Deep Learning Prerequisites: Linear Regression 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
- How to take a derivative using calculus
- Basic Python programming
- For the advanced section of the course, you will need to know probability
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:
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...
calculus (taking derivatives)
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)
Who this course is for:
- People 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
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.