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Machine Learning courses

Machine learning courses cover algorithms and concepts for enabling computers to learn from data and make decisions without explicit programming. Build your skills in NLP, deep learning, MLOps and more.

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Recommended for Machine Learning beginners

Build your Machine Learning skills with interactive courses, curated by real-world experts

课程

理解机器学习

基础技能水平
4.8+
9,542 条评价
2小时
无需编码的机器学习入门。

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课程

使用 scikit-learn 的监督学习

中级技能水平
4.8+
8,424 条评价
4小时
用 Python 中的 scikit-learn 提升你的机器学习技能。 在这门互动课程中使用真实世界数据集，学习如何做出强大的预测！

课程

理解机器学习

基础技能水平
4.8+
9,542 条评价
2小时
无需编码的机器学习入门。

课程

Python 中的无监督学习

中级技能水平
4.8+
1,095 条评价
4小时
学习如何使用 scikit-learn 和 scipy 对无标签数据集进行聚类、转换、可视化并提取洞察。

课程

MLOps 概念

中级技能水平
4.8+
2,564 条评价
2小时
了解 MLOps 如何将机器学习模型从本地笔记本带到生产环境中运行，并创造实际业务价值。

课程

Python 树模型机器学习

中级技能水平
4.8+
716 条评价
5小时
在本课程中，你将学习如何使用 scikit-learn 中的基于树的模型和集成方法进行回归和分类。

课程

Machine Learning for Business

基础技能水平
4.8+
1,645 条评价
2小时
Understand the fundamentals of Machine Learning and how its applied in the business world.

课程

Linear Classifiers in Python

中级技能水平
4.8+
326 条评价
4小时
In this course you will learn the details of linear classifiers like logistic regression and SVM.

课程

Image Processing in Python

中级技能水平
4.8+
210 条评价
4小时
Learn to process, transform, and manipulate images at your will.

课程

Extreme Gradient Boosting with XGBoost

中级技能水平
4.8+
255 条评价
4小时
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

课程

Introduction to MLflow

高级技能水平
4.7+
738 条评价
4小时
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

课程

MLOps Deployment and Life Cycling

高级技能水平
4.7+
852 条评价
4小时
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

课程

Cluster Analysis in Python

中级技能水平
4.8+
959 条评价
4小时
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

课程

End-to-End Machine Learning

中级技能水平
4.7+
341 条评价
4小时
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

课程

Model Validation in Python

中级技能水平
4.8+
833 条评价
4小时
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

课程

CI/CD for Machine Learning

高级技能水平
4.7+
368 条评价
5小时
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

课程

Machine Learning with PySpark

高级技能水平
4.8+
689 条评价
4小时
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

课程

Hyperparameter Tuning in Python

中级技能水平
4.8+
782 条评价
4小时
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

课程

Sentiment Analysis in Python

中级技能水平
4.8+
429 条评价
4小时
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

课程

Dimensionality Reduction in Python

中级技能水平
4.8+
849 条评价
4小时
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

课程

Natural Language Processing with spaCy

中级技能水平
4.7+
576 条评价
4小时
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

课程

Feature Engineering for NLP in Python

高级技能水平
4.8+
142 条评价
4小时
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

课程

Machine Learning for Finance in Python

中级技能水平
4.8+
208 条评价
4小时
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

课程

Monitoring Machine Learning Concepts

中级技能水平
4.8+
455 条评价
2小时
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Machine Learning 相关资源

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博客

How to Become a Machine Learning Engineer in 2026

Learn how to become a machine learning engineer and discover why it is one of the most lucrative and dynamic career paths in the data world.
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Kurtis Pykes

15分钟

博客

33 Machine Learning Projects for All Levels in 2026

Machine learning projects for beginners, final year students, and professionals. The list consists of guided projects, tutorials, and example source code.
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Abid Ali Awan

15分钟

博客

Top 12 Machine Learning Engineer Skills To Start Your Career

Master these skills to become a job-ready machine learning engineer in 2024.
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Natassha Selvaraj

11分钟

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    Frequently asked questions

    Is machine learning easy to learn?

    DataCamp's beginner machine learning courses are a lot of hands-on fun, and they provide an excellent foundation for machine learning to advance your career or business. Within weeks, you'll be able to create models and generate predictions and insights. You'll also learn foundational knowledge of Python and R and the fundamentals of artificial intelligence.

    After that, the learning curve gets a bit steeper. Machine learning careers require a deeper understanding of statistics, math, and software engineering, all of which can be mastered at DataCamp.

    What is machine learning used for?

    In a nutshell, machine learning is a type of artificial intelligence whose algorithms, as they acquire data, produce analytical models and make predictions with little to no human intervention.

    It's difficult to find an industry that doesn't use machine learning. For example, marketers use machine learning to forecast returns on investments in marketing campaigns. Likewise, purchasing departments use machine learning to predict needed inventory.

    Businesses of all kinds use machine learning to predict customer behavior, map supply chains, and forecast revenues. Machine learning is used to predict health outcomes and to improve patient satisfaction. Machine learning helps scientists model climate change scenarios, including possible solutions.

    More specifically, machine learning is used in smart devices, search engines, and streaming services (when Netflix suggests a show or movie based on your viewing history, that's machine learning).

    What jobs can you get with machine learning skills?

    Machine learning skills are valuable in programming, data science, and other computer engineering disciplines. In addition, machine learning is a must for anyone wanting to work in robotics!

    Not all jobs that require machine learning are in tech though. For example, linguists use machine learning to track ever-changing languages and dialects. In addition, business departments, such as marketing, accounting, logistics, and purchasing, to name a few, increasingly need machine learning experts to help them make informed business decisions. Knowing machine learning can give you a step up in nearly any position, as modeling and predicting are critical business needs.

    Are machine learning skills in demand?

    Yes, machine learning skills are in high demand. According to a report by the World Economic Forum, demand for AI and ML specialists is expected to grow by 40% between 2023 and 2027.

    How much math do I need to take a machine learning course?

    If you're looking to develop a high-level understanding of machine learning concepts, you don't need much math. If you want to dive deeper and make machine learning your career (as opposed to an added value to your existing career), a foundation in statistics and algebra is helpful. If you don't have a mathematical background, that's okay. We'll teach you everything you need, and our instructors are a lot less scary than your high school calculus teacher.

    Do I need to download machine learning software to learn on DataCamp?

    You do not need to download anything while learning with DataCamp. All the tools we use are web-based.

    其他技术和主题

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