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。

您将获得的技能: Exploratory Data Analysis, Deep Learning, Plot (Graphics), Artificial Neural Networks, Matplotlib, Data Cleansing, Data Analysis, Tensorflow, Natural Language Processing, Data Processing, Data Manipulation, Python Programming, Machine Learning
初级 · 指导项目 · 不超过 2 小时

您将获得的技能: Applied Machine Learning, Classification And Regression Tree (CART), Predictive Modeling, Microsoft Azure, No-Code Development, Machine Learning, Feature Engineering, Data Pipelines, Data Science, Data Analysis, Data Processing, Application Deployment
初级 · 指导项目 · 不超过 2 小时

您将获得的技能: Scikit Learn (Machine Learning Library), Applied Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning, Data Analysis
初级 · 指导项目 · 不超过 2 小时

您将获得的技能: Data Cleansing, Data Processing, Pandas (Python Package), Applied Machine Learning, Data Import/Export, Classification And Regression Tree (CART), Data Mining, Python Programming, Google Cloud Platform, Scikit Learn (Machine Learning Library), Machine Learning, Supervised Learning, Machine Learning Algorithms
中级 · 指导项目 · 不超过 2 小时

Coursera
您将获得的技能: Applied Machine Learning, Jupyter, Machine Learning, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis
中级 · 指导项目 · 不超过 2 小时

您将获得的技能: Application Programming Interface (API), Microsoft Azure, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), User Accounts, Image Analysis, Artificial Intelligence, Cloud Solutions, Cloud Computing, Software Development
中级 · 指导项目 · 不超过 2 小时

您将获得的技能: Machine Learning Algorithms, Data Visualization, Dashboard, Interactive Data Visualization, Data Visualization Software, Data Presentation, Machine Learning, Scikit Learn (Machine Learning Library), Web Applications, Predictive Modeling, Data Science, Python Programming, Pandas (Python Package)
中级 · 指导项目 · 不超过 2 小时

您将获得的技能: Python Programming, Command-Line Interface, Programming Principles, Computer Programming, Scripting, Scripting Languages, Development Environment
初级 · 指导项目 · 不超过 2 小时

您将获得的技能: Tensorflow, Python Programming, Natural Language Processing, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Machine Learning, Data Processing
中级 · 指导项目 · 不超过 2 小时

您将获得的技能: AWS SageMaker, AWS Identity and Access Management (IAM), Image Analysis, Amazon Elastic Compute Cloud, Amazon S3, Applied Machine Learning, Application Deployment, Machine Learning Algorithms, Computer Vision, Deep Learning, Machine Learning
高级设置 · 指导项目 · 不超过 2 小时

Coursera
您将获得的技能: Regression Analysis, NumPy, Applied Machine Learning, Supervised Learning, Machine Learning, Predictive Modeling, Deep Learning, Data Science, Python Programming
中级 · 指导项目 · 不超过 2 小时

Coursera
您将获得的技能: PyTorch(机器学习库), 深度学习, Python 程序设计, 生成模型架构, 图像分析
中级 · 指导项目 · 不超过 2 小时
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.