- Data Science
- Deep Learning
- Artificial Intelligence (AI)
- Machine Learning
- Python Programming
- Feature Engineering
- Statistical Hypothesis Testing
- Exploratory Data Analysis
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
IBM Machine Learning 专业证书
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.
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您将获得的技能
关于此 专业证书
应用的学习项目
This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:
Tools: Jupyter Notebooks and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.
需要一些相关领域经验。需要一些相关经验。
需要一些相关领域经验。需要一些相关经验。
专业证书是什么?
塑造技能,做好工作准备
无论您是想开始新的职业生涯,还是改变目前职业,Coursera 专业证书都能帮您为开始工作做好准备。选择最适合的时间和地点,自行安排学习进度。立即注册,探索新的职业道路,可免费试用 7 天。您可以随时暂停学习或结束订阅。
实践项目
将您的技能应用到实践项目,并丰富您的简历内容,进而向潜在雇主展示您已为开始工作做好准备。您需要成功完成项目以获得证书。
获得职业证书
当完后计划中的所有课程后,您将获得一张证书。您可以将其在专业网络上分享,并获得使用职业支持资源的权限,这能够为您开启职业生涯提供助力。许多招聘合作伙伴认可我们的许多专业证书,并且我们还有许多合作伙伴可以帮助您准备认证考试。您可以在适用的各个专业证书页面上找到更多信息。

此专业证书包含 6 门课程
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
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IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
常见问题
退款政策是如何规定的?
我可以只注册一门课程吗?
完成专项课程需要多长时间?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
完成专项课程后我会获得大学学分吗?
What will I be able to do upon completing the Specialization?
此课程是 100% 在线学习吗?是否需要现场参加课程?
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