### Lecture 17 – Multiple Linear Regression

You already know of Simple Linear Regression. You also know of Logistic Regression. Now we would discuss Multiple Linear Regression. This is a case where we have more than one …

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# Machine Learning 101

### Lecture 17 – Multiple Linear Regression

### Machine Learning 101 – Basics of Logistic Regression

### Machine Learning 101 – Minimizing Misclassification Rate in Bayes’ Classifier

### Machine Learning 101 – K-Nearest Neighbors Classifier

### Machine Learning 101 – The Bayes’ Classfier

### Machine Learning 101 – Introduction to Classification

### Machine Learning 101 – Bias-Variance Trade-off

### Machine Learning 101 – What is Probability Density?

### Machine Learning 101 – Application of Bayes’ Theorem

### Machine Learning 101 – Rules of Probability & Bayes’ Theorem

You already know of Simple Linear Regression. You also know of Logistic Regression. Now we would discuss Multiple Linear Regression. This is a case where we have more than one …

Read MoreFirst I would like clarify that the Logistic Regression model is a model for classification. Also note that Machine Learning 101 focuses on Supervised Learning. Therefore we always would be …

Read MoreIn Lecture 4, we learnt about the Bayes’ classifier. Here we would see how to minimize misclassfication rate in Bayes classifier. Again, we would review the cancer diagnosis example. …

Read MoreIn the last lecture, we discussed Bayes’ Classifier. Now, we are going to discuss K-Nearest Neighbors Classifier. Remember that Bayes Classifier tries to classify X depending on the conditional probability …

Read MoreThis is the second lecture on classification. It follow the first one: Introduction to Classification. Bayes’ Classifier is a classifier that works based on Bayes’ Theorem. It assigns each observation …

Read MoreIn subsequent lectures, we have discussed regression problems. Now we would apply the same analysis to classification but with little adjustment. In case of classification, we also have a dataset …

Read MoreThis Lecture follows from Lecture 7 on Underfitting and Overfitting. Here we would discuss Bias-Variance Trade-off. I will try to make this lesson very clear. You already know that in …

Read MoreBy now, you probably understand probability as well as probability theory. You also know about the Sum Rule and Product Rule. Then you also understand Bayes’ theorem from Lesson 9 …

Read MoreIn the previous lesson (Lesson 9), we derived Bayes theorem. So let’s write it out: Also recall that Bayes’ theorem helps us find conditional probabilities given marginal probability. Lets’ now …

Read MoreWe will now consider some of the important rules of probability. Meanwhile we would also understand the meaning of terms along the line. They include: Conditional Probability Sum Rule Product …

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