Unsupervised learning is the type of machine learning algorithm that uses unlabeled data. That is the data used does not have predefined labels like name, gender etc. This type of algorithm is used primarily when the goal is to find patterns and relationships in data without prior knowledge of the expected outcomes. In unsupervised learning the algorithm is given a set of inputs and the task is to find patterns and structures in the data. This typically is done by using clustering or dimensionality reduction techniques. Unlike supervised learning, this algorithm does not require the supervision of a human to supervise the models. The model learns by itself by testing out different data and finding patterns all by itself. The algorithm's learning goal is to identify patterns that are in the data set and categorize them based on some feature. For example, take a cat, its prominent features would include the color, whiskers, long tail, and retractable claws. With the help of these features, the model will learn that the input given might be a cat once it compares them. This is how this algorithm works. Many methods come under unsupervised learning like clustering, K means clustering, principal component analysis etc. Clustering Clustering is the process of grouping data points together based on their similarities. The algorithm examines the data and groups similar data points together. The goal is to identify groups of data points that share common characteristics or behavior. There are several clustering algorithms, including k-means clustering, hierarchical clustering, and density-based clustering. K-Means clustering This is an iterative process where each data point is assigned to groups and the data points are slowly clustered based on similar characteristics. The goal is to minimize the sum of the distances between the data points and the cluster center to identify the correct group to which each data point should belong. PCA (Principal Component Analysis) Principal component analysis, or PCA, is a dimensionality reduction technique often used to reduce the dimensionality of large data sets by reducing a large set of variables to a smaller one that still contains most of the information in the large set. Among other applications, the primary applications include anomaly detection, customer segmentation, recommendation systems, and image and video processing. Anomaly detection algorithms are used to identify points that are completely different from the rest of the points. Useful in fraudulent transactions. Customer segregation based on behavior and preference can also come in handy for giants like Flipkart and Myntra. Recommendation systems use clustering techniques to identify similar items and make recommendations based on them. These are widely used by Ott platforms. Image and Processing are used to identify features in images and videos. This type of machine learning model can be used in the identification of trends and discovering hidden insights in the data. It can be used to identify patterns and relations in the data that may not be immediately apparent. Although the downside is that it may possess to be a more challenging technique because there are no pre-existing correct outputs of a given particular input data to compare the output of the algorithm to. To know about reinforcement learning click on the link mentioned below.
Published on: May 14, 2023
#reinforcementlearning, #agent, #rewards, #punishments, #feedback, #qlearning, #deepreinforcementlearning, #application
#unsupervisedlearning, #unlabeleddata, #insights, #patterns, #applications, #predictions
Published: May 14, 2023
Author: Dipti Vatsa
Register now and learn with best in class trainers, a group of like minded learners, supported by trained professionals. Get placement assistance, interview preparation, information about industrial trends and much more along with quality learning.
It is high time for learning
We have taken all the steps to ensure smooth and quality learning, read what our clients have to say about us.
The classes attended were very much informative. The classes which I attended was on Machine Learning using Python. I learnt a lot there about the algorithms and various topics of the Machine Learning. The practical classes were very much interesting. Needed a more practical classes for each topics, which will help to understand at the level of best.
We learnt the concepts well and how to implement the algorithms in simple way.i like your teaching because you clearly explain the program line by line,so it was easy for us to understand the function of each line.
Classes were good, we need more explanation based on those specific topics or algorithms. Definitely , cultskills will make strong base for concepts (domain) as well as for your career. Thanks a lot!
Explaining the concepts in the way that everyone understands. Trainers are capable enough to make the students and the industry people get the concepts that they say.
It was really a great experience of learning with cult skills. Completed the Curriculam as of industrial standards and the interactive sessions allowed me to grasp the topic to their depths. Got to explore lot of new things from this course that will help me in my future ventures with ML and Web Development.
I've learnt a lot about the Python after joining the course provided to us. Our trainer is very informative, helpful and is having a positive approach towards problem solving. Really gained a vast knowledge about the course.
I have completed deep learning course from cult skills. Very interactive sessions, experienced staff and quality classes delivered by good trainers. Curriculum was covered along with industrial experience.
Nice experience. Detailed and interactive courses. Assignments are provided after each lecture. Doubt clearance session. Perfect approach for students who want to learn and explore new topics.