Answer / Ram Bhardwaj

Supervised learning involves training a model using labeled data, meaning the input data has predefined outputs or answers. The goal is to learn a mapping function between inputs and outputs so that the model can predict the output for new, unseen data. On the other hand, unsupervised learning works with unlabeled data where the model identifies patterns and structures in the data without any predefined labels.

List down various approaches for machine learning?

1 Answers  

---

Why machine learning is popular now?

1 Answers  

---

Define kernel svm?

1 Answers  

---

Explain what is a recommendation system?

1 Answers  

---

How do you ensure you're not overfitting with a model?

1 Answers  

---

What is the bias in machine learning?

1 Answers  

---

Explain the difference between L1 and L2 regularization?

1 Answers  

---

Explain the Algorithm Technique of Reinforcement Learning in Machine Learning?

1 Answers  

---

Why does overfitting happen?

1 Answers  

---

What are the two classification methods that SVM ( Support Vector Machine) can handle?

1 Answers  

---

What is naive bayes classifier?

1 Answers  

---

Do you know what's the “kernel trick” and how is it useful?

1 Answers  

---