Przejdź do treści głównej
Kategoria
Tematy

Samouczki z uczenia maszynowego

Poznaj wskazówki i dobre praktyki dotyczące AI i uczenia maszynowego, rozwijaj kompetencje i buduj kulturę pracy z danymi. Dowiedz się z naszych samouczków, jak maksymalnie wykorzystać modele uczenia maszynowego.
Inne tematy:
GroupSzkolenie 2 lub więcej osób?Wypróbuj DataCamp for Business

US Election 2024 Prediction With Machine Learning and Python

Learn how to predict the winner of the 2024 US presidential election using Python, machine learning, and data from FiveThirtyEight and the Federal Election Commission.

Tom Farnschläder

30 października 2024

RMSprop Optimizer Tutorial: Intuition and Implementation in Python

Learn about the RMSprop optimization algorithm, its intuition, and how to implement it in Python. Discover how this adaptive learning rate method improves on traditional gradient descent for machine learning tasks.
Bex Tuychiev's photo

Bex Tuychiev

23 października 2024

How to Visualize Machine Learning Models: From Linear Regression to Neural Networks

Machine learning is complex and often hard to wrap your head around. By visualizing machine learning models, you can get a great level of understanding of model performance and the decisions the model makes when making predictions.
Dario Radečić's photo

Dario Radečić

23 października 2024

A Guide to the DBSCAN Clustering Algorithm

Learn how to implement DBSCAN, understand its key parameters, and discover when to leverage its unique strengths in your data science projects.

Rajesh Kumar

21 stycznia 2026

Isolation Forest Guide: Explanation and Python Implementation

Isolation Forest is an unsupervised machine learning algorithm that identifies anomalies or outliers in data by isolating them through a process of random partitioning within a collection of decision trees.
Conor O'Sullivan's photo

Conor O'Sullivan

25 września 2024

SARSA Reinforcement Learning Algorithm in Python: A Full Guide

Learn SARSA, an on-policy reinforcement learning algorithm. Understand its update rule, hyperparameters, and differences from Q-learning with practical Python examples and its implementation.
Bex Tuychiev's photo

Bex Tuychiev

19 września 2024

Optimization in Python: Techniques, Packages, and Best Practices

This article teaches you about numerical optimization, highlighting different techniques. It discusses Python packages such as SciPy, CVXPY, and Pyomo and provides a practical DataLab notebook to run code examples.
Kurtis Pykes 's photo

Kurtis Pykes

31 sierpnia 2024

Adam Optimizer Tutorial: Intuition and Implementation in Python

Understand and implement the Adam optimizer in Python. Learn the intuition, math, and practical applications in machine learning with PyTorch
Bex Tuychiev's photo

Bex Tuychiev

29 sierpnia 2024

What is Boosting?

Boosting improves machine learning performance by sequentially correcting errors and combining weak learners into strong predictors.
Vinod Chugani's photo

Vinod Chugani

16 sierpnia 2024

Optuna for Deep Reinforcement Learning in Python

Explore how to master hyperparameter tuning with Optuna. Learn how to define hyperparameters, set up your objective function, and utilize sampling and pruning techniques in deep reinforcement learning.
Bunmi Akinremi's photo

Bunmi Akinremi

7 sierpnia 2024

DeepChecks Tutorial: Automating Machine Learning Testing

Learn how to perform data and model validation to ensure robust machine learning performance using our step-by-step guide to automating testing with DeepChecks.
Abid Ali Awan's photo

Abid Ali Awan

6 sierpnia 2024