Machine Learning With Logistic Regression
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Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
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What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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- 2. Agenda • Machine Learning – Intro? • Regression vs. Classification • Introduction to Logistic Regression • Learning of Logistic Regression • Use cases
- 3. SKYNET
- 4. Machine learning ● Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed.
- 6. Machine Learning techniques ● Supervised ● Unsupervised ● Reinforcement
- 7. Supervised Learning techniques ● There are two categories for supervised learning techniques – Regression – Classification
- 10. ● Classification – Output type: discrete – Trying to find: a boundary – Evaluation: accuracy ● Regression – Output type: continuous – Trying to find: best fit line – Evaluation: sum of sqaured errors
- 14. Logistic Regression ● A type of classification algorithm ● Based on linear regression to evaluate output and to minimize the error ● Named after the method it uses to evaluate the outputs the Logit function
- 15. What is Logit Function? ● Odds: Chances of happening one event over chances of this event not happening. ● i.e.
- 17. ● In previous slide we saw the graph of Logit function. ● This Logit function is bounded over x (between 0 and 1 for input values) and unbounded over y-axis (output values) ● But we need y-axis (output values) to be bounded to classify them.
- 18. ● Hence we take the inverse of the logit function
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