Machine Learning With Python
Learning Path ⋅ Skills: Image Processing, Text Classification, Speech Recognition, NLP, Deep Learning, LLMs, RAG
Machine learning lets you build systems that learn from data. This learning path walks you through practical machine learning with Python, from classical algorithms to modern LLM-powered workflows.
By completing this path, you’ll be able to:
- Train models with scikit-learn using techniques like linear regression, k-nearest neighbors, and k-means clustering
- Process images with Pillow and OpenCV, and build face detection and recognition tools
- Analyze text with NLTK and spaCy, and classify sentiment using Keras and PyTorch
- Work with LLMs through cloud APIs, local models with Ollama, and Hugging Face Transformers
- Build RAG pipelines using LangChain, LlamaIndex, and ChromaDB
This path is for Python developers who want to add machine learning to their skill set. You should be comfortable with Python basics and ready to work with third-party libraries.
You’ll start with environment setup and core ML principles, move through computer vision and NLP, then finish with LLMs, RAG, and advanced algorithms.
Machine Learning With Python
Learning Path ⋅ 30 Resources
Preparing Your Environment
Set yourself up for success on your machine learning journey. This section prepares your environment for a seamless developing and learning experience.
Tutorial
Setting Up Python for Machine Learning on Windows
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.
Applications of Machine Learning
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
Generate Images With DALL·E and the OpenAI API
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.
Interactive Quiz
Generate Images With DALL·E and the OpenAI API
Tutorial
Build a Recommendation Engine With Collaborative Filtering
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.
Machine Learning Principles
Understand the core principles of machine learning. You’ll learn about linear regression, neural networks, and how to properly split and prepare datasets.
Course
Starting With Linear Regression in Python
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.
Interactive Quiz
Linear Regression in Python
Course
Building a Neural Network & Making Predictions With Python AI
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.
Course
Splitting Datasets With scikit-learn and train_test_split()
Learn why it's important to split your dataset in supervised machine learning and how to do that with train_test_split() from scikit-learn.
Interactive Quiz
Split Your Dataset With scikit-learn's train_test_split()
Exploring Computer Vision
Work with image processing and computer vision in Python. You’ll cover topics from basic image manipulation to face detection and face recognition.
Course
Process Images Using the Pillow Library and Python
Learn how to use the Python Pillow library to deal with images and perform image processing. You'll also explore using NumPy for further processing, including to create animations.
Tutorial
Image Segmentation Using Color Spaces in OpenCV + Python
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
Traditional Face Detection Using Python
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
Build Your Own Face Recognition Tool With Python
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!
Natural Language Processing
Explore Natural Language Processing (NLP) in Python. You’ll perform sentiment analysis, text classification, and work with libraries like NLTK and spaCy.
Tutorial
Sentiment Analysis: First Steps With Python's NLTK Library
Learn how to work with Python's Natural Language Toolkit (NLTK) to process and analyze text. You'll also learn how to perform sentiment analysis with built-in as well as custom classifiers!
Tutorial
Natural Language Processing With Python's NLTK Package
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
Natural Language Processing With spaCy in Python
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.
Tutorial
Use Sentiment Analysis With Python to Classify Movie Reviews
Learn about sentiment analysis and how it works in Python. You'll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative.
Course
Learn Text Classification With Python and Keras
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
ChatterBot: Build a Chatbot With Python
Build a Python chatbot with the ChatterBot library. Clean real conversation data, train on custom datasets, and add local AI with Ollama.
Course
Speech Recognition With Python
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.
Tutorial
Use TorchAudio to Prepare Audio Data for Deep Learning
Learn to prepare audio data for deep learning in Python using TorchAudio. Explore how to load, process, and convert speech to spectrograms with PyTorch tools.
Interactive Quiz
Use TorchAudio to Prepare Audio Data for Deep Learning
LLMs and RAG
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
Prompt Engineering: A Practical Example
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.
Interactive Quiz
Practical Prompt Engineering
Course
Vector Databases and Embeddings With ChromaDB
Learn how to use ChromaDB, an open-source vector database, to store embeddings and give context to large language models in Python.
Course
First Steps With LangChain
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.
Interactive Quiz
Build an LLM RAG Chatbot With LangChain
Tutorial
Hugging Face Transformers: Leverage Open-Source AI in Python
As the AI boom continues, the Hugging Face platform stands out as the leading open-source model hub. In this tutorial, you'll get hands-on experience with Hugging Face and the Transformers library in Python.
Interactive Quiz
Hugging Face Transformers
Course
Leverage OpenAI's API in Your Python Projects
Learn how to use the ChatGPT API with Python's openai library to send prompts, control AI behavior with roles, and get structured outputs.
Interactive Quiz
How to Integrate ChatGPT's API With Python Projects
Tutorial
How to Integrate Local LLMs With Ollama and Python
Learn how to integrate your Python projects with local models (LLMs) using Ollama for enhanced privacy and cost efficiency.
Interactive Quiz
How to Integrate Local LLMs With Ollama and Python
Tutorial
Python MCP Server: Connect LLMs to Your Data
Learn how to build a Model Context Protocol (MCP) server in Python. Connect tools, prompts, and data to AI agents like Cursor for smarter assistants.
Interactive Quiz
Python MCP Server: Connect LLMs to Your Data
Tutorial
LlamaIndex in Python: A RAG Guide With Examples
Learn how to set up LlamaIndex, choose an LLM, load your data, build and persist an index, and run queries to get grounded, reliable answers with examples.
Interactive Quiz
LlamaIndex in Python: A RAG Guide With Examples
Machine Learning Toolkit
Learn how to choose between PyTorch and TensorFlow for your deep learning projects. This section compares these industry-standard tools.
Course
Python Deep Learning: PyTorch vs Tensorflow
PyTorch vs Tensorflow: Which one should you use? Learn about these two popular deep learning libraries and how to choose the best one for your project.
Algorithms in Machine Learning
Get hands-on experience with specific machine learning algorithms, including k-nearest neighbors (kNN), K-means clustering, and generative adversarial networks (GANs).
Course
Using k-Nearest Neighbors (kNN) in Python
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
K-Means Clustering in Python: A Practical Guide
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
Generative Adversarial Networks: Build Your First Models
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.
Congratulations on completing this learning path! You’ve covered image processing, NLP, speech recognition, and modern LLM workflows in Python.
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