Learning Path ⋅ Skills: Image Processing, Text Classification, Speech Recognition, NLP, Deep Learning, LLMs, RAG
This learning path covers practical machine learning with Python, from image processing and computer vision to Natural Language Processing (NLP) and modern LLM-based workflows.
You’ll build ML models with scikit-learn, work with neural networks in PyTorch and TensorFlow, and explore NLP using NLTK and spaCy. You’ll also learn how to use LLMs through cloud APIs, local models with Ollama, and RAG pipelines with LangChain and LlamaIndex.
Learning Path ⋅ 30 Resources
Set yourself up for success on your machine learning journey. This section prepares your environment for a seamless developing and learning experience.
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.
See machine learning in action through real-world projects. You’ll generate images with DALL-E and build a recommendation engine using collaborative filtering.
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.
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. You’ll learn about linear regression, neural networks, and how to properly split and prepare datasets.
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.
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.
Work with image processing and computer vision in Python. You’ll cover topics from basic image manipulation to face detection and face recognition.
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.
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.
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!
Explore Natural Language Processing (NLP) in Python. You’ll perform sentiment analysis, text classification, and work with libraries like NLTK and spaCy.
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.
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.
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.
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.
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.
Learn how to work with large language models (LLMs) and retrieval-augmented generation (RAG) in Python. You’ll use prompt engineering, vector databases, LangChain, and both cloud and local LLM APIs.
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.
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.
Course
Large language models (LLMs) have taken the world by storm. In this step-by-step video course, you'll learn to use the LangChain library to build LLM-assisted applications.
Learn how to choose between PyTorch and TensorFlow for your deep learning projects. This section compares these industry-standard tools.
Get hands-on experience with specific machine learning algorithms, including k-nearest neighbors (kNN), K-means clustering, and generative adversarial networks (GANs).
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.
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.
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.