Answer / Ashwini Kumar Singh

Missing data in a dataset can be handled using techniques such as imputation (replacing missing values with estimated ones), deletion (removing rows or columns with missing values), and handling them as separate categories. Other methods include regression, mean/median imputation, and multiple imputation.

Tell me what is the difference between bias and variance?

1 Answers  

---

What are convolutional networks? Where can we use them?

1 Answers  

---

Do you know what is kernel svm?

1 Answers  

---

Differentiate between data science, machine learning and ai.

1 Answers  

---

How machine learning is deployed in real world scenarios?

1 Answers  

---

Assume that you are working on a data set, explain how would you select important variables?

1 Answers  

---

What are the different methods for Sequential Supervised Learning?

1 Answers  

---

Explain how does naive bayes classifier work in machine learning?

1 Answers  

---

What is the difference between Gini Impurity and Entropy in a Decision Tree?

1 Answers  

---

How are NumPy and SciPy related?

1 Answers  

---

Mention the difference between Data Mining and Machine learning?

1 Answers  

---

What are the components of relational evaluation techniques?

1 Answers  

---