Unsupervised Machine Learning Ml And How It Works
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The document provides an overview of unsupervised machine learning, explaining what it is, how it works, and its various types such as clustering and anomaly detection. It outlines the process of providing uncategorized input data to algorithms and observing patterns, while also discussing the disadvantages like lack of specific definitions and lower accuracy. Additionally, it mentions various applications including image recognition and bioinformatics.
Unsupervised Machine Learning Ml And How It Works
- 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.