Answer / Atul Goel

Handling missing or corrupted data is crucial to ensure the quality and reliability of Machine Learning models. There are several strategies for dealing with missing data: Imputation (replacing missing values with a value estimate such as mean, median or mode), Deletion (removing the entire row or column containing missing data), and Data Interpolation (filling in missing values by interpolating between existing data points). For corrupted data, techniques like outlier detection and removal can be applied to clean and preprocess the dataset.

Explain the two components of Bayesian logic program?

1 Answers  

---

What does linear in ‘linear regression’ actually mean?

1 Answers  

---

What is your training in machine learning and what types of hands-on experience do you have?

1 Answers  

---

What is ensemble learning?

1 Answers  

---

What is the best language to learn machine learning?

1 Answers  

---

What is bayes' theorem?

1 Answers  

---

Explain how do you think google is training data for self-driving cars?

1 Answers  

---

What is a model selection in machine learning?

1 Answers  

---

Who invented machine learning?

1 Answers  

---

What is data structure? And what are the different types of data structures supported in r programming?

1 Answers  

---

Why do we need to convert categorical variables into factor?

1 Answers  

---

What is an AdaGrad algorithm in machine learning?

1 Answers  

---