Valery Leontyev’s Post

As a person who primarily programs in Python, I am guaranteed that Python is one of the most beginner-friendly programming languages, while its use cases are limitless.

Today I revised some fundamental Python concepts through a simple True/False practice session. These basics may look small, but they build a strong foundation for programming and AI 👇 🧠 Practice Questions: 1️⃣ # is used for single-line comments in Python → ✅ True 2️⃣ 2ndName is an invalid identifier in Python → ✅ True 3️⃣ ** is a valid arithmetic operator in Python (exponentiation) → ✅ True 4️⃣ in is a logical operator in Python → ❌ False (It is a membership operator) 5️⃣ Variable declaration is implicit in Python → ✅ True ✨ Key Takeaway: Understanding small concepts like identifiers, operators, and comments makes coding easier, cleaner, and more logical. I’m continuously learning Python to strengthen my base for AI, Data Science, and Software Development. Consistency > Speed 💯 📌 If you’re also learning Python, try answering these before checking the solutions! #Python #PythonBasics #Programming #LearningJourney #BCA #AI #CodingLife #TechSkills #Students #Consistency

Starting Python again felt… brand new✨ I went back to my Python learning, and honestly? It felt like the first time I ever opened the tutorial. I was hearing terms I swear I never heard the first time. But I guess that’s what returning to learning feels like you don’t just repeat it, you understand it differently. One thing I like currently about Python is the print() function. Simple, but powerful. It talks back to you. Literally. I also revisited the sources of functions, and it finally clicked: 📍Built-in functions — they come with Python straight out of the box (print(), input(), len()… the OGs) 📍Third-party functions — from external libraries like Pandas, NumPy, PySpark, etc. Created by someone else, but still very usable in your own code 📍User-defined functions— the ones I create That moment when you realize you can tell Python exactly what to do? Yeah… I smiled 😌 I even re-learnt escape sequences like: \" \ ' \n \t \b Small things, but they make your code speak clearly. Another beautiful thing I’m appreciating now: There’s more than one way to get the same result in Python. And the print() function? It’s not just for “Hello World.” It helps us: • Communicate with users • Display results • Debug and test our code • Understand what’s really happening behind the scenes Going back to learning actually feels good. Less pressure. More clarity. Deeper understanding. Sometimes restarting isn’t going backwards, it’s going stronger Back to learning. Again. And this time, it makes more sense. it's Day 7 of my consistency 30 days consistency challenge already 😊 #gwo_linkedin30dayschallenge #pythonlearning #dataanalytics #discipline #consistency

What is Python? Here's a 2 minute explanation for all you non-coders: Python is a programming language. What's a programming language? It's how we convince computers to do things they absolutely don't want to do. Like your teenager, but with more consistent results. Python powers everything from AI breakthroughs to those apps that somehow know you need cat videos at 2 AM. It's everywhere. And there's a reason we all put up with it. What makes Python special? It looks suspiciously like English (at least compared to other languages). Here's how you tell a computer to say "Hello, World!" in C++: #include <stdio.h> int main(void) { printf("Hello, World!"); return 0; } Here's Python doing the same thing: #holdmybeer print('Hello, World!') Some languages (not pointing fingers) looks to the average person like someone sneezed on their keyboard and forgot to fix it. Python looks like... well... it just looks better. Summary of why we've all collectively agreed Python is great: ✅ Readable: Your coworker's code might actually make sense at a glance (revolutionary) ✅ Flexible: Python can run the apps you're doomscrolling at 2 am... or spacecraft. Same language. No one questions this. ✅ Powerful: Has more community-built tools than if Bob the Builder and Handy Manny were in an arms race Want to analyze spreadsheets? Build an AI? Generate cat names every 30 seconds because Tuesday got weird? Python (thanks to the Python community) shrugs and says "sure." That's why we at Anaconda focus on Python. It lets us (and the world) solve actual problems instead of decoding whatever we wrote last Tuesday in a caffeine haze. If you're a Python beginner and want to test some products we're building, reach out. We need people who still remember what confusion feels like. #Python #Programming #DataScience #AI #Tech #Coding #LearnToCode #TechEducation #Anaconda

How Python Improved My Problem-Solving Mindset (Python Learning Journey – Day 9) Python didn’t just teach me how to code → it taught me how to think. Before learning Python, I often tried to solve problems all at once. I would jump straight to the solution and hope it worked. Python quietly changed that habit. It forces you to slow down and break things into steps. What is the input → what should happen next → what is the final result. This sequence becomes natural the more you practice. I noticed that I stopped guessing and started reasoning. Instead of asking “Will this work?” I began asking, “What exactly should happen here?” Writing Python made me more patient with problems. If something didn’t work, I didn’t panic. I traced the logic line by line and found where my thinking went off track. That shift was powerful. Problems stopped feeling heavy. They became smaller, manageable pieces that I could handle one by one. Each line of code felt like a decision, not a gamble. This mindset started showing up outside coding, too. When facing a complex task, I now pause and ask → What’s the first step → what comes after → what outcome do I want? Python didn’t give me answers. It gave me a framework to reach them. The language rewards clarity. If your thinking is messy, the code reflects it. If your thinking is clear, the solution becomes obvious. That’s when I realised something important → Programming is not about typing fast. It’s about thinking clearly. Learning Python is slowly training my mind to approach problems with structure, logic, and calmness. And that might be its biggest value. Has learning to code changed how you approach problems in everyday life? #pythonlearning #pythonlearningday9 #problemsolving #learninginpublic #developerthinking

𝗧𝗵𝗶𝘀 𝗜𝘀 𝗔 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗢𝗹𝗹𝗮𝗺𝗮 𝗚𝗨𝗜𝗗𝗘 You want to build AI agents with Python. This guide teaches you how to use Ollama and LangChain. You will learn: - How to set up Ollama + LangChain - How to use ollama.chat() vs ChatOllama() - How to build agents that remember things - How to deploy to production You can use ollama.chat() for simple queries. It is fast and easy to use. You can use ChatOllama() for more complex tasks. It can make decisions and use tools. To get started, you need to install the required libraries. Then you can start building your AI agent. You can use different models with Ollama. The size of the model depends on your computer. You can use Qwen2.5-Coder 1.5B for fast responses. You can use Qwen-Coder 7B for a good balance between speed and capability. You can use Qwen3-Coder 30B for the most capable model. If you have any problems, you can try the following fixes: - Start Ollama before running your Python code - Download the model if it does not exist - Use a smaller model if you get a "CUDA out of memory" error - Use a smaller model and shorter output if it takes too long to get a response Source: https://lnkd.in/geZD7x85 Optional learning community: https://t.me/GyaanSetuAi

I’ll be honest: when I started looking at Python code, I thought, "It’s just words and numbers, how hard can it be?" Then I ran into my first "TypeError." It turns out, Python is a bit of a perfectionist about how it stores information. It doesn’t just see "data"—it sees specific types, and if you mix them up, everything stops. I like to think of them as the "Social Circles" of the Python world: The 'Numbers' Crew (Int & Float): They only care about math. If you’re a whole number like 5, you’re an Integer. If you’ve got decimals like 5.99, you’re a Float. The 'Storytellers' (Strings): Anything inside "quotes." Even if it’s a number, if it has quotes, Python treats it like a word. The 'Organizers' (Lists & Dicts): This is where it gets fun. Lists are like a neat row of lockers. Dictionaries are more like a phonebook—you have a name (Key) and a number (Value). Why am I sharing this? Because sometimes we overcomplicate "learning to code." At the end of the day, it's just about learning how to put the right things in the right boxes. 📦 I’m officially diving into the deep end with Python, and I’m planning to share these "aha!" moments as I go. For the Python pros in my network: What’s the one thing you wish you knew in your first week? For the fellow beginners: What are you learning right now? Let’s keep each other accountable! 👇

“Do I need to learn Python to work with AI?” If you have Googled anything about LLMs or machine learning, you have seen Python is f**kin everywhere! Every tutorial. Every code snippet. Every course. It starts to feel like Python is artificial intelligence. So if you are a JavaScript developer, a marketer curious about AI, or someone deciding whether to invest time learning Python, the real question is simple: Is this actually necessary? Here is what is really happening. Python is not the engine. It is the steering wheel. The heavy lifting that makes LLMs work runs in C++ and CUDA on GPUs. Python is mainly used to send instructions to those systems. So why Python, and not JavaScript or Java? Three practical reasons. 1. The tools already exist there PyTorch, TensorFlow, NumPy, Pandas. The core AI libraries are built around Python. Using Python means immediate access to years of tooling, examples, and shared knowledge. JavaScript based ML tools are improving, but the ecosystem density is not comparable yet. 2. Researchers chose it first, and everyone followed Academic ML standardised on Python. Papers, open source models, tutorials. Almost all of them assume Python. If you want to use existing work, you are reading Python. 3. Experimentation matters more than speed Most AI work is trial and error. Python notebooks let you run a line, inspect the output, tweak, and repeat. That workflow matches how ML is actually built. So what does this mean for you? If you are a marketer or business leader: The programming language matters far less than people think. What matters is access to the right models, data, and decision making. If you are deciding what to learn: Basic Python literacy is useful if you want hands on AI capability. But it should not be a blocker. You can achieve a lot by using tools built by others. Python did not win because it is the best language. It won because it is where the community gathered. If you are struggling to apply this in practice, or want hands on tutorials and guidance on how to actually get value from LLMs, I have specific courses and practical guides available. Let me know in the comments or DM me directly.

🚀 How I Learned to Read Python Error Messages Without Fear (Python Learning Journey – Day 13) The first time I saw a long Python error message, I ignored most of it. Too much text. Too many unfamiliar words. Too much panic. But that habit was slowing me down. 👉 What if the error message isn’t noise? 👉 What if it’s actually guidance? 👉 What if it’s trying to help? That’s when my approach changed. 🌿 What Error Messages Really Are An error message is Python explaining what went wrong. Not emotionally. Not vaguely. But precisely. Each line has a purpose. It tells you where the problem happened → and often why. I learned to stop scanning and start reading. Line by line. From bottom to top. ✔️ The last line usually tells the real issue ✔️ The file name shows where to look ✔️ The line number saves time Once I treated error messages like instructions, fear disappeared. 🙌 Why It Matters Ignoring errors keeps you stuck. Understanding them moves you forward. This habit improved my patience and focus. It also reduced random trial and error. The same lesson applies outside coding. Clear feedback is valuable only if we listen to it. An error message isn’t a judgment. It’s a conversation with your code. 🔗 Now Your Turn Do you read error messages carefully, or skip straight to fixing? #PythonLearning #LearningInPublic #DeveloperJourney #CodingMindset #DebuggingSkills

A small Python detail that makes function calls safer: * in parameters Sometimes, the most impactful learning moments come from tiny language features. Recently, I revisited the use of * in Python function parameters — and it’s one of those things that quietly improves code clarity and safety. When you place a * in a function signature, it means that all parameters after the * must be passed as keyword arguments. Example: def build(queryset, *, name, url): ... Here’s how it must be called: build(qs, name="test", url="example.com") And here’s what Python prevents you from doing: build(qs, "test", "example.com") # ❌ not allowed Why this matters in real projects: - Makes function calls more readable - Prevents bugs caused by argument order - Protects public APIs from breaking when parameters evolve - Encourages explicit, self-documenting code It’s a small syntax feature, but it communicates intent clearly: these arguments matter and should be named. Another reminder that mastering Python isn’t just about frameworks — it’s about understanding the language itself and using it deliberately.