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Machine Learning With Python (Learning Path) – Real Python

Learning Path ⋅ Skills: Image Processing, Text Classification, Speech Recognition

This comprehensive course provides practical skills in Python-based machine learning, covering varied areas such as image processing, text classification, and speech recognition.

The curriculum delves into computer vision, Natural Language Processing (NLP), and applications with AI models like DALL-E2 and ChatGPT. You’ll also explore common tools, such as PyTorch, TensorFlow, and Keras, and gain a hands-on understanding of important machine learning algorithms.

Whether a beginner or a seasoned programmer, this course is a robust guide to transform your theoretical knowledge into practical expertise in Python machine learning. You’ll be at the forefront of technological innovation, unlocking new ways to interact with the digital world. Time to start your learning adventure!

Learning Path ⋅ 26 Resources

Set yourself up for success on your Machine Learning journey. This section prepares your environment for a seamless developing and learning experience.

#1

Tutorial

In this step-by-step tutorial, you’ll cover the basics of setting up a Python numerical computation environment for machine learning on a Windows machine using the Anaconda Python distribution.

Discover a range of exciting real-world Machine Learning applications. This section will guide you through specific projects involving DALL-E2, ChatGPT, and more.

#2

Tutorial

Learn to use the OpenAI Python library to create images with DALL·E, a state-of-the-art latent diffusion model. In this tutorial, you'll explore creating images and generating image variations. You'll also interact with DALL·E using API calls and incorporate this functionality into your Python scripts.

#3

#4

Tutorial

In this tutorial, you'll get your hands dirty with GitHub Copilot, a virtual pair programmer powered by artificial intelligence trained on billions of lines of code. You'll explore several practical use cases in Python for this amazing productivity tool.

#5

Tutorial

Learn about collaborative filtering, which is one of the most common approaches for building recommender systems. You'll cover the various types of algorithms that fall under this category and see how to implement them in Python.

Understand the core principles of Machine Learning. This section delves into basics such as linear regression, Neural Networks, and how to handle datasets.

#6

Course

Get started with linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.

#7

Course

In this step-by-step course, you'll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You'll learn how to train your neural network and make accurate predictions based on a given dataset.

#8

Unleash the power of image processing and Computer Vision. Cover topics from basic image processing to complex tasks like face detection and face recognition.

#9

#10

Tutorial

In this introductory tutorial, you'll learn how to simply segment an object from an image based on color in Python using OpenCV. A popular computer vision library written in C/C++ with bindings for Python, OpenCV provides easy ways of manipulating color spaces.

#11

Course

In this course on face detection with Python, you'll learn about a historically important algorithm for object detection that can be successfully applied to finding the location of a human face within an image.

#12

Tutorial

In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish!

Investigate the realm of Natural Language Processing (NLP) and Natural Language Generation (NLG). Learn how to perform tasks such as sentiment analysis, text classification and more.

#13

#14

Tutorial

In this beginner-friendly tutorial, you'll take your first steps with Natural Language Processing (NLP) and Python's Natural Language Toolkit (NLTK). You'll learn how to process unstructured data in order to be able to analyze it and draw conclusions from it.

#15

Tutorial

Learn how to use spaCy. This free and open-source library for natural language processing (NLP) in Python has a lot of built-in capabilities and is becoming increasingly popular for processing and analyzing data in NLP.

#16

#17

Course

In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll also see how you can use pretrained word embeddings and hyperparameter optimization.

#18

Tutorial

Chatbots can help to provide real-time customer support and are a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.

#19

Tutorial

Learn prompt engineering techniques with a practical, real-world project to get better results from large language models. This tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

#20

Tutorial

Vector databases are a crucial component of many NLP applications. This tutorial will give you hands-on experience with ChromaDB, an open-source vector database that's quickly gaining traction. Along the way, you'll learn what's needed to understand vector databases with practical examples.

#21

Course

See the fundamentals of speech recognition with Python. You'll learn which speech recognition library gives the best results and build a full-featured "Guess The Word" game with it.

Get to know how to choose the right tools like PyTorch and TensorFlow for your Machine Learning projects. This section offers insights on industry-standard tools.

#22

Dive headfirst into specific Machine Learning algorithms. Get hands-on experience with k-Nearest Neighbors (kNN), K-Means clustering, and Generative Adversarial Networks (GANs).

#23

Course

Learn all about the k-nearest neighbors (kNN) algorithm in Python, including how to implement kNN from scratch. Once you understand how kNN works, you'll use scikit-learn to facilitate your coding process.

#24

Tutorial

Learn how to perform k-means clustering in Python. You'll review evaluation metrics for choosing an appropriate number of clusters and build an end-to-end k-means clustering pipeline in scikit-learn.

#25

Podcast

What is it like to design a Python library for three different audiences? This week on the show, we have Nir Aides, creator of Jupylet. His new library is designed for deep reinforcement learning researchers, musicians interested in live music coding, and kids interested in learning to program. Everything is designed to run inside of a Jupyter notebook.

#26

Tutorial

Learn all about one of the most exciting areas of research in the field of machine learning: generative adversarial networks. You'll learn the basics of how GANs are structured and trained before implementing your own generative model using PyTorch.

Got feedback on this learning path?

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