Machine Learning 101

## In this online meetup, we will discuss the main categories of Machine Learning and implement some algorithms using the Python programming language.

**RSVP HERE** [https://bit.ly/rbvw-ml-101](https://bit.ly/rbvw-ml-101)

**After registering, you will receive an email with the Zoom link.**

In this session, we will closely examine supervised machine learning algorithms using scikit-learn, a core Python library for machine learning. We will delve into coding problems such as linear regression and K-Nearest Neighbors (KNN).

Linear Regression and K-Nearest Neighbors (KNN) are pivotal machine learning algorithms with distinct applications. Linear regression forms the bedrock for comprehending and modeling relationships between variables, particularly for predictive modeling tasks like sales forecasting, stock price prediction, and health outcome analysis. Its transparency through interpretable coefficients allows for understanding how each input variable influences the prediction. Moreover, it acts as a baseline model for evaluating the efficacy of more complex machine learning models, providing a benchmark for model performance.

On the other hand, KNN stands out due to its non-parametric, instance-based nature, making it suitable for cases with complex or unknown data distributions. It serves a dual role in both classification and regression tasks, offering versatility across a spectrum of data types, including numerical and categorical attributes. KNN’s ability to capture local patterns and relationships within the data, coupled with its adaptability for use in recommendation systems, anomaly detection, and pattern recognition, makes it an indispensable tool for various machine learning problems. In essence, linear regression and KNN contribute significantly to the machine learning landscape, each offering unique strengths and versatility for addressing diverse real-world challenges.