# Category Archives: Machine Learning

machine-learning

## Principal Component Analysis (PCA) – Part 4 – Python ML – OOP Basics

Goal of this post:

1. Add principal component analysis (PCA)
2. Refactor using inheritance
3. Convert gradient descent to stochastic gradient descent
4. Add new tests via `pytest`

What we are leaving for the next post:

1. Discussing the need for packaging
2. Start creating an actual package
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## Multivariate Linear Regression – Part 3 – Refactoring – Python ML – OOP Basics

Goal of this post:

1. Move beyond single linear regression into multiple linear regression by utilizing gradient descent
2. Refactor using inheritance
3. Reconfigure our `pytest` to include the general case

What we are leaving for the next post:

1. Add principal component analysis
2. Refactor using inheritance
3. Add new tests via `pytest`
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## Single Linear Regression – Part 2 – Testing – Python ML – OOP Basics

We have now entered part 2 of our series on object oriented programming in Python for machine learning. If you have not already done so, you may want to check out the previous post –> Part 1.

Goal of this post:

1. Fit a model to find coefficients
2. Find the RMSE, R^2, slope and intercept of the model
3. Test our model using `pytest`

What we are leaving for the next post:

1. Refactoring and utilizing inheritance
2. Utilizing gradient descent
3. Updating and adding tests
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## Single Linear Regression – Part 1 – Python ML – OOP Basics

Data scientists who come to the career without a software background (myself included) tend to use a procedural style of programming rather than taking an object oriented approach. Changing styles is a paradigm shift and really takes some time to wrap your mind around. Many of us who have been doing this for years still have trouble envisioning how objects can improve things. There are a lot of resources out there to help you understand this subject in more detail but I am going to take a “learn by doing” approach. The code used for this can be found on my GitHub.

Goal of this post:

1. Build a very basic object to house our linear regression model
2. Create a command line interface (CLI) to pass in different datasets
3. Print the object to the screen in a user-friendly format

What we are leaving for the next post:

1. Fitting a model to find coefficients
2. Finding the RMSE, R^2, slope and intercept of the model
3. Testing our model using pytest

Here we go!

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## Using the Google Vision API in R

### Utilizing RoogleVision

After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection in images. As it turns out, Google has done a phenomenal job with their Vision API. It’s absolutely incredible the amount of information it can spit back to you by simply sending it a picture.

Also, it’s 100% free! I believe that includes 1000 images per month. Amazing!

In this post I’m going to walk you through the absolute basics of accessing the power of the Google Vision API using the RoogleVision package in R. Continue reading