92.3% Off Deep Learning Prerequisites: Linear Regression in Python
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
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.
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: linear_regression_class
Make sure you always "git pull" so you have the latest version!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
- linear algebra
- 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.
USEFUL COURSE ORDERING:
- (The Numpy Stack in Python)
- Linear Regression in Python
- Logistic Regression in Python
- (Supervised Machine Learning in Python)
- (Bayesian Machine Learning in Python: A/B Testing)
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- (Supervised Machine Learning in Python 2: Ensemble Methods)
- Convolutional Neural Networks in Python
- (Easy NLP)
- (Cluster Analysis and Unsupervised Machine Learning)
- Unsupervised Deep Learning
- (Hidden Markov Models)
- Recurrent Neural Networks in Python
- Artificial Intelligence: Reinforcement Learning in Python
- Natural Language Processing with Deep Learning in Python
Who is the target audience?
- 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