Basic Introduction to ML

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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