How Would I Learn This: Part 1 - Find The Right ML Algorithms For Your Problems

Self teaching: You would learn about my way to identify the type of your machine learning problem, steps to explore your data, different features types for example, in clustering, algorithms.

There are wide range of ML algorithms to pick for your problem, from simple linear regression to complex neural networks, it can be overwhelming to get started, especially if you are a self taught.

The challenge here is to understand the problem and the data, breaking it down into manageable components, and identifying the best approach to achieve your goal.

The process of selecting a machine learning algorithm is not a one-size-fits-all task. It involves defining the type of problem, analysing the data, and matching the problem's characteristics with the most suitable algorithm.

This article will guide you through a 5-step framework I’ve learned for finding the right machine learning algorithm for your task:

1. Define the type of task you are solving.

2. Understand the machine learning category it falls under.

3. Explore and analyze your data (EDA).

4. Identify the specific variation of your problem and determine the appropriate algorithm.

5. Match the problem to the algorithm.

Let’s dive in!