# Machine Learning – Regression and Classification (math Inc.)

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## A brief description

Machine learning is a branch of artificial intelligence (AI) concerned with the creation of algorithms that learn from data and increase their performance over time without being trained to do so.

An algorithm is a series of mathematical processing steps used in data science. Algorithms are ‘trained’ in machine learning to identify patterns and characteristics in vast volumes of data so that they can make conclusions and forecasts based on new data. When more data is processed, the faster the model becomes, the more reliable the decisions and forecasts become.

Machine learning has created some impressive effects, such as the ability to interpret medical videos and forecast diseases on par with human experts.

Using advanced reinforcement learning, Google’s AlphaGo software defeated a world champion in the strategy game go.

Self-driving vehicles are being programmed using machine learning, which will forever transform the automotive industry. Imagine a world where auto crashes are dramatically minimized simply by eliminating the factor of human error.

This course covers the following topics:

1. Knowledge Advantage and GINI Impurety [Decision Trees] lecture

2. In demonstration sessions, numerical problems related to Decision Tree will be solved.

3. [Coding] Used a Decision Tree Classifier in a Workshop Session

4. Trees of Regression

5. Bring the Decision Tree Regressor into effect.

6. Linear Regression (Simple)

7. Cost function and computational application of the Ordinary Least Squares Algorithm tutorial

Multiple Linear Regression (MLR) is the eighth step in the regression process.

9. Linear Polynomial Regression

10. [[coding session]] Implement Simple, Many, Polynomial Linear Regression

### This course is intended for the following individuals:

Beginners and Seasonal Python programmers interested in learning the various AI and machine learning algorithms
Students who want to learn everything there is to know about the mathematics behind traditional regression and classification models.
Students interested in studying how to use data science libraries to address real-world Machine Learning issues.

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