Unsupervised Machine Learning ML and how it works is for the mid level managers to give information about what is unsupervised machine learning, types of unsupervised learning, and its disadvantages. You can also know how unsupervised machine learning works to understand supervised machine learning in a better way for business growth. https://bit.ly/3fTQ7iI

1. Unsupervised
Machine Learning
ML and how it works
Your Company Name

2. 2
Unsupervised Machine Learning
o What is Unsupervised Learning?
o How Unsupervised Machine Learning works
o Types of Unsupervised Learning
o Disadvantages of Unsupervised Learning
01

3. What is Unsupervised Learning?
3
Unsupervised Learning
Input Raw Data OutputAlgorithm
Interpretation Processing
o Unknown output
o No Training Data Set
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

4. How Unsupervised Machine Learning works
4
Step 1
Provide the machine learning algorithm uncategorized,
unlabeled input data to see what patterns it finds
Step 2
Observe and learn from the patterns the
machine identifies
Machine Machine
Similar Group 1
Similar Group 2
Types of Problems to Which it’s Suited
Clustering
Identifying similarities in groups
For Example: Are there patterns in the data to
indicate certain patients will respond better to
this treatment than others?
Anomaly Detection
Identifying abnormalities in data
For Example: Is a hacker intruding in our
network?
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

5. Types of Unsupervised Learning
5
Dimensionality
Reduction
o Text Mining
o Face Recognition
o Big Data Visualization
o Image Recognition
Clustering
o Biology
o City Planning
o Targeted Marketing
Unsupervised
Learning
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.

6. Disadvantages of Unsupervised Learning
6
You cannot get very specific about the definition of the
info sorting and therefore the output. This is
often because the info utilized in unsupervised learning is
labeled and not known. It's employment of the machine to
label and group the data before determining the hidden
patterns.
Less accuracy of the results. This is often also because
the input file isn't known and not labeled by
people beforehand , which suggests that the machine will got
to do that alone.
The results of the analysis can't be ascertained. there's no
prior knowledge within the unsupervised method of machine
learning. Additionally, the numbers of classes also are not
known. It results in the lack to determine the results
generated by the analysis.
This slide is 100% editable. Adapt it to your needs and capture your audience's attention.