Linear Regression Model with Many Features - Real Life Example I am learning Machine Learning (Linear Regression) from Prof. Andrew Ng's lecture. While listening when to use normal equation vs gradient descent, he says when our features number is very high (like $10^6$) then to use gradient descent. 
Everything is clear to me, but I wonder that can someone give me real life examples where we use such such huge number of features?

 A: Natural language processing come to mind. For instance, you might predict the amount of money someone spends on your website by their review. The review is text, encoded by an n-gram model. The ith, jth element of your training matrix is the ith customer and counts of jth n-gram. An n-gram is a string of contiguous words like, "the product was excellent."
Typically n-gram data is encoded in a sparse matrix. There are several data-structures suitable. One of the easiest to explain is a coordinate list. A coordinate list is a list of tuples [(i, j, value)]. Since the matrix is mostly zero, this is much more efficient than allocating a dense array.
A: Another example - image recognition. Imagine that you have just a 512 x 512 gray-scale image - it means that without additional pre-processing you already have $2^{18}$ features - with each pixel being a feature.
It's not necessarily a good example for Linear Regression, but Gradient Descent is used in many ML algorithms.
A: Genetic analysis is another common example. Most mammals have genomes with a number of protein-coding genes in the order of $10^4$, but there's lots of other coding regions (for different kinds of RNA that are not used for protein synthesis directly), too. Additionally, some experimental/methodological procedures bases on sequencing slice the genome into millions ($10^6$) of bins and measure certain molecular features for these bins (like DNA methylation or CpG-islands).
