SEM-Machine-Learning Glossary

To access Machine Learning and apply its techniques to Search Engine Marketing it’s helpful to know about a couple of basic terms you’ll cross quite often. All of these terms are commonly used on these pages and in Machine Learning in general.

Term	Explanation
Algorithm	Process of defined (mathematical) steps to solve a certain problem
Artificial Intelligence	The process of building inteligent systems that are able to mirror human behaviour
Attributes	Each particular observation your datasets includes in its columns
Classification	Routine of categorizing values into different classes and groups of data. Cam be used for audience- or keyword-classification a.o.
Click Through Rate	The number of websiteclicks divided by the number of Impressions
Clustering	Technique of grouping data into different categories based on similarities and observations. k-Means is a widely used algorithm for this problem
Confusion matrix	Tool to visualize and describe the results and performance of a classification model in Machine Learning
Conversions	The defined and measured action a user makes on your website based on your KPIs
Correlation	Statistical metric that provides the size of fluctation betweentwo or more variables
csv	Comma seperated value files are a file format to store data in tables
Data cleaning	Process of removing and replacing wrong data, fixing missing data, detecting outliers to prepare your csv file for Machine Learning
Data collection	Procedure of gaining relevant data from all kind of sources like website-tracking, CRM or 3rd-party providers
Data science	The wonderful discipline of gaining knowledge and insights from data
Dataset	Collection of datapoints build from rows and columns including variables and features
Decision Tree	Classification algorithm within Supervised Learning that predicts new values based learned rules and decisions and representing the output in tree-form
Deep learning	One of several AI applications. Deep Learning imitates the human brain functions while performing a certain task repeatedly and learning from huge datasets
Descreptive statistics	Figures thate are used to describe data and put them together in columns and rows
Feature	The data-input provided to a Machine Learning Model in Supervised Learning
Feature selection	Technique to select the most valuable features for your purpose from your columns and get rid of the irrelevant ones
Histogram	Visualization tool that displays data in groups and logical ranges
Impressions	Number of times your search ad was shown to the user
Input	The labeled information (x) you provide to your machine in supervised learning
K-means	Clustering Algorithm within Unsupervised Learning Models that calculates the distance between entities to group data in clusters. Often used for market- or audience segmentation.
K-Nearest Neighbours	KNN is a classifification algorithm within Supervised Learning which uses the datapoints close to each other as a reference point to group and cluster raw data
Kaggle	Online Community of data scientists, well known for it’s Machine Learning competitions
Lable	The data-output provided to a Machine Learning Model in Supervised Learning
Linear Regression	Regression algorithm within Supervised Learning which uses labeled data to predict new values
Logistic Regression	Classification algorithm within Supervised Learning which uses the dependency of one ore more variables to predict an outcome
Machine Learning	Subset of Artificial Intelligence that works with huge amount of data to predict new values, find patterns and similiarities e.g. without explicity beeing programmed
Matplotlip	Machine Learning library for data visualization through charts, scatterplots and histograms
Mean	Statistical distribution describing the numerical average
Median	Statistical distribution describing the value in the middle of a group of numbers
Model	Depiction of computation operations that processes provided data through an algorithm
Model Accuracy	The metric that defines the rate of correctnes and quality of a model based on a test with provided data
Naive bayes	Classification algorithm within Supervised Learning that uses the independency of one ore more variables to predict an outcome
Numpy	Machine Learning library to transform data into vectors, matrices, arrays and functions
Output	The labeled information (y) you provide to your machine in supervised learning. It’s the result-information we already know but need to combine with (x) to find outliers and errors
Overfitting	Describes the “too good” performance of a model referring to its target function
Pandas	Machine Learning library with csv-reading function for data import and data-cleaning functions
Probability	Field in the mathematical discipline of statistics that quantifies chance of offurance refering to certain events. Commonly used or click-prediction in ad-tech
Python	The most popular and mainly used programming language in Machine Learning and Data Science
Pytorch	Machine Learning framework written in Lua language, which is based on imperative programming
R	Commonly used programming language in Machine Learning for statistical purposes
Random Forest	Classification algorithm within Supervised Learning that uses a group of Decision Trees to predict new values
Reinforcement Learning	Type of Machine Learning where the model follows a trial and error approach and learns from exploration and mistakes. It’s the backbone of Real Time Bidding in SEM.
scikit learn	Machine Learning framework written in Python that provides several algorithms for supervised and unsupervised learning
Segmentation	Clustering datapoints into different groups based on similiarities and patterns
Standard Deviation	Numerical value in statistics that describes how the numbers of a group differs from the mean
Statistical Fit	Numerical value that displays the accuracy of your approximation referring to your target
Statistics	Mathematical discipline dealing with analyzing and interpreation of numerical data
Stochastic Gradient Descent	Mathematical function that can be used for error fixing to adjust and optimize parameters within a dataset. Commonly used as learning algorithm in very large datasets
Supervised Learning	In Supervised Learning you train an algorithm by providing labeled data-input by yourself
Support Vector Machines	Classification algorithm within Supervised Learning that splits a datasets into pre-defined categories
Tensorflow	Machine Learning framework with capacities to build Deep Learning models and Neural Networks
Tracking	The process of gaining data from interactions users have with your website
Training	The process of providing input-data to your model to improve accuracy
Underfitting	Describes the poor performance of a model referring to its target function
Unsupervised Learning	Unsupervised Learning works without labeled data and training. Algorithms in USL cluster and group dataset based on similarities, patterns and associations.