Learn how to automate file organization in Python.

Learn different ways to rename files in Python using the os and pathlib modules.

You can use the round() function with negative arguments to round it to the nearest 10, 100, 1000, and so on.

Learn what additional arguments you can use for the print function.

Learn how to easily find the longest String in a List in Python using the max() function.

Learn how to loop over multiple Lists in Python with the zip function.

In this Python Tutorial we learn how to build a terminal application (CLI app) to manage our tasks and todos.

In this article we learn how to analyze Apple Health Data with Python.

Understand the purpose and usage of args and kwargs in Python functions.

In this article we learn about the most essential built-in string methods.

8 quick Python refactoring tips for cleaner and more Pythonic code.

A clean way to ask the user for input and catch invalid inputs.

Master the new Pattern Matching Feature in Python 3.10. We have a look at all the different code syntax options.

In this Python Tutorial we create a note taking app with speech recognition and the Notion API.

This article shows and explains the most important new features in Python 3.10.

This article shows and explains the most important new features in Python 3.10.

Knowing this can save you HOURS of debugging! (5 Python Pitfalls)

10 Must Know Python Basics and More Tips And Tricks.

This article shows the difference between append() and extend() for Python Lists.

Learn different ways to concatenate two lists or other iterables in Python.

Learn what's the difference between the __str__ and __repr__ methods in Python.

Learn what's the difference between a class method, a static method, and an instance method in Python.

This article shows how to pad a numeric string with zeroes to the left such that the string has a specific length.

This article shows how a directory and all missing parents of this directory can be created in Python.

This article shows different ways to merge two Dictionaries in Python.

Learn how to run a system command from Python and how to execute another program.

Learn how to check if a String contains a Substring in Python and how to get the position.

Learn how the index of an item in Python Lists can be found.

Find out how to access the index in for loops in Python.

This article presents different ways how to check if a file or a directory exists in Python.

Learn how to remove elements in a List in Python while looping over it. There are a few pitfalls to avoid.

In this post I share my favorite free Machine Learning Crash Courses.

It's good practice to apply this in your scripts. Find out why and how this works.

A while loop is used for iterating over a sequence. This artice shows how to use while loops.

A for loop is used for iterating over a sequence. This artice shows how to use for loops.

8 quick Python refactoring tips for cleaner and more Pythonic code.

Learn about new Django async features like async views, middleware, and tests.

Learn how to deploy PyTorch models on iOS devices with Torchscript.

Learn everything to get started with Huggingface and the Transformers library.

In this video I show you 10 deep learning projects from beginner to advanced that you can do with TensorFlow or PyTorch.

Learn how to deploy Machine Learning models with Google Cloud Run.

In this MongoDB Crash Course with Python you will learn everything you need to get started.

In this tutorial I explain why I don't care about code formatting while writing Python code.

Build a Machine Learning web application from scratch in Python with Streamlit.

In this Python Tutorial I show you how you can create beautiful terminal styling with Rich. Rich is a Python library for rich text and beautiful formatting in the terminal.

In this Tutorial I show you how easily Neural Networks can be hacked, and what you should do to protect against this.

In this Python Tutorial we learn if the for loop or the while loop is faster in Python, and I show 3 better solutions instead of these loops.

Learn all essential numpy functions in this tutorial.

In this Python Tutorial you will learn how you can efficiently work with spreadsheets in order to analyze and edit your data.

In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.

In this tutorial I show you how to scrape reddit with the reddit API and automatically label the data for NLP projects.

In this video you will learn how to build a photo sharing web app using Django.

In this Python Tutorial we do time sequence prediction in PyTorch using LSTMCells.

I show you an overview of the NVIDIA Jarvis framework for conversational AI and how to get started with it.

In this Python Tutorial we build a GUI application with Tkinter for a chatbot.

In this tutorial we build a stock prediction web app in Python using streamlit, Yahoo finance, and Facebook Prophet.

In this course we implement the most popular Machine Learning algorithms from scratch using only Python and NumPy.

In this Tutorial I share quick Python Automation Ideas that you can use everyday to simplify your life.

In this tutorial, I show you how I edit my videos using Python and MoviePy

Learn about cron jobs and how to schedule commands and Python scripts in the terminal via crontab (for Linux and Mac)

Learn how to create a website blocker that automates the site blocking for us and helps us to be more productive.

How to setup Jupyter Notebook in a conda environment and then install the kernel

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch.

Implement the famous Snake game in this beginner tutorial and learn how to get started with pygame.

In this PyTorch Tutorial we learn how to use a Learning Rate (LR) Scheduler to adjust the LR during training.

In this Docker Tutorial I show how to get started with Docker for your Python Scripts and Python Web Apps.

In this Beginner Object Oriented Programming (OOP) Tutorial I will be covering all the fundamentals about classes, objects, and inheritance in Python.

In this Tutorial we have a look at some of its key features and then we build our first web application with it.

In this part we learn about the softmax function and the cross entropy loss function.

Implement a Recurrent Neural Net (RNN) in Tensorflow! RNNs are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.

I this tutorial I share 5 Beginner Machine Learning projects with you and give you tips how to solve all of them.

In this part we improve our model from last time to classify Lego Star Wars Minifigures by using Transfer Learning.

In this part we use a real image dataset from Kaggle with Lego Star Wars Minifigures and do a full project walkthrough.

In this part we learn we can use the functional API and the advantages of this approach.

In this part we learn how we can save and load our model.

In this part we build our first convolutional neural network using the keras Sequential API.

In this part we implement a full project with a Regression problem.

In this part we build our first deep neural network using the keras Sequential API.

In this part I show you how to use tensors. Tensors are the central object in the TensorFlow library.

In this part I show you how to install TensorFlow on Mac, Windows, and Linux. And I also show you how to get GPU support.

In this tutorial we build an interactive deep learning app with Streamlit and PyTorch to apply style transfer.

Learn how to use the Python Debugger using the breakpoint() function in this Tutorial.

Learn how to use the interactive mode in Python.

Support me on Patreon and get access to exclusive contents.

Implement a Recurrent Neural Net (RNN) in PyTorch! Learn how we can use the nn.RNN module and work with an input sequence.

freeCodeCamp.org released my 6-hour intermediate Python course.

Implement a Recurrent Neural Net (RNN) from scratch in PyTorch! I briefly explain the theory and different kinds of applications of RNNs. Then we implement a RNN to do name classification.

In this Tutorial we learn about this framework and how we can convert our PyTorch code to a Lightning code.

I show you my minimal Visual Studio Code Setup for Python Programming. I use only 5 Extensions. It's simple but allows me to be really productive.

Learn NumPy in this complete Crash Course! I show you all the essential functions of NumPy and some tricks and useful methods.

Create and Deploy your first Deep Learning app! In this PyTorch tutorial we learn how to deploy our PyTorch model with Flask and Heroku.

Implement the famous Snake game in this beginner tutorial using the curses module!

I show 11 Tips and Tricks to Write Better Python code! I show a lot of best practices that improve your code by making your code much cleaner and more Pythonic.

Learn how to write a TODO App with Flask in this Crash Course.

In this tutorial we build a simple chatbot in PyTorch. I will also provide an introduction to some basic Natural Language Processing (NLP) techniques.

In this tutorial we build an interactive machine learning app with Streamlit and Scikit-learn to explore different datasets and classifier.

I've rebuilt this site using Publish (Static Site Generator) by John Sundell. The website contains now all of my tutorials and videos.

Build & Deploy a Python web app to schedule Tweets. I'm using Flask, Heroku, the Twitter API, and Google Sheets API for this. You can watch how I build this app step by step from zero to deployment!

Learn how to use Google Sheets API in Python. We are using the gspread module for this.

Implement the LDA algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

In this Python Tutorial, I want to show you how to work with TinyDB. TinyDB is a tiny, document oriented database which is perfect for small personal projects.

The common data format in Machine Learning is a CSV file (comma separated values). In this Tutorial I show 4 different ways how you can load the data from such files and then prepare the data.

In this Python Tutorial, we will be learning about Regular Expressions (or RE, regex) in Python. Regular expressions are a powerful language for matching text patterns.

In this part we will learn how to save and load our model. I will show you the different functions you have to remember, and the different ways of saving our model.

In this part we will learn about the TensorBoard and how we can use it to visualize and analyze our models.

A complete study plan to become a Machine Learning Engineer with links to all FREE resources.

Implement the AdaBoost algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

I created a series on YouTube where I explain polular Machine Learning algorithms and implement them from scratch using only built-in Python modules and numpy.

Part 4: Analyze youtube statistics in a jupyter notebook.

Part 3: Get in depth statistics for each video with the YouTube Data API.

Part 2: Get a list of all the videos of a channel with the YouTube Data API.

In this Python Tutorial we will be learning how to work with the YouTube Data API and analyze channel statistics.

In this part we will learn about transfer learning and how this can be implemented in PyTorch.

In this Python Tutorial I show you the new assignment expression also known as the walrus operator. This Python feature is new in Python 3.8.

In this part we will implement our first convolutional neural network (CNN) that can do image classification based on the famous CIFAR-10 dataset.

In this Python Tutorial I show you how you can add a Progress Bar to your Python code in just one line of code!

In this part we will implement our first multilayer neural network that can do digit classification based on the famous MNIST dataset.

In this Python Tutorial we will be learning about Lists Comprehension in Python. List comprehension provides a simple and concise way to create lists.

In this part we learn about activation functions in neural nets. What are activation functions, why are they needed, and how do we apply them in PyTorch.

Scrape the IMDb Top 250 movies and let Python choose a movie for you! Learn how to use requests and BeautifulSoup to scrape websites.

In this part we learn about the softmax function and the cross entropy loss function.

Learn how we can automatically scrape and download images from Google Images with Python.

In this part we learn how we can use dataset transforms together with the built-in Dataset class. Apply built-in transforms to images, arrays, and tensors, or write your own.

In this part we see how we can use the built-in Dataset and DataLoader classes and improve our pipeline with batch training.

In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far.

In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far.

In this part we improve the code from the last part and will learn how a complete training pipeline is implemented in PyTorch.

In this part we will learn how we can use the autograd engine in practice. First we will implement Linear regression from scratch, and then we will learn how PyTorch can do the gradient calculation for us.

In this part I will explain the famous backpropagation algorithm. I will explain all the necessary concepts and walk you through a concrete example.

In this part we learn how to calculate gradients using the autograd package in PyTorch.

This part covers the basics of Tensors and Tensor operations in PyTorch. Learn also how to convert from numpy data to PyTorch tensors and vice versa!

New Tutorial series about PyTorch! Learn all the basics you need to get started with this deep learning framework! Part 1: Installation.

In this Tutorial I show you how you can install and use Anaconda.

Implement a K-Means algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Implement a PCA algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Implement a Random Forest algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Part 2 contains the implementation of a Decision Tree algorithm using only built-in Python modules and numpy.

Part 1 will cover the theory of Decision Trees.

Implement a SVM (Support Vector Machine) algorithm using only built-in Python, and learn about the math behind this popular ML algorithm. modules and numpy

Implement a single-layer Perceptron algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Implement the Naive Bayes algorithm, using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

In this Machine Learning from Scratch Tutorial, we are going to refactor the code from the previous two videos. We will implement Linear and Logistic Regression in only 60 lines.

Implement the Logistic Regression algorithm, using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Implement the Linear Regression algorithm, using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

Context managers are a great tool for resource management. They allow you to allocate and release resources precisely when you want to.

Tutorial about shallow vs deep copying in Python and the `copy` module.

This tutorial covers the asterisk sign (`*`) and its different use cases in Python.

In this article we will talk about function parameters and function arguments in detail.

In this tutorial we talk about how to use the `multiprocessing` module in Python.

In this tutorial we talk about how to use the `threading` module in Python.

Overview and comparison of threads and processes, and how to use it in Python.

Generators are functions that can be paused and resumed on the fly, returning an object that can be iterated over.

A decorator is a function that takes another function and extends the behavior of this function without explicitly modifying it.

Python defines a set of functions that are used to generate or manipulate random numbers. This tutorial covers the random module.

JSON (JavaScript Object Notation) is a leightweight data format for data exchange.

The logging module in Python is a powerful built-in module so you can quickly add logging to your application.

A Python program terminates as soon as it encounters an error. In Python, an error can be a syntax error or an exception.

A lambda function is a small (one line) anonymous function that is defined without a name.

The Python itertools module is a collection of tools for handling iterators. Simply put, iterators are data types that can be used in a for loop.

The collections module in Python implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers, dict, list, set, and tuple.

A string is a sequence of characters. String literals in Python are enclosed by either double or single quotes.

A Set is an unordered collection data type that is unindexed, mutable, and has no duplicate elements. Sets are created with braces.

A dictionary is a collection which is unordered, changeable and indexed. A dictionary consists of a collection of key-value pairs.

A tuple is a collection of objects which is ordered and immutable. Tuples are similar to lists, the main difference ist the immutability.

List is a collection data type which is ordered and mutable. Unlike Sets, Lists allow duplicate elements.