Machine Learning - A first class ticket to next generation business
A "no math" introduction to machine learning concepts. Touches on various common machine learning architectures -- including neural networks and deep learning. Includes a large number of resource links.
That’s a good thing. ) • We’re not going to dive into the math • My goal is to show you how easy it is to use • It’s a tool — just another API You don't need to understand how an engine works to drive a car. KEEP IT SIMPLE
Enhance an existing business, or • Get a new job/promotion Machine learning will give your applications superpowers ...for now. (It will be the norm very soon) WHAT IT CAN DO FOR YOU
to write a ton of code • You don’t need to invest massive amounts of time • You don’t need a data science degree • You don’t need to be a math whiz • You don’t need mountains of data MYTH BUSTING
few months… • Google open sources natural language processing platform • Amazon open sources deep learning platform • Google announces quantum computing works • IBM offers access to quantum computer • Google’s DeepMind beats Go champion WHAT’S NEW
in 10 years there’s nothing but quantum machine learning” ~Hartman Nevet Head of Google’s Quantum AI Lab via: technologyreview.com via: researchgate.net
those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” ~Mark Weiser Scientific American, 1991
US households own consumer robots 1 (robotics) • 30% of new cars will have a self-driving mode 2 (auto) • 70% of mobile users access devices via biometrics 2 (security) • We interact with 150+ smart devices (IoT) every day 2 (lifestyle) All are underpinned by machine learning 1 roboticstrends.com/article/13_of_us_households_to_own_consumer_robots_by_2020 2 weforum.org/agenda/2015/02/5-predictions-for-technology-in-2020
Suite of mobile apps Determine the most relevant (highest- converting) sales offer to present to each individual user — and the best (highest- converting) time to present it.
IS A DECISION FOREST? is male? is age > 16? is Y app installed? is X app installed? end has used > 30 days? was X function used? was Y function used? no yes no yes no yes no yes end (better ways to do this now) no yes end do it
Algorithm — Process the computer uses to learn the model (perspective) • Training — Building the model from historical data (life experience) ◦ Supervised learning — Labeled training data ◦ Unsupervised learning — Unlabeled training data ◦ Reinforcement learning — Reward-based training • Feature — Points of differentiation in the data MAJOR COMPONENTS cse.unsw.edu.au/~billw/mldict.html
(logistic regression)… • Binary (Yes or no, Actionable or non-actionable) • Pick from list (Is this tweet a question, complaint, or praise?) • Number (How much will this house sell for?) Sky's the limit on how you can apply these WHAT IS THE OUTPUT?
of frisbee.” Great example of deep learning — understanding the context of an image. io9.gizmodo.com/computers-wrote-the-caption-for-this-photograph-and-ch-1660450610
ENGAGEMENT Customer support call recordings Convert audio into text Analyze for mood keywords Determine if response is required Reach out to customer/prospect Blog & community comments Social media mentions Press & blog coverage Customer support chat Product reviews Inbound emails [ IBM Watson Speech to Text ] [ IBM Watson Tone Analyzer ] [ IBM Watson AlchemyLanguage ]
neuron takes in multiple inputs ≥ 1 input neurons ≥ 1 output neurons ≥ 1 hidden layers Output “fires” if all weighted inputs sum to a set “threshold” Each connection applies a “weighted” influence on the receiving neuron Layers build on each other (iterative) Each input can be a separate “feature”
3 bedrooms 37 years old 1450 ft2 $191,172 Is it “old” or “historic?” Is it “small” or “open floor plan?” $32,108 per bedroom $64,251 per acre Need a lower weight for “old” Apply initial abstractions Set values
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