Machine Learning With Python

Learning PathSkills: Image Processing, Text Classification, Speech Recognition

Python Machine Learning Artwork

Machine learning is a field of computer science that uses statistical techniques to give computer programs the ability to learn from past experiences and improve how they perform specific tasks.

With this learning path, you’ll sample a range of common machine learning scenarios using Python.

Additional Resources

Machine Learning With Python

Learning Path ⋅ 10 Resources

A Basic Python Setup for Machine Learning on Windows

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.

Python Face Recognition and Face Detection

Course

Traditional Face Detection With 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.

Color Spaces and How to Use Them With OpenCV and Python

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.

Linear Regression in Python

Tutorial

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.

Practical Text Classification With Python and Keras

Tutorial

Practical Text Classification With Python and Keras

Learn about Python text classification with Keras. Work your way from a bag-of-words model with logistic regression to more advanced methods leading to convolutional neural networks. See why word embeddings are useful and how you can use pretrained word embeddings. Use hyperparameter optimization to squeeze more performance out of your model.

Split Your Dataset With scikit-learn's train_test_split()

Tutorial

Split Your Dataset With scikit-learn's 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.

Python Speech Recognition

Tutorial

The Ultimate Guide To Speech Recognition With Python

An in-depth tutorial on speech recognition with Python. Learn which speech recognition library gives the best results and build a full-featured "Guess The Word" game with it.

PyTorch vs Tensorflow for Your Python Deep Learning Project

Tutorial

PyTorch vs TensorFlow for Your Python Deep Learning Project

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.

Generative Adversarial Networks: Build Your First Models

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.

K-Means Clustering in Python: A Practical Guide

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.

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