## Deep Learning Prerequisites: Linear Regression in Python Course

### Data science: Learn linear regression from scratch and build your working program in Python for data analysis.

#### What you’ll learn

Deep Learning Prerequisites: Linear Regression in Python Course

- Derive and solve a linear regression model, and apply it appropriately to data science problems
- Program your version of a linear regression model in Python

#### Requirements

- How to take a derivative using calculus
- Basic Python programming
- For the advanced section of the course, you will need to know the probability

#### 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 linear regression module in Python.

- 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 predict 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.

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

#### TIPS (for getting through the course):

- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

#### Who this course is for:

- Who are interested in data science, machine learning, statistics, and artificial intelligence
- People new to data science who would like an easy introduction to the topic
- People who wish to advance their career by getting into one of technology’s trending fields, data science
- Self-taught programmers who want to improve their computer science theoretical skills
- Analytics experts who want to learn the theoretical basis behind one of statistics’ most-used algorithms
- Python For Beginners – Learn Python Completely From Scratch Course
- Last updated 3/2020

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