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Machine Learning Essentials Supervised Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this course, machine learning will be introduced to you with the focus on supervised learning. The participants will learn about the most effective supervised machine learning techniques, and gain practice implementing them and getting them to work for themselves. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

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Targeted Groups:

This course was design to suite those who are interested in the field of Machine learning and want to work or conduct research in the field.

Course Objectives:

This course was designed to let the participants able to:

  • Understand Artificial Intelligence and Machine Learning concepts

  • Distinguish between the Machine Learning algorithms

  • Understand the Supervised Learning for Regression and classification problems

  • Conceptualize any life-problem into a mathematical model

  • Optimize the mathematical model to get the more accurate values.

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Course Contents

Unit 1: Introduction to Machine Learning

  • What is Artificial Intelligence (AI)?

  • What is Machine Learning (ML)?

  • Examples of Machine Learning Applications


Unit 2: Machine Learning Algorithms

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Recommender Systems


Unit 3: Supervised Learning

  • Regression

  • Regression Examples

  • Classification

  • Classification Examples


Unit 4: Model and Cost Function

  • Model Representation

  • Cost Function

  • Cost Function - Intuition


Unit 5: Parameter Learning

  • Gradient Descent

  • Gradient Descent Intuition

  • Gradient Descent for Linear Regression for Single Variable Function

  • Gradient Descent for Linear Regression for Multi-Variables Function

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