ML-UNIT-4
Welcome to letslearningcse.blogspot.com,
Note: This material was prepared based on the JNTU R20 syllabus for AI and ML.
Now, I am going to give you a brief introduction to ML Unit 4 [Unsupervised Learning].
Let's get into the topic!! As we all know, machine learning is basically classified into three types. Please take a look at the flow chart below.
Now, I hope you get a little bit of clarity about the concept of what we are going to discuss.
What is unsupervised learning?
Unsupervised learning is a type of machine learning technique that enables a model to be trained on unlabeled data and predict patterns based on the trained data without any human interference.
Ex: recommendation systems, sentiment analysis, search engines
It has two types of techniques:
- clustering
- association
Association:
It is a type of unsupervised learning that is used to find out the dependency of one data item on another data item, which can lead to building a recommendation system and sentiment analysis.
clustering:
Clustering is nothing but the grouping of similar objects into a single cluster.
It is mainly used for statistical data analysis.
It has two types of clustering scenarios:
- Soft clustering: In soft clustering, data points can't belong to only one cluster. We can't make a single cluster.
- Hard clustering: in hard clustering, data points can belong to only one cluster. It can make a single cluster of items.
Ex: k-means, dbscan, agglomerative, hierarchical clustering, etc.
Clustering Methods:
- Partition clustering or centroid clustering
- Density-based clustering
- Distribution-based clustering
- Hierarchical clustering
Applications of clustering:
- Customer segmentation
- data analysis
- Dimensionality Reduction
- Feature Engineering
- outlier detection
- semi-supervised learning
- search engines
- image segmentation
Image segmentation:
It is a process that splits the picture or image into numerous regions that belong to the same label. It splits the image, pixel by pixel.
It has four types of image segmentation techniques:
- Color Segmentation
- Instance Segmentation
- Semantic Segmentation
- Panoptic Segmentation
Color segmentation is a type of image segmentation technique. In this method, pixels having the same color are assigned to the same segment or class label.
Instance segmentation is a type of image segmentation technique. In this method, even objects belonging to the same category can be assigned different class labels.
Semantic segmentation is a type of image segmentation technique. This method was quite opposite to instance segmentation. In this method, objects belonging to the same category are assigned the same class label.
Panoptic segmentation is a type of image segmentation technique. This method is a combination of instance segmentation and semantic segmentation. Instance segmentation can be used for images that have countable classes, whereas semantic segmentation can be used for uncountable objects.
image can be identified by:
- height of the image
- width of the image
- number of channels
gaussian mixture:
It is used to organize the data by checking its similarities and differences between them.
dimensionality reduction:
It is used to reduce the variables in the training portion and helps the core essence of machine learning models.
What are you going to see in this material?
- about clustering algorithms
- K-means clustering algorithm
- DBSCAN clustering algorithm
- image segmentation
- gaussian mixture
- dimensionality reduction
- types of PCA
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