What is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence that allows systems to learn from data and make predictions or decisions without being explicitly programmed. ML is widely used in various industries, from healthcare to finance, for tasks like image recognition, natural language processing, and predictive analytics.

Introduction:

If you want to learn about machine learning and become an expert one thing that you would want to avoid is “tutorial hell” and this guide will help you achieve that by using a project based real life implementation based approach to Machine Learning through various projects and concepts.

The key to learning from this guide is that it acts as a reference roadmap which you can use to learn about everything and it is all given in sequential order you can refer to all kinds of resources on the internet including the official documentations (which are really a gold mine!) rather than watching hours of tutorials and getting a basic descent understanding after that pickup a random project from sites like Kaggle which you want to do and implement it you would learn a lot of things along the way.

This roadmap is designed as a personal guide and tracker to help you structure and monitor your journey in machine learning. It is based on my own project-based learning experience and does not promote any form of misinformation or harmful practices. The roadmap aims to provide a clear and practical approach to learning ML but should be adapted to suit your unique goals and pace.

Roadmap

1. Learning the Basics

• Understand what machine learning is and its real-world applications.
• Differentiate between supervised, unsupervised, and reinforcement learning.

2. Frameworks and Libraries

• Python: Learn Python, including using Jupyter Notebooks which would be essential for data science and ML workflows.

• Pandas:

  • NumPy arrays, spreadsheet functionality, and SQL-like joins.
  • Learn to use built-in statistics, plotting, and visualization tools.
  • Project: Perform a data analysis project to consolidate learning.

  Pick up any random dataset which you would like and create a data science project which would help grasping the fundamentals of python, pandas and everything related to it (It does not need to be complex).

• Learn NumPy Basics