自动学徒课程可帮助您了解如何构建、生成和分析预判定模型。您可以培养在数据准备、算法选择、优化和评估方面的能力。许多课程利用有源数据库测试模型。

多位教师
您将获得的技能: 预测建模, 人工智能, 监督学习, 无监督学习, 张力流, Scikit-learn (机器学习库), NumPy, 机器学习, 人工智能和机器学习(AI/ML), Jupyter, 决策树学习, 深度学习, Python 程序设计, 应用机器学习, 强化学习, 随机森林算法, 数据伦理, 功能工程, 负责任的人工智能, 分类与回归树 (CART)
初级 · 专项课程 · 1-3 个月

DeepLearning.AI
您将获得的技能: 回归分析, 人工智能, 监督学习, 预测建模, Scikit-learn (机器学习库), NumPy, 机器学习, Python 程序设计, Jupyter, 数据转换, 统计建模, 应用机器学习, 功能工程, 分类与回归树 (CART)
初级 · 课程 · 1-4 周

您将获得的技能: 数据操作, 计算机编程, 数据处理, Pandas(Python 软件包), 自动化, JSON, 数据导入/导出, NumPy, 面向对象编程(OOP), 数据结构, 还原式 API, 编程原则, Python 程序设计, 数据分析, 脚本, Jupyter, 网页抓取, 应用编程接口 (API)
初级 · 课程 · 1-3 个月

Imperial College London
您将获得的技能: 回归分析, 数据操作, 机器学习算法, 应用数学, 衍生产品, 数据科学, 统计分析, 概率与统计, 微积分, 人工神经网络, 无监督学习, 降维, NumPy, Python 程序设计, Algorithm, Jupyter, 高等数学, 统计, 线性代数
初级 · 专项课程 · 3-6 个月

IBM
您将获得的技能: 商业智能, 风险缓解, 生成式人工智能, 负责任的人工智能, 自然语言处理, 内容创作
初级 · 课程 · 1-4 周
University of London
您将获得的技能: 人工智能, 数据处理, 监督学习, 机器学习, 功能工程, 计算机视觉, 数据收集, 数据分析, 应用机器学习, 深度学习
攻读学位
初级 · 课程 · 1-4 周

IBM
您将获得的技能: 人工智能, 预测建模, 监督学习, 数据科学, 无监督学习, 机器学习, 深度学习, 性能指标, 强化学习, 分类与回归树 (CART)
初级 · 课程 · 1-4 周

Amazon Web Services
您将获得的技能: MLOps (Machine Learning Operations), AWS SageMaker, Amazon Web Services, Machine Learning, Applied Machine Learning, Predictive Modeling
初级 · 课程 · 1-4 周

您将获得的技能: Prompt Engineering, Exploratory Data Analysis, Data Wrangling, Prompt Patterns, LangChain, Large Language Modeling, Unsupervised Learning, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, Supervised Learning, Keras (Neural Network Library), Data Transformation, Feature Engineering, Flask (Web Framework), Data Analysis, Responsible AI, LLM Application, Data Import/Export
初级 · 专业证书 · 3-6 个月

IBM
您将获得的技能: 探索性数据分析, 数据可视化软件, 监督学习, SQL, 无监督学习, 专业网络, Plotly, 生成式人工智能, 数据整理, 数据清理, 数据导入/导出, 仪表板, 数据分析, 数据转换, 交互式数据可视化, 数据可视化, Jupyter, 数据扫盲, 同行评审, 功能工程
攻读学位
初级 · 专业证书 · 3-6 个月

O.P. Jindal Global University
您将获得的技能: Supervised Learning, Tensorflow, Image Analysis, Artificial Neural Networks, Scikit Learn (Machine Learning Library), Python Programming, Machine Learning, Deep Learning, Unstructured Data, NumPy, Matplotlib, Natural Language Processing, Text Mining, Pandas (Python Package), Regression Analysis, Performance Tuning
攻读学位
初级 · 课程 · 1-3 个月

DeepLearning.AI
您将获得的技能: 人工智能, 数据科学, 人工神经网络, 战略思维, AI 产品战略, 机器学习, 负责任的人工智能, 数据伦理, 深度学习
初级 · 课程 · 1-4 周
Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It is important because it drives innovation across various sectors, from healthcare to finance, by automating processes and providing insights that were previously unattainable. As industries increasingly rely on data-driven decision-making, understanding machine learning becomes essential for staying competitive.
A variety of job opportunities exist in the field of machine learning. Positions include machine learning engineer, data scientist, AI researcher, and business intelligence analyst. These roles often require a blend of programming skills, statistical knowledge, and domain expertise. As organizations continue to adopt machine learning technologies, the demand for skilled professionals in this area is expected to grow.
To learn machine learning effectively, you should focus on several key skills. Proficiency in programming languages such as Python or R is crucial, along with a solid understanding of statistics and linear algebra. Familiarity with data manipulation and visualization tools, as well as experience with machine learning frameworks like TensorFlow or PyTorch, will also be beneficial. These skills will provide a strong foundation for your machine learning journey.
There are many excellent online resources for learning machine learning. Notable options include the IBM Machine Learning Professional Certificate and the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These programs offer structured learning paths and hands-on projects to help you build practical skills.
Yes. You can start learning Machine Learning on Coursera for free in two ways:
If you want to keep learning, earn a certificate in Machine Learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.
To learn machine learning, start by taking introductory courses that cover the basics of algorithms and data analysis. Engage in hands-on projects to apply what you've learned, and gradually progress to more advanced topics. Utilize online resources, participate in forums, and collaborate with peers to enhance your understanding. Consistent practice and real-world application will reinforce your skills.
Typical topics covered in machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, and neural networks. Additionally, courses often explore data preprocessing, feature engineering, and model evaluation. Understanding these concepts will equip you with the knowledge needed to tackle various machine learning challenges.
For training and upskilling employees in machine learning, programs like the Applied Machine Learning Specialization are highly effective. These courses focus on practical applications and real-world scenarios, making them suitable for professionals looking to enhance their skills and contribute to their organizations' data-driven initiatives.