Python

In the previous tutorial, we learned about control flow: if, for, while, and match/case. Now let’s learn about functions — reusable blocks of code. Functions are the foundation of clean code. They let you write a piece of logic once and use it many times. By the end of this tutorial, you will know how to define functions, use different parameter types, write lambda functions, and understand closures. Defining a Function Use the def keyword to create a function: ...

In the previous tutorial, we learned about variables, types, and f-strings. Now let’s learn how to make decisions and repeat actions in Python. Control flow statements let your program choose what to do based on conditions and repeat actions in loops. By the end of this tutorial, you will know how to use if, for, while, match/case, and several useful loop helpers. if, elif, else The if statement runs code only when a condition is true: ...

In the previous tutorial, we installed Python and wrote our first program. Now let’s learn about the building blocks of every Python program: variables and types. By the end of this tutorial, you will know how to create variables, work with different data types, format strings with f-strings, and convert between types. Variables in Python A variable stores a value. In Python, you create a variable by assigning a value with =: ...

In the previous tutorial, we learned what Python is and why it is worth learning. Now it is time to install it and write your first program. By the end of this tutorial, you will have Python running on your computer. You will know how to use the Python REPL, write a script, and run it from the terminal. Installing Python Python works on macOS, Linux, and Windows. Let me show you how to install it on each system. ...

Python is the most popular programming language in the world right now. It has been the number one language on the TIOBE Index since 2021 and holds a record-breaking 26% rating in 2025-2026. But why? What makes Python so popular? And should you learn it? In this tutorial, we will answer these questions. By the end, you will understand what Python is, where it is used, and why it is worth learning in 2026. ...

These ten concepts appear in almost every Python project. If you know all of them, you can read and write real Python code. If you are missing one, that is the one that trips you up on every project. 1. Variables and Data Types Python infers types automatically. No declaration needed. name: str = "Alex" age: int = 25 score: float = 9.5 active: bool = True nothing = None print(type(name)) # <class 'str'> The four built-in collection types: numbers = [1, 2, 3] # list — ordered, mutable point = (10, 20) # tuple — ordered, immutable tags = {"python", "dev"} # set — unique items user = {"name": "Alex", "age": 25} # dict — key/value pairs Use type() to check the type of any variable at runtime. Use type hints for documentation and IDE support. ...

PyTorch is the standard framework for deep learning research and production. Most AI papers, Hugging Face models, and state-of-the-art systems use PyTorch. This article gets you from zero to a working neural network. Setup pip install torch torchvision import torch print(torch.__version__) # 2.x print(torch.cuda.is_available()) # True if you have a GPU Tensors A tensor is the fundamental data structure in PyTorch. It is like a NumPy array, but it can run on GPU and supports automatic differentiation. ...

Neural networks power most AI you use today. ChatGPT, image recognition, voice assistants — all neural networks. You do not need a math degree to understand them. This article explains the concepts clearly, with code examples in plain Python and PyTorch. What Is a Neural Network? A neural network is a function. It takes numbers in, does math, and produces numbers out. That’s it. The magic is in how it learns which math to do. ...

You know NumPy and Pandas. Now it is time to train a model. scikit-learn is the standard library for machine learning in Python. It is simple, well-documented, and works for most real-world tasks without a GPU. Setup pip install scikit-learn pandas numpy import sklearn print(sklearn.__version__) # 1.5+ The ML Workflow Every supervised ML task follows these steps: 1. Load data 2. Prepare features (X) and target (y) 3. Split into train and test sets 4. Train a model 5. Evaluate on test set 6. Make predictions on new data Let’s go through each one. ...

Before you train any machine learning model, you need to handle data. NumPy and Pandas are the two libraries you will use every day. This is a practical crash course. No theory — just the operations you actually need. Setup pip install numpy pandas Check versions: import numpy as np import pandas as pd print(np.__version__) # 2.x print(pd.__version__) # 2.x NumPy: Arrays NumPy gives you fast multi-dimensional arrays. They are faster than Python lists for math operations. ...