Baştan Yapay Zeka Öğrenmek İsteyenlere Tavsiyeler

Introduction

This tutorial is designed for anyone looking to start learning about artificial intelligence (AI) from scratch. Based on insights from the video "Baştan Yapay Zeka Öğrenmek İsteyenlere Tavsiyeler" by Mehmet Hilmi ML, this guide provides a comprehensive roadmap, recommended resources, and practical steps to kickstart your journey in AI.

Step 1: Learn Python

Starting with Python is essential, as it is the most popular programming language for AI and machine learning.

• Why Python?

  • Easy to learn and widely used in the AI community.

• Resources:

  • Visit Learn Python to access free tutorials and exercises.

Step 2: Master Jupyter Notebook

Jupyter Notebook is a powerful tool that allows you to write code, see outputs, and document your work all in one place.

• Getting Started:

  • Familiarize yourself with its interface and features.

• Documentation:

  • Access the Jupyter Notebook Documentation for detailed instructions.

Step 3: Explore AI and Machine Learning Courses

Google offers excellent free resources to help you understand the basics of AI and machine learning.

• Recommended Course:
  • Enroll in Google’s AI and ML Crash Course to gain foundational knowledge.

Step 4: Dive into Data Analysis and Visualization

Understanding data is crucial in AI. Familiarize yourself with tools for data analysis and visualization.

• Web Scraping:

  Learn to collect data using

  • BeautifulSoup for basic web scraping.
  • Selenium for more complex tasks.

• Data Analysis:

  • Use Pandas to manipulate and analyze data.

• Data Visualization:

  Create visual representations with

  • Matplotlib
  • Seaborn

Step 5: Learn Machine Learning and Deep Learning

After grasping data analysis, move on to machine learning and deep learning concepts.

• Learning Path:
  • Check out the YouTube playlist on Machine Learning for structured lessons.

Step 6: Utilize Google Colab

Google Colab is a free, cloud-based platform that allows you to write and share code easily.

• Getting Started:
  • Access Google Colab and familiarize yourself with its features.

Step 7: Follow Documentation for Libraries

To deepen your understanding, refer to the official documentation of key libraries.

• Key Libraries:
  • Scikit-Learn for machine learning algorithms.
  • Keras for building neural networks.
  • PyTorch for deep learning applications.

Conclusion

Starting your journey in artificial intelligence requires a structured approach. Begin with Python, utilize resources like Jupyter and Google Colab, and progressively delve into data analysis and machine learning. Following this roadmap will set a solid foundation for your AI knowledge, enabling you to tackle more complex projects in the future. For your next steps, consider enrolling in the recommended courses and actively engaging with the documentation of the libraries mentioned. Happy learning!