IBM Introduction to Machine Learning 专项课程
Learn machine learning through real use cases. Build the skills for a career in one of the most relevant fields of modern AI through hands-on projects and curriculum from IBM’s experts.
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您将学到的内容有
Understand the potential applications of machine learning
Gain technical skills like SQL, machine learning modelling, supervised and unsupervised learning, regression, and classification.
Identify opportunities to leverage machine learning in your organization or career
Communicate findings from your machine learning projects to experts and non-experts
您将获得的技能
关于此 专项课程
应用的学习项目
In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.
You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.
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。
此专项课程包含 4 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 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 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.
非监督式学习
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
完成专项课程后我会获得大学学分吗?
Can I just enroll in a single course?
我可以只注册一门课程吗?
Can I take the course for free?
我可以免费学习课程吗?
此课程是 100% 在线学习吗?是否需要现场参加课程?
What is machine learning?
What careers can I pursue in the field of machine learning?
How long does it take to complete the Specialization?
完成专项课程需要多长时间?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
完成专项课程后我会获得大学学分吗?
还有其他问题吗?请访问 学生帮助中心。