Machine Learning With Python
Learning Path ⋅ Skills: Image Processing, Text Classification, Speech Recognition
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.
Machine Learning With Python
Learning Path ⋅ 12 Resources
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.
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.
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.
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.
Starting With Linear Regression in Python
In this video course, you'll 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.
Learn 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.
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.
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.
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
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.
The k-Nearest Neighbors (kNN) Algorithm in Python
Learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging.
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.