Deep Learning Prerequisites: Linear Regression in Python
- 4.6
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Brief Introduction
Data science, machine learning, and artificial intelligence in Python for students and professionalsDescription
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.
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
calculus (taking derivatives)
matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Requirements
- Requirements
- How to take a derivative using calculus
- Basic Python programming
- For the advanced section of the course, you will need to know probability
Lazy Programmer Inc.
4.6 Raiting
Data Science: Deep Learning and Neural Networks in Python
- 4.6
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The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow All the materials for this course are FREE....
Unsupervised Deep Learning in Python
- 4.5
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Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA This course is the next logical step in my deep learning, data science, and machine learning series....
Fundamentals of Machine Learning with Python Implementation.
- 3.5
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Learn Fundamentals of Machine Learning from scratch to make students well equipped with all basics and math involved Hello there! Welcome to Fundamentals of Machine Learning with Python Implementation....
PyTorch Basics for Machine Learning
- 0.0
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This course is the first part in a two part course and will teach you the fundamentals of PyTorch. Enroll to learn more, complete the course and claim your badge!...
