“Linear algebra is a pillar of machine learning.” — Jason
Check THIS LINK for reading book…
In general, any machine learning problem can be assigned to one of two broad classifications:Supervised learning and Unsupervised learning.
Tom Mitchell provides a more modern definition: “A…
Eigen-stuffs
Eigenvectors
For a linear transformation, an eigenvector is a vector which, after applying the transformation, stays in the same span.
linear transformation
eigenvector
When we say eigenvectors, we always need to say eigenvectors of a linear transformation.It's the same with…
eigenvectors
eigenvectors of a linear transformation
Intro to Statistical Learning
Prediction
Inference
inference
Free Hardwares for Deep Learning
cat /etc/os-release
df -h
cat /proc/cpuinfo
cat /proc/meminfo
Supervised learning
feeding algorithm data with right answers, so it can predict more accurately.
Book note: ISL chapter 3 Linear Regression
SLR
which is to examine how ONE variable effect the result.
Linear Regression: Assume there is linear relationship between x and y. so that’s to be:
One-hot Encoding
Refer to Quora: What is one-hot encoding and when is it used in data science?Refer to youtube: A demo of One Hot Encoding (TensorFlow Tip of the Week)
“Encoding” is to take a number to represent a categorical value.
Build Simple Model for ML
[Refer to Kaggle: Your First Machine Learning Model](https://www.kaggle.com/dansbecker/your-first-machine-learning-model)
Refer to: Linear Regression Tutorial Using Gradient Descent for Machine Learning
Gradient Descent is the process of minimizing a function by following the gradients of the cost function.
Most libraries (including scikit-learn) will give you an error if you try to build a model using data with missing values.
Refer to Kaggle: Handling Missing Values
