Machine Learning Roadmap for Absolute Beginners

Overview

This guide provides a step-by-step approach to learning Machine Learning (ML) from scratch. Follow each step, practice the exercises, and update this file as you progress.

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Step: Learn Python Basics

Machine Learning relies heavily on Python. Start with basic programming concepts.

What to Learn:

• Variables, loops, conditionals (if/else).
• Functions and data structures (lists, dictionaries).
• Basic file I/O (reading/writing files).

Practice:

• Solve problems on Codecademy or LeetCode.

⏳ Estimated Time: 1–2 weeks

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Step: Data Manipulation & Visualization

Learn how to handle and visualize data using Python libraries.

Libraries to Learn:

1. pandas: DataFrames, filtering, grouping, merging.
2. Matplotlib/Seaborn: Creating bar charts, scatter plots.

Practice:

• Load a CSV file (e.g., Titanic dataset) and analyze it

2 weeks (parallel to coding practice)

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Common Mistakes to Avoid

1. Skipping fundamentals (Python/math).
2. Not practicing enough—ML is hands-on!
3. Getting stuck on theory—focus on applied learning.

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