Linear Regression Models: Introduction
Machine Learning
| Beginner
- 13 videos | 1h 18m 38s
- Includes Assessment
- Earns a Badge
Machine learning (ML) is everywhere these days, often invisible to most of us. In this course, you will discover one of the fundamental problems in the world of ML: linear regression. Explore how this is solved with classic ML as well as neural networks. Key concepts covered here include how regression can be used to represent a relationship between two variables; applications of regression, and why it is used to make predictions; and how to evaluate the quality of a regression model by measuring its loss. Next, learn techniques used to make predictions with regression models; compare classic ML and deep learning techniques to perform a regression; and observe various components of a neural network and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; how to calculate the optimal weights and biases of a neural network; and how to find the optimal parameters for a neural network.
WHAT YOU WILL LEARN
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define what regression is and recall how it can be used to represent a relationship between two variables
identify the applications of regression and recognize why it is used to make predictions
describe how to evaluate the quality of a regression model by measuring its loss
recognize the specific relationship which needs to exist between the input and output of a regression model
describe the technique used in order to make predictions with regression models
compare classic ML and deep learning techniques to perform a regression -
identify the various components of a neural network such as neurons and layers and how they fit together
recall the two types of functions used in a neuron and their individual roles
describe the configurations required to use a neuron for linear regression
list the steps involved in calculating the optimal weights and biases of a neural network
define the technique of gradient descent optimization in order to find the optimal parameters for a neural network
recall key concepts of linear regression and deep learning
IN THIS COURSE
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1.Course Overview2m 23sUP NEXT
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2.Statistical Tools and Regression8m 55s
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3.Reasons to Use Regression7m 17s
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4.Regression Loss: Least Square Error5m 50s
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5.Capturing Variance in Regression7m 40s
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6.Prediction Using Regression3m 36s
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7.Introduction to Deep Learning7m 24s
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8.The Architecture of Neural Networks5m 3s
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9.Neurons: The Building Blocks of a Neural Network7m 49s
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10.Linear Regression Using a Single Neuron3m 15s
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11.Training a Neural Network6m 45s
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12.Gradient Descent Optimization7m 40s
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13.Exercise: Introduction to Linear Regression5m 1s
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