# IBM AI Engineering 专业证书

Launch Your Career in AI Engineering

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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

By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer. You will also complete a Capstone Project and demonstrate ability to present and communicate outcomes of deep learning projects

#### 可分享的证书

#### 100% 在线课程

#### 灵活的计划

#### 中级

需要一些相关领域经验。需要一些相关经验。

#### 完成时间大约为2 个月

#### 英语（English）

### 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

### 使用 Python 进行机器学习

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

### Scalable Machine Learning on Big Data using Apache Spark

This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.

### Introduction to Deep Learning & Neural Networks with Keras

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.

### Deep Neural Networks with PyTorch

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.

### 关于 IBM

### 审阅

#### 4.4

##### 来自IBM AI ENGINEERING的热门评论

One of the best & most interesting courses in this complete data science specialization. As a naive to this domain, I aquired a ton of insights in Machine Learning.

This was my favorite IBM specialization course, well explained and very interesting.\n\nThis course has inspired me to study Machine Learning more deeply.

Best course till now in the entire specialization.

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

could be split in two courses to be given enough focus. it was very condensed and needed more time and explanation in each section. The instructor was very good but more details would have been nice

The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.

This is a very good start for Machine leaning with Python. I didnt have much idea about ML concepts but this course gave me great understanding on each topic and lot of learning. Awesome Course !!

This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.

## 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？

可以！点击您感兴趣的课程卡开始注册即可开始学习。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某证书的一部分，系统会自动为您订阅完整的证书。访问您的学生面板，跟踪您的进度。

此课程是 100% 在线学习吗？是否需要现场参加课程？

此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

完成专项课程需要多长时间？

This Professional Certificate consists of 6 self-paced courses. Effort required to complete each course is 4-5 weeks if spending 2-4 hours per week. At this rate the entire specialization can be completed in 3-6 months.

What background knowledge is necessary?

Do I need to take the courses in a specific order?

It is highly recommended to complete the courses in the suggested order.

完成专项课程后我会获得大学学分吗？

At this time there is no university credit for completing courses in this specialization.

What will I be able to do upon completing the Specialization?

Upon completing this Professional Certificate you will be able to:

- Describe what is Machine Learning (ML), Deep Learning (DL) & Neural Networks
- Explain ML algorithms including Classification, Regression, Clustering, and Dimensional Reduction
- Implement Supervised and Unsupervised ML models using scipy and scikitlearn
- Express how Apache Spark works and how to perform Machine Learning on Big Data
- Deploy ML Algorithms and Pipelines on Apache Spark
- Demonstrate an understanding of Deep Learning models such as autoencoders, restricted Boltzmann machines, convolutional networks, recursive neural networks, and recurrent networks
- Build deep learning models and neural networks using the Keras library
- Utilize the PyTorch library for Deep Learning applications and build Deep Neural Networks
- Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
- Apply deep learning using TensorFlow and perform backpropagation to tune the weights and biases
- Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
- Demonstrate ability to present and communicate outcomes of deep learning projects

还有其他问题吗？请访问 学生帮助中心。