Machine Learning

Question 1

Machine learning

Answer 1

Machine learning enables systems to learn and improve from experience without being explicitly programmed to come to any specific conclusions

Question 2

Machine learning

Answer 2

Machine learning enables systems to learn and improve from experience without being explicitly programmed to come to any specific conclusions

Question 3

Algorithm

Answer 3

Algorithms are set of rules that machine follows to process data and to make predictions.

Question 4

Training data

Answer 4

Basically, it’s the information we use to teach the systems

Question 5

Model

Answer 5

Models, or the output of the machine learning process that can be applied to new data.

Question 6

Types of Machine learning

Answer 6

Supervised Learning and Unsupervised Learning.

Question 7

Supervised learning

Answer 7

In supervised learning, we train a model on labeled data, where both the input and correct output are provided. This helps the model learn the relationship between them. For example, predicting house prices based on features like size, location, and number of rooms.

Question 8

Unsupervised learning

Answer 8

unsupervised learning works with unlabeled data. It focuses on identifying Hidden Patterns or structures without explicit instructions on what the optimal output should be. For instance, clustering customers into different segments based on purchasing behavior.

Question 9

Bias in machine learning

Answer 9

Bias in machine learning and AI refers to the presence of systematic errors that can lead to unfair or discriminatory outcomes.

Bias can arise from various sources, such as biased training data, flawed algorithms, or skewed assumptions made during the model development process.