Find the best Machine Learning frameworks & tools for Search Engine Marketing

Published by Patrick Mebus on

Good news for you, if you’re just about to start your Machine Learning project in Search Engine Marketing: It’s not necessary to write every single line of code by yourself. You can save a lot of time and effort, if you use framework-environments and algorithm-libraries – powerful tools that will make your ML-access much easier.

What is a Machine Learning Framework?

A  Machine Learning framework is a collection of  several libraries, written in a certain language, that includes functions dedicated to train algorithms for specific tasks and operations.

In other words: a Machine Learning framework is a powerful precoded-function-toolkit that can save you a lot of time. You don’t have to write every piece you code by yourself. Instead you can just call a function from a certain frame-work-library.

Unfortunately the framework ‚marketplace’ is a mess. There are so many options, possibilities and overlaps, that it’s hard to keep the overview and even more important to keep in mind what the goal is you want to achieve.

In this article we’re going to introduce some of the of the most commonly used frameworks for Machine Learning and recommend the things you really need for Search Engine Marketing.

1. Core Libraries for Machine Learning

Machine Learning is based on data and math to execute your operations. Thanks god, you don’t have to calculate every task by yourself. For fast execution of algorithms you can use a package of fundamental Open Source Libraries for Python. I recommend to install the core-libraries in the table below to have a basic stack and strong engine for your Machine Learning operations.

Machine Learning Library Key-Features
Pandas csv-reading function for Search engine-data import; data cleaning functions
NumPy Transforms your data into vectors, matrices, arrays and functions
SciPy Differential equation and gradient optimization
Matplotlib Visualization through charts, scatterplots and histograms for better understanding or presentations

2. scikit-Learn framework

The first popular framework we’re going to introduce is scikit-Learn. scikit-Learn, released in 2007, is a great solution for basic Machine Learning algorithms. Nothing more or less. In my opinion it gets a little too less attention in the last couple of months. Maybe because the goldrush in Machine Learning takes place in Deep Learning and Deep Neural Networks with strong GPU support at the moment – something scikit-Learn does not support yet.

There are plenty of  things that come along with this framework, which indicates for scikit-Learn as our framework of choice for Search Engine Marketing and the reasons that let me use scikit-Learn for the ML-examples on this website

scikit-Learn advantages

  • It’s written in Python (easy to learn)
  • It has a high-level-syntax (good for orientation – say more with less lines of code)
  • scikit-Learn includes all the relevant algorithms we need for predictive analytics and segmentation in SEM like Linear Regression or Decision Trees
  • There is a great documentation refering to problems in classic Machine Learning and a large community


3. Tensorflow framework

Tensorflow, written in Python-language, was released by the Google Brain team in 2015 and quickly became one of the most popular open source frameworks in Machine Learning and especially in Deep Learning.

In my opinion the biggest advantages of Tensorflow are the alround-design, flexibility and visual-aspect. Beside regression- and classification-algorithms Tensorflow is your framework, if you’re going to run operations in Deep learning and Neural Networks. No surprise, that companies like Airbnb, Uber and Twitter rely on Tensorflow.

Tensorflow’s TensorBoard provides great usability

What makes Tensorflow very usable for ML-beginners is a feature called „Tensorboard“, a data-suite to visualize and debug your results. That makes it much easier to understand your algorithms results. You can run Tensorflow operations either via CPU or GPU, if you have larger tasks and operations to accomplish.


But there’s a flipside too: Tensorflow runs on a static computation graph and comes along with symbolic differentiation. What does that mean? Bascically the result is, that you have to write more (complicated looking) code to say less.

Even Tensorflow is an amazing and rapidly fast growing solution, for our SEM-operations it might be some kind of an overkill. Since we’re trying to find the easiest and most uncomplicated way to add Machine Learning as an application to our Search Engine Advertising.

4. PyTorch framework

And then we have PyTorch. PyTorch is a further technical Python-development of Torch, that was written in Lua
language by Facebooks research team in 2016.

There are two great PyTorch key-features we should mention here. In contrast to Tensforflow Pytorch framework is based on imperative programming. The code you use is the code that actually gets executed. Furthermore PyTorchs Dynamic Computatuion Graph can be a life saver when it somes to debugging.

While Tensorflow is more commonly used for production and prototyping, PyTorch is considered to be as a solution for research.

5. Recommended Resources

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Patrick Mebus

I’m a Digital Marketer with deep passion for Search Engines, Automation and AI. I’m here to make Machine Learning more feasible for Search Engine Marketers.

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