SIZ Education | Basic Understanding of Machine learning algorithms

What is machine learning at the science of our computers learn about being explicitly programmed it lies at the intersection of hacking skills and math and statistics without the need for substantive expertise that's because the machine learning algorithms don't need to know the data type that's being used for training they don't need to know for example that this is a financial or a medical data so where do you start in the next few slides we're going to cover some of the machine learning algorithms.

Exploration and research have highlighted some of the most popular classes or types of algorithms:

1. Fortson classification
2. Logistic regression
3. Support vector
4. Random forests
5. Least-squares
6. Clustering detection etc.

[Figure_7: Machine Learning Algorithms (Made by me)]

Explanation:

• Logistic regression is a linear model with fast training where the clusters are expressed in probabilities for example good or bad or 1 or 0. Regression is great for predicting a continuous-valued output.

• Support vector is a very effective algorithm described as the best-supervised learning area. It’s more robust to outliers and handles non-linearity.

• Fortson classification is great for text classification. It assumes independent variables for example in a case of a text it assumes that each Word contributes to the overall probability independently.

• Clustering is an automating grouping of similar objects intercepts or clusters and is useful as an initial step in data exploration.

• Least-squares is a multivariate method to find the best possible match for the given data sets. It constructs the SRF by using slope and constant values so that an analyst can find the micro scale possible value.

• Random vector when working with multiple random variables, it is sometimes helpful to use vector and matrix displays. This makes the formulas more cohesive and allows us to apply facts from linear algebra.

I am also thankful to the management of SIZ for providing such a great platform where we can show our talent and skills.

CC: @cryptokraze @siz-official