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Ready to craft the most intelligent virtual assistant you've ever worked with? In this step-by-step tutorial, we're going to guide you through how to build a robust GPT-4 chatbot from scratch — no PhD or humongous tech stack needed. Whether you desire to craft a customer care agent, personal tutor, story-sharing friend, or business partner, this video takes you through each and every step of the process. Learn how to get started with the OpenAI GPT-4 API, prepare your development environment, and create smart, responsive chat interactions that are remarkably human. We'll also go over how to create memory, personalize tone, integrate real-time functionality, and publish your bot to the web or messaging platforms. At the conclusion of this guide, you'll have a working GPT-4 chatbot that can respond to context, reason logically, and evolve based on user input. If you’ve ever dreamed of creating your own intelligent assistant, this is your moment. Start building your own GPT-4 chatbot today and unlock the future of conversation.

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Welcome to Episode 11 of the Python for Data Analysis Series.

In this episode, we explore NumPy, one of the most important Python libraries used in data science, machine learning, and scientific computing.

NumPy (Numerical Python) provides powerful tools for working with large multi-dimensional arrays, matrices, and numerical operations. It forms the backbone of the Python data ecosystem and is widely used by data scientists, AI engineers, and researchers.

In this video, you will learn:

✔ What NumPy is and why it is important ✔ Why NumPy is faster than Python lists ✔ The concept of NumPy arrays ✔ One-dimensional, two-dimensional, and multi-dimensional arrays ✔ How to create arrays using NumPy ✔ Built-in functions such as zeros, ones, arange, linspace ✔ Random array generation in NumPy ✔ Array reshaping and slicing ✔ Element-wise array operations ✔ Broadcasting in NumPy

By the end of this video, you will understand how NumPy improves performance, efficiency, and scalability when working with numerical data in Python.

This episode is perfect for:

• Python beginners • Data science learners • Machine learning students • Analytics professionals • Anyone interested in numerical computing with Python

📌 Series: Python for Data Analysis 🎧 Podcast: One Click Learning 🎬 Episode: 11 – Introduction to NumPy

Testing PIO support for RP1 chips on Pi 5 Computers

👉Python के साथ अनंत संभावनाओं को अनलॉक करें! 🚀

💻हमारा Python कोर्स आपको तकनीकी दुनिया के लिए मांग में कौशल से लैस करेगा।🎯

👨‍🎓आपकी कोडिंग यात्रा अभी शुरू होती है!👩‍🎓

🔗Click On: https://dotnet.courses.store/698578?mainCategory=149

🎒DOTNET Institute💻

(28/12/2022)

Today I start my book about Python and I realized that it would take several steps before I could play my snake game.

If I maintain discipline and don't give up, maybe I'll finish it in January.

And it's okay, the theory is boring, it takes time but without it we can't do the practice.

And since I'm going to use Python in a big project I don't think it's useful and smart to skip the algorithms and data structure and just copy a random tutorial from the little game.

And I loved seeing the translation of the codes I used in Portugol now in python, it's really cool.