Answer / Prakash Raj

Bayes' Theorem is a probability theory rule that calculates conditional probabilities. It states the probability of an event A given event B (P(A|B)) can be found by multiplying the prior probability of A (P(A)), the likelihood of B given A (P(B|A)), and the prior probability of B (P(B)) divided by the total evidence or marginal likelihood P(B). In machine learning, Bayes' Theorem is employed in classification algorithms such as Naive Bayes and Bayesian networks to make predictions based on conditional probabilities.

Tell me what are the last machine learning papers you've read?

0 Answers  

---

What are your favorite use cases of machine learning models?

1 Answers  

---

What are the classification problems in machine learning?

1 Answers  

---

What are the different types of classifiers?

1 Answers  

---

Pick an algorithm and write a Pseudocode for the same?

1 Answers  

---

What Is Fourier Transform In A Single Sentence?

1 Answers  

---

How is KNN different from K-means clustering?

1 Answers  

---

Tell us what's a fourier transform?

1 Answers  

---

Is naïve bayes a supervised or unsupervised method?

1 Answers  

---

What is ‘tuning’ in ML?

1 Answers  

---

Which library would you prefer for plotting in Python language: Seaborn or Matplotlib or Bokeh?

1 Answers  

---

Why are vectors and norms used in machine learning?

1 Answers  

---