This document provides an overview of machine learning. It defines machine learning as the field concerned with constructing computer programs that improve with experience at performing tasks. The document discusses how machine learning is used in areas like search predictions, photo tagging, recommendations, and medical testing. It also outlines different machine learning techniques like classification, clustering, and regression. Finally, it lists popular programming languages, libraries, and cloud services used for machine learning applications in domains such as webspam detection, robotics, banking, and more.

1. MACHINE LEARNING
- Rajat Kumar
- /in/raj4t

2. CONTENTS
- What is Machine Learning ?
- The Buzz - AI vs ML
- ML in our Daily Life
- Motivating Example
- Why Now ?
- Working
- Categories
- Tehniques
- Applications
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3. WHAT IS MACHINE LEARNING ?
Learning = Improving with experience at some task.
“The field of machine learning is concerned with the question of how to
construct computer programs that automatically improve with experience.”
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LEARNING
ALGORITHMS
TRAINING DATA
TRAINED MACHINE
(MODELS)

4. THE BUZZ - AI vs ML
Artificial Intelligence is the Philosophy.
Machine Learning is the Technique.
- Machine Learning is a sub-domain of AI. It is way to perform AI.
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5. MACHINE LEARNING IN OUR DAILY LIFE
We use ML dozens of time a day.
- Google Search Prediction
- Photo Tagging (Facebook)
- Product Recommendations
- (Netflix, Amazon, Flipkart)
- Biology - Medical tests
- Spam Detection
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6. MOTIVATING EXAMPLE
- Email Spam Detection :
Output : Categorize email messages as spam or legitimate.
Objective Function : Percentage of emails correctly classified.
Input : Database of emails, some with human givel labels.
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7. WHY NOW ?
- Flood of available data.
- Increasing Computational Power.
- Increasing support from Industries.
- Growing progress in available algorithms
- and theory developed by researchers.
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8. WORKING
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9. CATEGORIES
- Supervised Learning : Correct classes of training data are known.
- a
- Unsupervised Learning : Correct classes of training data are not known.
- a
- Semi-supervised Learning : A mix of supervised and Unsupervised Learning
- a
- Reinforcement Learning : Allows the machine to learn its behaviour based on
feedback from the environment.
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10. TECHNIQUES
- Classification : Predict class from observation.
- Clustering : Group Observations into Meaningful Groups.
- Regression (Prediction) : Predict value from Observations.
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11. TECHNOLOGIES IN PRODUCTION
- Python : Loads of Libraries - scikit-learn, Keras, Theano
- R Language
- Matlab / GNU Octave
- Tensorflow : Library by Google Brain
- Orange
- Apache Spark
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12. APPLICATIONS
- Applications in : Fighting Webspam, Imitation Learning(Robotics), Medical
Tech, Automatic Translation, Security, Banking/Telecom.
- Recent ML Systems :
Azure ML Studio
IBM Watson
BigML
Amazon ML.
- Kaggle.
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13. ANY QUESTIONS ?
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