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Machine learning: driving data science forward

Writer's picture: Ansiya NasarAnsiya Nasar

Updated: Nov 15, 2024


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Machine learning is like teaching a computer to learn from its mistakes. With every new information, it gets better and better at solving problems and making decisions.


  • Some Applications of machine learning in Data Science are:


Predictive Analytics: Helping us to predict what's next by analyzing past data.

Fraud detection: Detect unusual patterns or suspicious activity to safeguard our security.

Anomaly Detection: Detect hidden trends and abnormalities.

Healthcare and medical: Helps in assisting diagnosis, treatment, and saving lives with personalized care.


  • Two types of Machine Learning:


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  1. Supervised Learning


Machine Learning Training in Kochi, Kerala - Advanced Course in Cochin, Ernakulam

Supervised Learning


Supervised learning is like mentoring a computer, as shown in the above figure. We provide labeled data, showing right and wrong answers. This helps the computer recognize patterns and relationships, making accurate predictions and decisions.


For example, if you are predicting the price of a house, the input could be the size and location, and the output would be the price.



A. Regression Model: If we want to predict numbers, we can use this model. The output would be a continuous number.

Example: To predict price or sales.


B. Classification Model: If we want to sort things into different groups or categories, we can use this model. The output we get is a label that tells us to which group something belongs.

Example: Classify patients having a disease or not based on symptoms, test results, etc.



2. Unsupervised Learning


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Unsupervised Learning


Unsupervised learning is like letting a curious computer explore data on its own, just like in the above figure. It uncovers hidden connections and groups similar information together.


A. Clustering Model: Clustering is an unsupervised machine learning technique used to group similar data points. Imagine having a messy closet full of customer data. Clustering helps make it into neat categories.

Example: Identifying similar buying habits


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