Machine Learning With Python
Learning Path ⋅ Skills: Image Processing, Computer Vision, 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, classify sentiment with Keras, and prepare audio data for deep learning with TorchAudio
- 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 ⋅ 31 Resources
Is this path right for you? Mentor AI knows what’s in it and can help you decide where to begin.
Ask Mentor AI
Preparing Your Environment
Machine learning work starts with a working numerical computation stack, not with a model. You’ll set up Python for machine learning on Windows using the Anaconda distribution, so the libraries the rest of this path relies on are ready to import. With that in place, you’ll go straight to seeing machine learning applied to real projects.
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.
Interactive Quiz
Setting Up Python for Machine Learning on Windows
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 in Python With OpenAI's GPT Image API
Learn how to generate images from text prompts with OpenAI's GPT Image API in Python, tune their size and quality, and edit them with a follow-up prompt.
Interactive Quiz
Generate Images in Python With OpenAI's GPT Image 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.
Interactive Quiz
Python AI: How to Build a Neural Network & Make Predictions
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()
Machine Learning Toolkit
You built a neural network from scratch and learned how to split data so your results hold up. Real projects lean on a deep learning framework to do that work for you. You’ll compare PyTorch and TensorFlow and pick the one you’ll carry into the image, text, and audio projects ahead.
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.
Exploring Computer Vision
With a framework chosen, you’ll point it at images. You’ll work with image processing and computer vision in Python, covering 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
Learn OpenCV color segmentation in Python. Convert an image between color spaces, then use an HSV range to segment an object by its color.
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.
