This document provides an overview of different types of artificial intelligence and machine learning techniques, describing what they are, how they work, and common use cases. It begins by defining artificial intelligence and machine learning. The main types of machine learning covered are supervised learning (including linear regression, logistic regression, decision trees, naive bayes, support vector machines, random forest, adaboost, gradient boosting trees, and neural networks), unsupervised learning (including k-means clustering, gaussian mixture models, hierarchical clustering, and recommender systems), and reinforcement learning. Deep learning techniques like convolutional neural networks, recurrent neural networks, and multilayer perceptrons are also explained. The document aims to simplify AI concepts and techniques for business use.

1. Simplifying AI:
What to use when?

2. We read about
the use ML
models and ML
Ops, Deep
Learning and
even sentient AI
everyday...

3. We get the
shivers when we
see machines
behaving like
impulsive people
on Netflix
movies...

4. How much do we know
about them? Do we know
what they mean? And what
they mean for our
business?

5. Let's Uncomplicate by
keeping Netflix aside...
2020

6. Let's Uncomplicate by
keeping Netflix aside...
2020

7. Let's define the coolest
term first
WHAT
IS AI?

8. When a machine starts to perform cognitive
functions that we associate with human
minds such as perceiving, reasoning,
learning and problem solving - we call it
'Artificial Intelligence'.

9. The term was coined by Prof
John McCarthy which he
explained as 'the science
and engineering of making
intelligent machines'.

10. Computer Vision, Robotics,
Natural Language
Processing, Machine
Learning are all
technologies that enable AI.

11. At the heart of AI is
machine learning
2020

12. What does Machine Learning do?
Most recent advances in AI has
been done through Machine
Learning. ML Algorithms detect
patterns in large sets and learn to
make predictions and
recommendations on their own.

13. The coolest part is - the
ML algorithms can adapt
and improve themselves
over time as more data
becomes accessible.
2020

14. Types of ML Outputs Descriptive
Describe what happened. This is heavily used across
industries. E.g - LMS analytics, learner behavior.
01.
Prescriptive
Describe what will happen. This is inherently
probabilistic. e.g: Financial Monitoring.
02.
Describe What to do. e.g: Making investment
predictions or predicting learner drop-out rates.
Predictive
03.
There are three types of analytics.
Out of them Prescriptive and
Predictive are key domains of ML
outputs.
MACHINE
LEARNING

15. The three types of Machine Learning: Type 1
Supervised
Learning
We give the input data
We give the output variables
We label all the data
We train the algorithm to make
connections between the input
and output variables.
Then we have coffee as the
algorithm works on its own

16. The three types of Machine Learning: Type 2
Unsupervised
Learning
We give the input data
The algorithm figures out the
output variable on its own.
E.g: An algorithm figures out
types of probable customers
for a product from social media
chatter.
We have coffee after giving
only the input data

17. The three types of Machine Learning: Type 3
Reinforcement
Learning
An algorithm figures out how to
do a particular task by getting
rewarded for doing it right and
punished for doing it wrong.
E.g: If a self-driving car hits a
pedestrian you take away its
fuel. That's punishment.
We stand behind the algorithm
with a stick here.

18. And there's
Transfer
Learning too...
When a ML algorithm has
worked something out in a
particular domain, it is easier to
apply the same knowledge and
learn something faster in a
adjacent areas.

19. How do we use these algos in
actual business cases
2020

20. Let's start with the
Supervised Learning
Algorithms
2020

21. Linear Regression
THE A-Z OF MAKING IT STICK 2020
Linear Regression is a standard
method for modeling the past
relationship between independent
variables and dependent variables
to help predict future values of
the output.
This can help you understand
product-sales drivers e.g - prices
of competition, distribution, etc.
This can help you optimize price
points and estimate product-price
elasticities.
What is it? Where to use?

22. Logistic Regression
THE A-Z OF MAKING IT STICK 2020
An extension of Linear Regression
-Logistic regression is used for
classification tasks where the
output is always in a binary (a Yes
or No answer).
Logistic regression is used in
areas where the output variable
needs to be a yes/no answer, for
example: will someone pay off a
loan, predict if a skin lesion is
malignant or benign.
What is it? Where to use?

23. Linear/Quadratic Discriminant Analysis
THE A-Z OF MAKING IT STICK 2020
A Linear Discriminant Analysis
upgrades a logistic regression to
deal with non-linear problems so
that if you make changes in the
input variables they do not have
proportional changes to the
output variables.
If you are a SaaS firm, this can
help you predict the possibility of
Client churn, or even the potential
of a sales person closing a lead.
What is it? Where to use?

24. Decision Trees
THE A-Z OF MAKING IT STICK 2020
A highly interpretable
classification or regression model
that splits data-features into
branches at decision nodes until a
final decision output is made.
If you are building a product,
Decision Trees can help you
identify which feature is most
likely to drive sales/adoption of
the product.
What is it? Where to use?

25. Naive Bayes
THE A-Z OF MAKING IT STICK 2020
Classification technique that
applies Bayes theorem which
allows the probability of an event
to be calculated based on the
knowledge of factors that might
affect it.
You can use Naive Bayes to
analyze sentiments - such as
understanding your product
perception in the market or even
create classifiers to identify to
filter spam emails (Naive Bayes
can help you classify emails which
say 'I am a sad billionnaire in
Nigeria' and automatically put
them in spam.
What is it? Where to use?

26. Naive Bayes
THE A-Z OF MAKING IT STICK 2020
Classification technique that
applies Bayes theorem which
allows the probability of an event
to be calculated based on the
knowledge of factors that might
affect it.
You can use Naive Bayes to
analyze sentiments - such as
understanding your product
perception in the market or even
create classifiers to identify to
filter spam emails (Naive Bayes
can help you classify emails which
say 'I am a sad billionnaire in
Nigeria' and automatically put
them in spam.
What is it? Where to use?

27. Support Vector Machine
THE A-Z OF MAKING IT STICK 2020
Support Vector Machine is a
technique that's typically used for
classification but can be
transformed to perform
regression. It draws an optimal
division between classes
You can predict how many
patients a hospital will need to
serve in a given time period.
Support Vector Machine
algorithms are also applied in text
classification, image recognition,
face recognition, etc.
What is it? Where to use?

28. Random Forest
THE A-Z OF MAKING IT STICK 2020
Random Forest improves the
accuracy of a single Decision Tree
by generating multiple decision
trees and then identifying the best
output by taking a majority vote.
You can predict call volumes in
call centers and take relevant
hiring and training decisions by
using the Random Forest
algorithm.
What is it? Where to use?

29. AdaBoost
THE A-Z OF MAKING IT STICK 2020
Think of an AdaBoost (or Adaptive
Boosting) is iike a discerning CEO
who troubles everyone for the
best result. The AdaBoost is a
classification technique that runs
a multitude of models to come up
with a decision and weighs them
based on their accuracy.
You can predict call volumes in
call centers and take relevant
hiring and training decisions by
using the Random Forest
algorithm.
What is it? Where to use?

30. Gradient Boosting Trees
THE A-Z OF MAKING IT STICK 2020
A classification technique where
the decision trees come in a
sequential manner by correcting
the errors of the previous decision
trees. The final output is a
combination of the results of all
the trees.
This can be used in forecasting -
such as forecasting of demand or
inventory levels in eCommerce.
What is it? Where to use?

31. Simple Neural Network
THE A-Z OF MAKING IT STICK 2020
A model in which artificial neurons
make up three layers ( an input
layer, a hidden layer and an output
layer) that can be used to classify
data or find relationships between
variables in regression problems.
Predict the probability of a user
paying a certain price for a
particular service
Can be used in SaaS services, OTT
services, healthcare programs,
etc.
What is it? Where to use?

32. Whew!
Wasn't that
quite a bit!
Here are some quick quips on
where supervised learning is going!

33. For supervised learning
to run effectively - it
needs tons of labeled
data
WHICH,
FRANKLY... IS A
CHALLENGE.

34. That's why Tesla is using
SELF SUPERVISED LEARNING.
THE A-Z OF MAKING IT STICK 2020

35. Supervised learning can be time consuming and may take
months to create the right dataset to train the ML model.
SELF-SUPERVISED LEARNING IS A MACHINE LEARNING
APPROACH WHERE THE MODEL TRAINS ITSELF BY
LEVERAGING ONE PART OF THE DATA TO PREDICT
THE OTHER PART AND GENERATE LABELS
ACCURATELY.

36. Robotic surgeries: SSL can help in robotic surgeries by
estimating the depth of the human body and providing
better medical visuals.
Autonomous cars: SSL can estimate the roughness of the
terrain.
Where can Self Supervised Learning be effective:

37. Let's move on to
Unsupervised Learning

38. K-Means Clustering
The algorithm helps put data into a
number of groups (K) each of
which contain data of similar
characteristics.
K-Means Clustering can help you
identify customer types, customer
preferences, and even customer
personas from unstructured data
such as social media
conversations.
What is it? Where to use?

39. K-Means Clustering
The algorithm helps put data into a
number of groups (K) each of
which contain data of similar
characteristics.
K-Means Clustering can help you
identify customer types, customer
preferences, and even customer
personas from unstructured data
such as social media
conversations.
What is it? Where to use?

40. Gaussian Mixture Model
A generalized version of K-Means
Clustering, the GMM allows for
more flexibility in the size and
shape of the clusters
Gaussian Mixture Model can help
in better segmentation of
customers using less-distinct
customer characteristics.
For example, you can use the
GMM to identify the chances of
employee attrition.
What is it? Where to use?

41. Hierarchical Clustering
It aggregates clusters in a
hierarchical tree to form a
classification system.
You can use the Hierarchical
Clustering algorithm to divide your
loyalty program customers into
micro-segments to devise a more
personalized approach towards
customer-outreach and increase
ARR.
What is it? Where to use?

42. Recommender Systems
A recommender system often
uses cluster behavior prediction
to identify the important data
necessary for making a
recommendation
We did get back to Netflix didn't
we, even though we kept it aside.
Netflix uses the Recommender
system to make personalized
movie recommendations to you.
Next time you get a Korean Sci Fi
recommendation after watching a
Russian Sci Fi, just remember
what was being used.
What is it? Where to use?

43. Now for
mistakes
and
punishment
s....
You got it! We are talking about
Reinforcement learning here.

44. Some Use Cases here
Reinforcement learning can be
used to make the right trade in
time. The way you train your algo
is by punishing it every-time it
makes the wrong trade.
Using a multi-modal input model
you train your self-driving car to
avoid mistakes, potholes, hitting
pedestrians on the road, going in
the wrong road, etc. Over a period
of time through a cycle of rewards
and punishments your algo
optimizes its driving behavior.
Algorthmic trading
Optimizing driving
behaviour of self-
driving cars

45. And now for
Deep Learning
It's the key tech behind some fo the
coolest inventions such as Self-
Driving cars, voice control behind
phones, etc.

46. We are
mirroring your
neurons
I

47. Deep Learning is a
subset of Machine
Learning that uses
algorithms inspired by
the structure and
functions of the brain's
neural network

48. Deep Learning and Artificial Neurons
receive input values,
weighs these values with weights and
returns an output value.
Deep Learning works with artificial neurons. "Artificial Neurons" are
mathematical tools that
The value returned by the neuron is called an 'activation' similar to the
electrical signals that a human neuron receives from its dendrites and
decides if it needs to activate it to other neurons.

49. How does Deep Learning work?
In Deep Learning, an Artificial Neural Network passes information
through a series of neurons in an input layer. Think of your sensory
organs (ear, nose) taking in information and passing onto your brain.
The hidden layers at the second level processes this information and
assigns weights to it. For example, if you are trying to predict an
inventory - the weightage given to past sales history will be higher than
the week when it has been sold.
Deep Learning is used in predicting and completing complex tasks such
as predicting the weather.

50. Types of Deep Learning Neural Networks
Recurrent Neural Networks
Convolutional Neural Networks
Multilayered Perceptrons
The three popular types of Deep Learning Neural Networks include the
following:

51. Convolutional Neural
Networks
Recurrent Neural Networks
A ConvNet (employed in areas like
Computer Vision) use a range of
convolutional operations to extract
the features given an image or video.
A ConvNet or CNN is a favourite when it
comes to areas like image
classification, image extraction, videos,
etc.
The applications of CNN can be seen in
a range of industries starting from
surveillence, creative, medicine, etc.
RNN uses sequential data feeding.
Thus, the inputs in RNN include
current input as well as previous
samples. RNN is good with time series
calculations and handling sequential
data.
The application of RNN include text
predictions - if you are about to type
something in the search box and you
get a series of choices based on your
previous inputs as well as common
inputs of others, then it is RNN at play.
The applications of RNN go into
understanding human language and has
implications in Natural Language
Processing and Natural Language
Understanding.

52. Multilayer
Perceptrons
Multilayer perceptron is a
classical neural network used for
basic operations like data
visualization, data compression,
and encryption. It basically has a
input layer, hidden layers and
output layer
Multilayer Perceptrons are used in
areas like data visualization, data
analysis, etc.

53. WHOA!
WASN'T THAT
A LOT? BUT
BEFORE WE
GO... HERE'S A
SHORT
TEASER...

54. Sunspring is a 2016 movie written by an AI
called Benjamin. Can you guess the tech that
was used in training up the AI here?