This document provides an overview of machine learning, including definitions of key concepts like pattern recognition and algorithm generation. It also discusses the role of data scientists in identifying relevant data and algorithms. A variety of machine learning techniques are covered, such as supervised learning methods like classification, regression, and anomaly detection. Recommended resources on machine learning basics, algorithms, and example applications are also listed.

1. Machine Learning and Cognitive Services
Karthikeyan VK
Karthik_3030@yahoo.com
@karthik3030

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What is Machine Learning ?
➢Looking for patterns in Data
➢Generate Code
➢Recognize those pattern in new
Data

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What Machine Learning does ?
➢Predicting future
➢Organizing or Grouping

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Why should I Care ?

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Artificial Intelligence vs ML vs Deep Learning

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Find Patterns

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Find Patterns now

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Machine Learning Process

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Role of Data Scientist
➢Which raw data to use?
➢How should that data be processed to
create prepared data?
➢Identify Combinations of prepared data
and machine learning algorithms should
you use to create the best model?

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Basics
➢Statistics
➢Linear algebra
➢Probability
➢Standard deviation
➢Slope

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Fallacies vs Bias vs Variance
Fallacies are mistakes of reasoning
Thinking habits which are likely to threaten
objectivity or to lead to errors in reasoning.
Error from sensitivity to small
fluctuations(Including anomaly)

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Features vs Labels
Features
Input
Labels
Output
Name, Amount, Where Issued,
Age of Cardholder
Genuine/Fraud

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Five Questions
Is this A or B
?
Is this Weird
?
How much
or many ?
How this is
organized ?
What should
I do next ?

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Types of algorithms

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Supervised Learning
➢Classification
➢Regression
➢Anomaly Detection

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Supervised Learning - Classification

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Supervised Learning - Regression

19. Enter TextSupervised Learning – Anomaly Detection

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Un-Supervised Learning
➢Clustering
➢Dimensionally Reduction

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Un-Supervised Learning - Clustering

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R vs Python

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Usages of Machine learning

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Philosophical question

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Books & Movies
➢Cartoon Introduction to Statistics
➢Thinking Fast & Slow by Daniel Kaheman
➢Art of thinking clearly – Rolf Dobelli
➢I-Robot
➢Terminator 2

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DEMO

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References
@karthik3030
• https://docs.microsoft.com/en-us/azure/machine-learning/studio/data-science-for-beginners-
the-5-questions-data-science-answers
• https://docs.microsoft.com/en-us/azure/machine-learning/studio/what-is-ml-studio
• https://in.udacity.com/course/intro-to-machine-learning--ud120
• https://app.pluralsight.com/player?course=python-getting-started&author=bo-
milanovich&name=python-getting-started-m1&clip=2&mode=live

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@karthik3030
Need
Meetup
Space

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@karthik3030
Thank you
/Q&A