Introduction

This tutorial aims to take you through the journey of the history and evolution of Machine Learning (ML). We will start from the very beginning, where the concept was merely a theoretical idea, to the present day where it's revolutionizing numerous industries, especially the technology sector.

By the end of this tutorial, you will have a clear understanding of the evolution of machine learning, its various stages, and how it has grown to become an integral part of modern technology.

No specific prerequisites are necessary for this tutorial, although having a basic understanding of computer science concepts will be beneficial.

Step-by-Step Guide

Conceptual Evolution

1. 1950s - Birth of Machine Learning
   Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks.

2. 1980s - Evolution of Machine Learning
   With the advent of cheaper and more powerful computers, machine learning evolved from being knowledge-driven to data-driven.

3. 1990s - Advancements in Machine Learning
   The focus shifted towards decision trees, nearest neighbor methods, and the proliferation of statistical learning techniques.

4. 2000s - Modern Machine Learning
   The current phase of machine learning is characterized by neural networks and deep learning, focus on decision-making, and the use of big data.

Best Practices and Tips

• Always keep up-to-date with the latest trends in machine learning.
• Understand the basics of statistics and probability for better comprehension.
• Get hands-on experience by working on real-world machine learning projects.

Code Examples

This tutorial doesn't involve any coding as it's purely theoretical and historical. But here are some resources where you can find practical code examples:

• Scikit-learn
• TensorFlow Tutorials

Summary

In this tutorial, we've covered the history and evolution of machine learning from its birth in the 1950s to its modern usage in the 2000s. The next step in your learning journey could be diving deeper into specific machine learning algorithms, their use cases, and implications.

Some additional resources for further study:
- Book: "The Hundred-Page Machine Learning Book" by Andriy Burkov
- Online Course: "Machine Learning" by Stanford University (Available on Coursera)
- Research Papers: Google Scholar (Search for Machine Learning)

Practice Exercises

Since this tutorial is more theoretical than practical, here are some thought exercises:

1. Understand the Impact
   Research how machine learning has impacted an industry of your choice. Present your findings.

2. Current Trends
   Identify current trends in machine learning. How do you see them evolving?

3. Future of Machine Learning
   Based on the history and current trends, predict the future of machine learning. What breakthroughs or advancements do you foresee?

Remember, the field of machine learning is vast and continuously evolving. Keep learning, practicing, and staying updated with the latest trends.