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Deep Learning Prerequisites: Linear Regression in Python

 Deep Learning Prerequisites: Linear Regression in Python

 Deep Learning Prerequisites: Linear Regression in Python  / get udemy coupon code


This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

What you'll learn
  • Derive and solve a linear regression model, and apply it appropriately to data science problems
  • Program your own version of a linear regression model in Python
 Description

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:
  •     deep learning
  •     machine learning
  •     data science
  •     statistics
In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

What's that you say? Moore's Law is not linear?

You are correct! I will show you how linear regression can still be applied.

In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.

We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.

Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.
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