# Week #3 in Machine Learning

2 min readJan 24, 2022

*We cover more Supervised learning algorithms*

Last week we covered some classification types in ML. This week I looked into more algothirms that are used in supervised learning for classification and regression problems.

**4. Naïve Bayesian model**

- Used for large finite datasets.
- A method of assigning class labels using a direct acyclic graph. The graph consists a parent node and multiple children’s nodes where each bode is assumed to be independent and separate from the parent.
- There are three types of naïve Bayesian models:
**Multinominal naïve bayes, Bernoulli naïve bayes,Gaussian naïve bayes.**

- The technique is primarily used in text classification, spam detection and recommendation systems.

**5. Random forests**

- It is an ensemble method.
- Used for both classification and regression purposes.
- “forest” refers to a collection of uncorrelated decision trees. The trees are later merged to minimize variance and make the predictions more accurate.

**6. Neural Networks**

- They are primarily leverage for deep learning algorithms.
- They process training data by mimicking interconnectivity in the human brain through layers of nodes.

- Each node is made up of inputs, weights and bias.
- Neural networks learn mapping function through supervised learning, adjusting based on the loss function though gradient decent.
- They require significant computational resources.

Important Links

- https://serokell.io/blog/naive-bayes-classifiers
- https://www.ibm.com/cloud/learn/supervised-learning
- https://www.upgrad.com/blog/types-of-supervised-learning/
- https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article
- https://towardsdatascience.com/supervised-vs-unsupervised-learning-bf2eab13f288
- https://www.networkworld.com/article/3587131/machine-learning-in-network-management-has-promise-challenges.html
- https://www.investopedia.com/terms/n/neuralnetwork.asp