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For example:
15 3
A = k3
=
2 47
1 3 53 33 × 3 15 9
×
×
=
kA 3A = ⇒
×
×
×
2 3 4 3 7 3 6 12 21
Applications of Matrices in AI
Matrices are used throughout the field of machine learning for computing:
• Sales Forecasting and Price Prediction: Matrices are used to represent relevant predictor and response variables.
• Image Processing: Digital images can be represented using matrices. In the following figure, each box appears
transparent or coloured. It is represented in the corresponding matrix with either a “1” or a “0”. A zero means the box
contains nothing and is white, while a one indicates that the box is filled (in this case it is filled with black).
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0
0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0
0 1 1 0 1 1 0 0 0 0 0 1 1 0 1 1 0
0 1 1 0 0 1 1 0 0 0 1 1 0 0 1 1 0
0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0
0 1 1 0 0 0 0 1 1 1 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
• Recommender Systems: They use matrices to relate between users' purchased and users' viewed item(s).
• Natural Language Processing: In NLP, vectors represent the distribution of a particular word in a document. Vectors
are one-dimensional matrices.
Data Preprocessing
Data preprocessing is an essential phase in the machine learning process that prepares datasets for effective machine
learning applications. It includes multiple processes to clean, transform, reduce, integrate, and normalise data:
Data Data Data Reduction Data Integration Feature
Cleaning Transformation & Normalisation Selection
1. Data Cleaning: Businesses have abundance of data. However, not all of it is accurate
or organised. When it comes to machine learning, if data is not sufficiently cleaned, the
accuracy of your model is at risk. The following steps are taken to clean/prepare the
data:
• Missing Data: Missing data refers to the absence of certain values in the dataset,
which can result from various causes. To handle missing data, strategies include
removing rows or columns with missing values, imputing missing values with
estimates, or utilising algorithms that can manage missing data.
Data Literacy—Data Collection to Data Analysis 305

