Deep Learning Prerequisites Linear Regression in Python

This course will teach you one of the most popular techniques used. in Machine learning, data science, and statistics: Linear regression. File Size: 942MB

Deep Learning Prerequisites Linear Regression in Python

This course will teach you one of the most popular techniques used. in Linear regression is a method of machine learning, data science, and statistics. We will cover all aspects of the theory, including the formulation of the solution and the application to real-world issues. We will show you how to code your own linear regression module in Python.

Linear The simplest model of machine learning, regression, is easy to learn. But it has so many depths that you’ll keep coming back to this model for years. If you are interested, it is a great course to start with. in Your first steps in The fields of

Deep Learning

Machine learning

data science

Statistics

In this section I will demonstrate how 1-D linear regression can be used to prove Moore’s Law true.

What is that? Moore’s Law does not follow a linear pattern

You are correct. You are correct!

In the next section we will make 1-D linear regression more general and any-dimensional linear regression more specific. in Also, you will need to know how to make a machine learning model which can learn from multiple inputs.

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Multi-dimensional linear regression will be used to predict a patient’s systolic pressure based on their weight and age.

We’ll also be discussing practical machine-learning issues when analysing data, including generalization, overfitting, and train-test splittings.

No external materials are required for this course. Everything you need (Python?, and some Python Libraries) can be obtained free of charge.

This course is designed for programmers who want to improve their programming skills by learning data science. You have technical or mathematical backgrounds and want to learn how to become a software engineer. “hacker”This course could be helpful.

This course focuses upon “how to build and understand”Not just “how to use”. An API can be used by anyone. in 15 minutes after reading some documentation. It’s not all about. “remembering facts”It’s about “seeing for yourself” via experimentation. It will show you how to visualize what’s going on in Internally, the model. This course is designed for those who want to learn more about machine learning models.

“If you can’t implement it, you don’t understand it”

Or, Richard Feynman, a great physicist said: “What I cannot create, I do not understand”.

My courses are unique in that you learn how to implement machine-learning algorithms from scratch.

You can also learn how to plug in with other courses in You have put your data in a library. But do you really need 3 lines of code to help?

After repeating the process with 10 datasets you realize that you didn’t learn any new things. It took you 1 thing to learn, and then you just repeated the exact 3 lines of code 10 other times.

Suggested Prerequisites:

calculus (taking derivatives)

Arithmetic in matrix

Probability

Python coding: if/else, loops, lists, dicts, sets

Numpy Coding: Matrix and Vector Operations, Loading a CSV File

I WOULD LIKE TO TAKE MY COURSES IN THE RIGHT ORDER.:

You can check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in You can find the FAQ of all my courses, including the Numpy course.

Who is this course for?

People who are curious in Machine learning, data science, statistics, and artificial Intelligence
Data science novices who want an easy introduction
Data science is a field that will help people who want to progress in their careers.
Programmers who are self-taught and want to learn more about computer science theory
Analytics professionals who are interested in learning the theoretical basis of one of the most popular algorithms in statistics

 Here’s what you’ll get in Deep Learning Prerequisites Linear Regression in Python