - AI fast forwards video for sports highlights]]
- [[Find an AI mentor]]
- [[Get To AI At Scale]]
- [[Prioritisation]]
- [[Prove the value of AI]]
- [[PWC study - AI's impact on GDP by 2030]]
- [[The 3 Foundations - (AI) is only as good as your (IA)]]
- [[The AI Job Replacement Axiom]]
- [[The AI Ladder]]
- [[The next 10,000 business cases - Kevin Kelly]]
- [[Time-Proportion Value Increase of the Journalist]]
- [[Two Key Elements Of AI - Componentry and Process]]
-
[[Week 2 - Introduction]]
- [[Adaptive resonance theory]]
- [[Four stages of human cognition]]
- [[How to get started with Cognitive Technology]]
- [[Adoption starts with education]]
- [[IBM's cognitive advantage global market report]]
- [[The Three patterns of AI technology usage]]
- [[Adoption starts with education]]
- [[Best practice - AI lessons from early adopters]]
- [[Build, deploy or collaborate]]
- [[Checklist for AI Adoption]]
- [[Four main categories of AI implementation]]
- [[Four stages of human cognition]]
- [[Functional Patterns]]
- [[Goal-based Patterns]]
- [[k-means clustering]]
- [[Technology Patterns]]
- [[Project Debater - Understanding each other through AI]]
- [[Quote the numbers - benefits of AI adoption]]
- [[Woodside - AI causes growth in jobs]]
- [[Machine Learning]]
- [[Supervised Learning]]
- [[Regression - supervised learning]]
- [[Classification - supervised learning]]
- [[Reinforcement - supervised learning]]
- [[Unsupervised Learning]]
- [[Training your model]]
- [[Three ways to evaluate models]]
- [[Deep Learning]]
- [[Neural Networks]]
- [[Back-propagation]]
- [[Perceptrons]]
- [[Bias]]
- [[Artificial Intelligence/Introduction to AI/Week 2 - Introduction/Activation function]]
- [[CNNS - Convolutional neural networks]]
- [[RNNS - Recurrent neural networks]]
- [[Artificial Intelligence/Introduction to AI/Week 2 - Introduction/Natural Language Processing]]
- [[Modern Approaches in NLP]]
- [[Recurrent Neural Networks in NLP]]
- [[Reinforcement Learning in NLP]]
- [[Deep Learning in NLP]]
- [[Long short-term memory - LSTM]]
- [[Gated Recurrent Unit]]
- [[Artificial Intelligence/Introduction to AI/Week 2 - Introduction/Activation function]]
-
[[Week 3 - Introduction]]
- [[The threat to journalists' jobs]]
- [[Journalists' jobs will have to change]]
- [[AI and regaining market share]]
- [[Can Journalists and AI Cooperate - or is it war]]
- [[AI and differentiation free vs paywalled content]]
- [[Collating meta-content with AI]]
- [[Creatives have already been replaced in places]]
- [[Using AI to fact-check and verify information]]
- [[Popularity of AI journalistic tools]]
- [[Autogeneration of news content]]
- [[Sports, fans, and data-chewing bots]]
- [[Bots for news content]]
- [[Bots for comments moderation]]
- [[AI and regaining market share]]
- [[AI is 'polysemous' - accumulates meanings]]
- [[AI is here, most unaware]]
- [[AI will purify the use of journalistic skills]]
- [[Autogeneration of news content]]
- [[Beware when using traditional messaging theory around AI]]
- [[Collating meta-content with AI]]
- [[Creativity buffering jobs from AI takeover]]
- [[Current uses of Natural Language Generated content]]
- [[Editors resist fully automated news process]]
- [[Natural Language Generation]]
- [[Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Natural Language Processing]]
- [[News Media industry changes caused by AI]]
- [[Opinions on AI replacing jobs]]
- [[Optimising the editorial work process with AI]]
- [[Origin stories of AI in News Media and Journalism]]
- [[Popularity of AI journalistic tools]]
- [[Recurring origin stories of AI]]
- [[Saad and Issa's use of AI in journalism]]
- [[Sports, fans, and data-chewing bots]]
- [[Tech has changed Journo hiring priorities]]
- [[The threat to journalists' jobs]]
- [[Traditional journalism driving AI, not AI driving journalism]]
- [[Translating worldwide news with AI]]
- [[Unsupervised Neural Networks for detecting Fake News]]
- [[Week 4 - Introduction]]
Creating Chatbots with Watson
-
[[Building AI-powered Chatbots with Watson]]
- [[Introduction to Chatbots]]
- [[What are Chatbots]]
- [[The First Chatbot - Eliza]]
- [[Seven most important conversational computing providers]]
- [[Nine Criteria for evaluating Chatbot vendors]]
- [[IBM Watson Assistant]]
- [[Amazon Alexa]]
- [[Google Dialogflow]]
-
[[How to build a Chatbot with Watson Assistant]]
- [[Intents]]
- [[Entities]]
- [[Dialogues]]
- [[Context Variables and Slots]]
- [[IBM Notes on Chatbot writing]]
- [[Preparing a Chatbot for deployment]]
- [[API details for the Flower Shop Assistant]]
- [[Dummy WordPress site Credentials]]
- [[Deploying a Chatbot to Wordpress]]
Watson Overview
-
[[Module 1 - Watson AI Overview]]
- [[The business importance of recording interactions]]
- [[Transfer Learning - IBM]]
-
[[Watson AI is changing how business is done]]
- [[Expertice on call]]
- [[Expert service - 60 per cent faster]]
- [[AR-powered technician support]]
- [[Customer response in seconds - not minutes]]
- [[Market data in plain language]]
- [[Confidence in the details]]
- [[Answering tomorrow's legal questions]]
- [[Predicting and preventing breakdowns]]
- [[Preserving institutional wisdom]]
- [[Claims assessed 25 per cent faster]]
- [[Maintenance done 90 per cent faster]]
- [[Expected AI spend next year (2021)]]
- [[Four key principles of Watson]]
- [[How AI amplifies an employee's role]]
- [[How to get IBM on board]]
- [[Watson Explorer]]
- [[Watson with Audits and Compliance]]
- [[Module 2 - Available Watson Services]]
-
[[Module 3 - Advanced Watson Services]]
- [[Watson at LivePerson Contact Centres]]
- [[Watson Compare and Comply]]
- [[Watson Knowledge Catalogue]]
- [[Watson Machine Learning]]
- [[Watson OpenScale]]
- [[Watson Personality Insights]]
- [[Watson Studio]]
- [[Watson Tone Analyser]]
- [[Big 5 personality model]]
- [[Computer Vision, its applications, and IBM Watson]]
- [[Open Source Replacements - TODO]]
-
[[Module 4 - Watson Use Cases and Resources List]]
- [[Watson At Work]]
- [[Toby - Watson's use cases]]
- [[Bradesco]]
- [[Watson for Oncology, Clinical Trial Matching, and Genomics]]
- [[Watson in use at Coca-Cola Company]]
- [[Watson at ADNOC]]
- [[Watson in use at KONE]]
- [[Watson at LivePerson]]
- [[Watson at Hilton]]
- [[402_solutionbrief_200401_openscale_copyupdate_14020014USEN_.pdf]]
- [[Data_Science_Pipeline_Readiness.pdf]]
- [[ESG-Tech-Validation-IBM-Watson-Studio-Feb-2019.pdf]]
- [[How_to_get_IBM_on_board.mp4]]
- [[Watson_services.mp4]]
- [[Watson_Studio.mp4]]
- [[Watson_Compare_And_Comply.mp4]]
- [[Watson_call_centre_solution.mp4]]
- [[IBM_Watson_Professional_Solutions_Certification.pdf]]
Python for Data Science and AI
- [[Python Week 1 Main Page]]
-
[[Python Week 2 Main Page]]
- [[Python Lists and Tuples]]
- [[Week2 Tuples Lab]]
- [[Week2 Lists Lab]]
- [[Python Dictionaries]]
- [[Week2 Dictionaries Lab]]
- [[Python Sets]]
- [[Week2 Sets Lab]]
-
[[Python Week 3 Main Page]]
- [[Python Conditions and Branching]]
- [[Week3 Conditions and Branching Lab]]
- [[Python Loops - For Loops]]
- [[Python Loops - While Loops]]
- [[Week3 Python For and While Loops Lab]]
- [[Python Functions]]
- [[Python Function Scope - Global vs Local]]
- [[Week3 Functions and Scope Lab]]
- [[Python Classes]]
- [[Week3 Classes and Objects Lab]]
- [[Python Week 4 Main Page]]
- [[Python Week 5 Main Page]]
Deploying IBM AI Services
-
[[AI Service Deployment Main Page]]
- [[Creating a Discovery Collection]]
- [[Data vs Insight]]
- [[Advanced Watson Discovery]]
- [[Creating a Watson Chatbot with Discovery]]
- [[Integrating Discovery and Assistant]]
- [[Adding Watson Speech Services]]
- [[Voice Options for your Chatbot]]
- [[Deployment Options for Watson Speech Assistant]]
- [[Deploying your Watson Assistant to Facebook Messenger]]
- [[Deploying your Watson Assistant to Slack]]
Resources
- [[Exxact_Quote_100274-1.pdf]]
- [[Pasted image.png]]
- [[Professional Machine Learning Workstation - Exxact Quote]]
- [[Recommended algorithms by usage]]
- [[HistoryOfAI.jpeg]]
- [[Creating Chatbots with Watson.pdf]]
- [[Creating_Intents.pdf]]
- [[Define_Domain_Specific_Intents.pdf]]
- [[Create_Entities.pdf]]
- [[Import_and_Export_Entities.pdf]]
- [[Implement_the_Dialog.pdf]]
- [[Add_a_preview_and_retrieve_your_credentials.pdf]]
- [[Deploy_your_Chatbot.pdf]]
- [[Explore_Context_Variables.pdf]]
- [[Master_Slots.pdf]]
- [[Enable_Digressions.pdf]]
- [[Get_to_know_the_Analytics_tab.pdf]]
- [[Pasted image 20201103162748.png]]
- [[IBM_Python_for_Data_Science_Professional.pdf]]
See also the [[The Golem Project Overview]] for the ultimate implementation of this and the [[Master of Philosophy]].
For a professionally designed workstation built for machine learning, check out the [[Professional Machine Learning Workstation - Exxact Quote]]
For a discontinued project that may still be of educational value, see the [[Main Library - Chatterbot]] and [[Why I didn't use Chatterbot]].
Currently my most popular bot is Floria from The AFIRM project, which you can read about at the [[AFIRM Overall Project Page]]
Most, it not all code is written in Python, and will link through the [[Python - Main Page]]
📖 stoas
- public document at doc.anagora.org/main-ai-page
- video call at meet.jit.si/main-ai-page
⥱ context
← back
activation function
adaptive resonance theory
adding watson speech services
adoption starts with education
advanced context variables
advanced watson discovery
ai and the time to be creative
ai glossary
ai in the media key questions to consider
ai is not magic demystify and apply
ai needs barriers to be successful
ai requires buy in from leadership
ai s ratchet effect
ai service deployment main page
ai specific pain points
an ai initiative s success relies on measurability
applications for the three types of machine learning
applications of computer vision
back propagation
best practice ai lessons from early adopters
bias
big 5 personality model
biological comparisons
bradesco
brainstorming computer vision application ideas
build deploy or collaborate
building ai powered chatbots with watson
certification build ai powered chatbots without programming (ibm)
checklist for ai adoption
classification supervised learning
cnns convolutional neural networks
comparison of not having an ai plan in 2020
computer vision its applications and ibm watson
computer vision main file
computer vision week 1 main file
computer vision week 2 main file
computer vision week 3 main file
computer vision week 4 main file
constraints on ai and robotic advances
context variables and slots
creating a watson chatbot with discovery
data ai ml pipeline creation links
data science
data vs insight
deep learning
deep learning in nlp
deploying your watson assistant to facebook messenger
deploying your watson assistant to slack
deployment options for watson speech assistant
diversity lowers the risk of ai
eleutherai main page
enriching customer service
examples and impact of ai (list)
expected ai spend next year (2021)
expert insights ai fast forwards video for sports highlights
expressions and variables
fairness in ai diversity in faces dataset
find an ai mentor
four key principles of watson
four main categories of ai implementation
four stages of human cognition
functional patterns
gated recurrent unit
get to ai at scale
goal based patterns
hidden layers
how ai amplifies an employee s role
how to build a chatbot with watson assistant
how to get ibm on board
how to get started with cognitive technology
ibm notes on chatbot writing
ibm s cognitive advantage global market report
ibm watson assistant can be text or voice
idea ai link related as you type
idea ai pattern idea journo matching
idea_ai ai enabled point cameras collecting traffic clangers
idea_ai customised sports commentary
idea_ai nlp powered meta content creation
ideas for newsrooms (list)
image classification
implementing dialogues
integrating discovery and assistant
introducing ai
introduction to chatbots
introduction to computer vision
introduction to python data types
k means clustering
long short term memory lstm
machine learning
main library chatterbot
modern approaches in nlp
module 1 watson ai overview
module 2 available watson services
module 3 advanced watson services
module 4 watson use cases and resources list
natural language processing
neural networks
one dimensional numpy
open source replacements todo
pandas
perceptron learning
perceptrons
prioritisation
professional machine learning workstation exxact quote
programming main page
project debater understanding each other through ai
prove the value of ai
pwc study ai s impact on gdp by 2030
python classes
python conditions and branching
python dictionaries
python file handling reading
python file handling writing
python function scope global vs local
python functions
python ibm api assignment lab
python lists and tuples
python loops for loops
python loops while loops
python main page
python sets
python string operations
python week 1 main page
python week 2 main page
python week 3 main page
python week 4 main page
python week 5 main page
q learning
quote the numbers benefits of ai adoption
recent research in computer vision
recommended algorithms by usage
recurrent neural networks in nlp
regression supervised learning
reinforcement learning in nlp
reinforcement supervised learning
rnns recurrent neural networks
simple apis in python
supervised learning
tanmay s journey and take on ai
technology patterns
tensor flow course master page
the 3 foundations (ai) is only as good as your (ia)
the ai job replacement axiom
the ai ladder
the business importance of recording interactions
the ladder of ai
the next 10 000 business cases kevin kelly
the testing set
the three patterns of ai technology usage
the training set
the validation set
three ways to evaluate models
time proportion value increase of the journalist
toby watson s use cases
training your model
transfer learning ibm
two dimensional numpy
two key elements of ai componentry and process
unsupervised learning
voice options for your chatbot
watson ai is changing how business is done
watson at adnoc
watson at hilton
watson at liveperson
watson at liveperson contact centres
watson at work
watson availability
watson compare and comply
watson for oncology clinical trial matching and genomics
watson ibm use cases certification
watson in use at coca cola company
watson in use at kone
watson knowledge catalogue
watson machine learning
watson natural language understanding
watson openscale
watson personality insights
watson speech to text
watson studio
watson text to speech
watson tone analyser
watson with audits and compliance
week 1 introduction
week 1 main page
week 2 introduction
week 2 watson discovery
week 3 introduction
week 4 introduction
week3 classes and objects lab
week4 numpy in one dimension lab
week4 numpy in two dimensions lab
week4 pandas data lab
week5 us economic dashboard in python lab
what is ai
women leaders in ai
woodside ai causes growth in jobs
activation function
adaptive resonance theory
adding watson speech services
adoption starts with education
advanced context variables
advanced watson discovery
ai and the time to be creative
ai glossary
ai in the media key questions to consider
ai is not magic demystify and apply
ai needs barriers to be successful
ai requires buy in from leadership
ai s ratchet effect
ai service deployment main page
ai specific pain points
an ai initiative s success relies on measurability
applications for the three types of machine learning
applications of computer vision
back propagation
best practice ai lessons from early adopters
bias
big 5 personality model
biological comparisons
bradesco
brainstorming computer vision application ideas
build deploy or collaborate
building ai powered chatbots with watson
certification build ai powered chatbots without programming (ibm)
checklist for ai adoption
classification supervised learning
cnns convolutional neural networks
comparison of not having an ai plan in 2020
computer vision its applications and ibm watson
computer vision main file
computer vision week 1 main file
computer vision week 2 main file
computer vision week 3 main file
computer vision week 4 main file
constraints on ai and robotic advances
context variables and slots
creating a watson chatbot with discovery
data ai ml pipeline creation links
data science
data vs insight
deep learning
deep learning in nlp
deploying your watson assistant to facebook messenger
deploying your watson assistant to slack
deployment options for watson speech assistant
diversity lowers the risk of ai
eleutherai main page
enriching customer service
examples and impact of ai (list)
expected ai spend next year (2021)
expert insights ai fast forwards video for sports highlights
expressions and variables
fairness in ai diversity in faces dataset
find an ai mentor
four key principles of watson
four main categories of ai implementation
four stages of human cognition
functional patterns
gated recurrent unit
get to ai at scale
goal based patterns
hidden layers
how ai amplifies an employee s role
how to build a chatbot with watson assistant
how to get ibm on board
how to get started with cognitive technology
ibm notes on chatbot writing
ibm s cognitive advantage global market report
ibm watson assistant can be text or voice
idea ai link related as you type
idea ai pattern idea journo matching
idea_ai ai enabled point cameras collecting traffic clangers
idea_ai customised sports commentary
idea_ai nlp powered meta content creation
ideas for newsrooms (list)
image classification
implementing dialogues
integrating discovery and assistant
introducing ai
introduction to chatbots
introduction to computer vision
introduction to python data types
k means clustering
long short term memory lstm
machine learning
main library chatterbot
modern approaches in nlp
module 1 watson ai overview
module 2 available watson services
module 3 advanced watson services
module 4 watson use cases and resources list
natural language processing
neural networks
one dimensional numpy
open source replacements todo
pandas
perceptron learning
perceptrons
prioritisation
professional machine learning workstation exxact quote
programming main page
project debater understanding each other through ai
prove the value of ai
pwc study ai s impact on gdp by 2030
python classes
python conditions and branching
python dictionaries
python file handling reading
python file handling writing
python function scope global vs local
python functions
python ibm api assignment lab
python lists and tuples
python loops for loops
python loops while loops
python main page
python sets
python string operations
python week 1 main page
python week 2 main page
python week 3 main page
python week 4 main page
python week 5 main page
q learning
quote the numbers benefits of ai adoption
recent research in computer vision
recommended algorithms by usage
recurrent neural networks in nlp
regression supervised learning
reinforcement learning in nlp
reinforcement supervised learning
rnns recurrent neural networks
simple apis in python
supervised learning
tanmay s journey and take on ai
technology patterns
tensor flow course master page
the 3 foundations (ai) is only as good as your (ia)
the ai job replacement axiom
the ai ladder
the business importance of recording interactions
the ladder of ai
the next 10 000 business cases kevin kelly
the testing set
the three patterns of ai technology usage
the training set
the validation set
three ways to evaluate models
time proportion value increase of the journalist
toby watson s use cases
training your model
transfer learning ibm
two dimensional numpy
two key elements of ai componentry and process
unsupervised learning
voice options for your chatbot
watson ai is changing how business is done
watson at adnoc
watson at hilton
watson at liveperson
watson at liveperson contact centres
watson at work
watson availability
watson compare and comply
watson for oncology clinical trial matching and genomics
watson ibm use cases certification
watson in use at coca cola company
watson in use at kone
watson knowledge catalogue
watson machine learning
watson natural language understanding
watson openscale
watson personality insights
watson speech to text
watson studio
watson text to speech
watson tone analyser
watson with audits and compliance
week 1 introduction
week 1 main page
week 2 introduction
week 2 watson discovery
week 3 introduction
week 4 introduction
week3 classes and objects lab
week4 numpy in one dimension lab
week4 numpy in two dimensions lab
week4 pandas data lab
week5 us economic dashboard in python lab
what is ai
women leaders in ai
woodside ai causes growth in jobs
↑ pushing here
(none)
(none)
↓ pulling this
(none)
(none)
→ forward
402_solutionbrief_200401_openscale_copyupdate_14020014usen_ pdf
adaptive resonance theory
add_a_preview_and_retrieve_your_credentials pdf
adding watson speech services
adoption starts with education
advanced context variables
advanced watson discovery
afirm overall project page
ai and differentiation free vs paywalled content
ai and regaining market share
ai and the time to be creative
ai in the media key questions to consider
ai is here most unaware
ai is not magic demystify and apply
ai is polysemous accumulates meanings
ai needs barriers to be successful
ai requires buy in from leadership
ai s ratchet effect
ai service deployment main page
ai specific pain points
ai will purify the use of journalistic skills
ai won t look like humans kevin kelly
all business is data business kevin kelly
amazon alexa
answering tomorrow s legal questions
api details for the flower shop assistant
ar powered technician support
artificial intelligence/introduction to ai/week 2 introduction/activation function
artificial intelligence/introduction to ai/week 2 introduction/natural language processing
artificial intelligence/introduction to ai/week 3 introduction/natural language processing
artificial intelligence/watson ai overview/module 2 available watson ai services/watson discovery
autogeneration of news content
back propagation
best practice ai lessons from early adopters
beware when using traditional messaging theory around ai
bias
big 5 personality model
bots for comments moderation
bots for news content
bradesco
build deploy or collaborate
building ai powered chatbots with watson
can an ai be creative
can journalists and ai cooperate or is it war
checklist for ai adoption
child dialogue nodes
claims assessed 25 per cent faster
classification supervised learning
cloud services
cnns convolutional neural networks
collating meta content with ai
comparison of not having an ai plan in 2020
computer vision its applications and ibm watson
confidence in the details
constraints on ai and robotic advances
context variables and slots
create_entities pdf
creating a discovery collection
creating a watson chatbot with discovery
creating chatbots with watson pdf
creating_intents pdf
creatives have already been replaced in places
creativity buffering jobs from ai takeover
current uses of natural language generated content
customer response in seconds not minutes
data science
data vs insight
data_science_pipeline_readiness pdf
deep learning
deep learning in nlp
define_domain_specific_intents pdf
deploy_your_chatbot pdf
deploying a chatbot to wordpress
deploying your watson assistant to facebook messenger
deploying your watson assistant to slack
deployment options for watson speech assistant
dialogues
diversity lowers the risk of ai
dummy wordpress site credentials
editors resist fully automated news process
enable_digressions pdf
entities
esg tech validation ibm watson studio feb 2019 pdf
examples and impact of ai (list)
expected ai spend next year (2021)
expert insights ai fast forwards video for sports highlights
expert service 60 per cent faster
expertice on call
explore_context_variables pdf
expressions and variables
exxact_quote_100274 1 pdf
fallback child node
filtering and saving data with pandas
find an ai mentor
four key principles of watson
four main categories of ai implementation
four stages of human cognition
functional patterns
gated recurrent unit
get to ai at scale
get_to_know_the_analytics_tab pdf
goal based patterns
google dialogflow
historyofai jpeg
how ai amplifies an employee s role
how to build a chatbot with watson assistant
how to get ibm on board
how to get started with cognitive technology
how_to_get_ibm_on_board mp4
ibm digital badge python for applied data science
ibm notes on chatbot writing
ibm s cognitive advantage global market report
ibm watson assistant
ibm_python_for_data_science_professional pdf
ibm_watson_professional_solutions_certification pdf
implement_the_dialog pdf
implementing dialogues
import_and_export_entities pdf
integrating discovery and assistant
intents
introducing ai
introduction to chatbots
introduction to python data types
job creation in an ai future
journalists jobs will have to change
k means clustering
long short term memory lstm
machine learning
main library chatterbot
maintenance done 90 per cent faster
market data in plain language
master contents page
master of philosophy main page|master of philosophy
master_slots pdf
modern approaches in nlp
module 1 watson ai overview
module 2 available watson services
module 3 advanced watson services
module 4 watson use cases and resources list
natural language generation
neural networks
news media industry changes caused by ai
nine criteria for evaluating chatbot vendors
open source replacements todo
opinions on ai replacing jobs
optimising the editorial work process with ai
origin stories of ai in news media and journalism
pandas
pasted image 20201103162748 png
pasted image png
perceptrons
popularity of ai journalistic tools
predicting and preventing breakdowns
preparing a chatbot for deployment
preserving institutional wisdom
prioritisation
professional machine learning workstation exxact quote
project debater understanding each other through ai
prove the value of ai
pwc study ai s impact on gdp by 2030
python classes
python conditions and branching
python dictionaries
python file handling reading
python file handling writing
python function scope global vs local
python functions
python lists and tuples
python loops for loops
python loops while loops
python main page
python sets
python string operations
python week 1 main page
python week 2 main page
python week 3 main page
python week 4 main page
python week 5 main page
question what can ai do
quote the numbers benefits of ai adoption
recommended algorithms by usage
recurrent neural networks in nlp
recurring origin stories of ai
regression supervised learning
reinforcement learning in nlp
reinforcement supervised learning
rnns recurrent neural networks
saad and issa s use of ai in journalism
seven most important conversational computing providers
sharing a jupyter notebook to github
sports fans and data chewing bots
supervised learning
tanmay s journey and take on ai
tech has changed journo hiring priorities
technology patterns
the 3 foundations (ai) is only as good as your (ia)
the ai job replacement axiom
the ai ladder
the business importance of recording interactions
the first chatbot eliza
the golem project overview
the ladder of ai
the next 10 000 business cases kevin kelly
the threat to journalists jobs
the three patterns of ai technology usage
three ways to evaluate models
time proportion value increase of the journalist
toby watson s use cases
traditional journalism driving ai not ai driving journalism
training your model
transfer learning ibm
translating worldwide news with ai
two key elements of ai componentry and process
unsupervised learning
unsupervised neural networks for detecting fake news
using ai to fact check and verify information
vendor lock in
voice options for your chatbot
watson ai is changing how business is done
watson at adnoc
watson at hilton
watson at liveperson
watson at liveperson contact centres
watson at work
watson availability
watson compare and comply
watson explorer
watson for oncology clinical trial matching and genomics
watson in use at coca cola company
watson in use at kone
watson knowledge catalogue
watson machine learning
watson natural language understanding
watson openscale
watson personality insights
watson speech to text
watson studio
watson text to speech
watson tone analyser
watson with audits and compliance
watson_call_centre_solution mp4
watson_compare_and_comply mp4
watson_services mp4
watson_studio mp4
we just don t understand understanding
week 1 introduction
week 2 introduction
week 3 introduction
week 4 introduction
week1 string operations lab
week1 types expressions and variables lab
week2 dictionaries lab
week2 lists lab
week2 sets lab
week2 tuples lab
week3 classes and objects lab
week3 conditions and branching lab
week3 functions and scope lab
week3 python for and while loops lab
week4 pandas data lab
week4 reading from files lab
week4 writing to files lab
week5 us economic dashboard in python lab
what are chatbots
what is ai
why i didn t use chatterbot
women leaders in ai
woodside ai causes growth in jobs
402_solutionbrief_200401_openscale_copyupdate_14020014usen_ pdf
adaptive resonance theory
add_a_preview_and_retrieve_your_credentials pdf
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