AI & ML
Artificial Intelligence refers to the development of programs that can help computers to perform task that requires human like intelligence.
Machine learning is a subset of AI that involves training data to give predictions based on that data for specific tasks using statistical techniques.
Artificial intelligence includes features like Natural Language Processing and Computer vision that can be used in applications of Machine Translation and Autonomous Vehicles.
Machine learning includes Supervised and Unsupervised learning and Recommendation Systems that can be used in development of applications like Predicting Sales for a store, Predicting stock prices, Email Spam Filtering, Movie recommendation.
Features of AI:
- Data Management and Preprocessing.
- Model Development and Training.
- Model Evaluation and Validation.
- Deployment and Serving.
- Monitoring and Management.
- Explainability and Interpretability.
- Security and Compliance.
- Collaboration and Workflow.
- Interoperability and Integration Cost Management
- Eliminate dull and boring tasks
- Data ingestion
- Imitates human cognition.
- Futuristic.
- Prevent natural disasters. Prevent natural disasters
- Facial Recognition and Chatbots.
In recent years, machine learning has emerged as a paramount technological innovation, exerting a profound influence on a wide array of industries and applications. This text highlights the primary characteristics:
Predictive modeling involves utilizing data as input for machine learning algorithms in order to construct models that can anticipate future events. These models have a variety of applications, such as assessing the probability of a loan default or predicting the likelihood of consumer purchases, among other uses.
Machine learning algorithms are responsible for automating the discovery of patterns in data, reducing the need for human intervention and allowing for more accurate and efficient analysis.
Machine learning techniques have inherent capabilities to effectively process huge volumes of data, aptly aligning with the demands of scalability inherent in big data processing. Businesses now have the ability to make informed decisions using the information obtained from such data.
The generalization capability of machine learning algorithms enables the discovery of extensive patterns in data, which can be utilized to analyze new and unexplored data. Although the data used for training the model may not have immediate relevance to the current task, it is still valuable for predicting future events.
Machine learning algorithms are designed to continuously learn and adjust as new data becomes accessible. They demonstrate adaptiveness in their ability to adapt and improve with the arrival of fresh information. Over time, their performance can be improved, making them more precise and efficient as additional data becomes accessible.