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Dear Learners, welcome to this video. This tutorial will help you to learn the most important supervised learning algorithms. Supervised machine learning turns data into real, actionable insights. It enables organizations to use data to understand and prevent unwanted outcomes or boost desired outcomes for their target variable.

Logistic Regression:
Logistic Regression is a statistical method to model the probability of existing events and is used for the purpose of classification. It is one of the most important predictive analysis algorithms. Logistic regression is used for binary classification and multi-class classification. Whenever one wants to forecast or predict something, these regression algorithms are used for that purpose!

Advantages
1. Makes no assumptions about distributions of classes in feature space
2. Easily extended to multiple classes (multinomial regression) §
3. Natural probabilistic view of class predictions
4. Quick to train 1
5. Very fast at classifying unknown records
6. Good accuracy for many simple data sets i
7. Resistant to overfitting
8. Can interpret model coefficients as indicators of feature importance

Disadvantages -
1. Constructs linear boundaries

Naïve Bayes Classifiers:
Naïve Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naïve Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for the prediction of an unknown class.

Naïve Bayes classifiers are linear classifiers based on Bayes' theorem. The model ‘
generated is probabilistic :
1. It is called naïve due to the assumption that the features in the dataset are mutually
independent |
2. In the real world, the independence assumption is often violated, but naïve Bayes
classifiers still tend to perform very well |
3. The idea is to factor all available evidence in the form of predictors into the naïve Bayes rule
to obtain a more accurate probability for class prediction
4. It estimates conditional probability, which is the probability that something will happen, given that something else has already occurred. For e.g. the given mail is likely spam, given the appearance of words such as “prize”
5. Being relatively robust, easy to implement, fast, and accurate, naïve Bayes classifiers
are used in many different fields

Advantages
1. It is straightforward and simple to use.
2. There is less training data needed.
3. It manages data that is both continuous and discrete.
4. With regard to the number of predictors and data points, it is quite scalable.
5. It is quick and can be utilized to make predictions in the present.
6. It is not sensitive to unimportant characteristics.

Topics Covered:
00:00:00 Introduction
00:00:00 Logistic Regression
01:15:25 Naïve Bayes Classifier

#machinelearning #logisticregression

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