# IBM AI Engineering 專業證書

Launch your career as an AI engineer. Learn how to provide business insights from big data using machine learning and deep learning techniques.

提供方

### 您將學到的內容有

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn

Deploy machine learning algorithms and pipelines on Apache Spark

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

## 您將獲得的技能

## 關於此 專業證書

## 應用的學習項目

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes.

需要一些相關領域經驗。需要一些相關經驗。

需要一些相關領域經驗。需要一些相關經驗。

### 此專業證書包含 6 門課程

### 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.

### 提供方

#### IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.

## 常見問題

完成专项课程后我会获得大学学分吗？

此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校，了解详情。Coursera 上的在线学位和 Mastertrack™ 证书提供获得大学学分的机会。

Can I just enroll in a single course?

如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。

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。

What are some examples of careers in artificial intelligence?

An understanding of artificial intelligence can be used to support many careers, but some careers specifically require a background in AI. Some examples of careers in AI include:

- AI Developer

- Data Analyst

- Data Engineer

- Data Scientist

- Machine Learning Engineer

- Marketing Analyst

- Operations Analyst

- Quantitative Analyst

- Software Analyst

- Software Developer

- Software Engineer

- User Experience Engineer

How long does it take to complete the Professional Certificate?

This Professional Certificate consists of 6 self-paced courses. Each course takes 4-5 weeks to complete if you spend 2-4 hours working through the course per week. At this rate, the entire Professional Certificate can be completed in 3-6 months. However, you are welcome to complete the program more quickly or more slowly, depending on your preference.

What background knowledge is necessary?

This Professional Certificate's pre-requisites includes the following skills:

- Working knowledge of Python and Jupyter Notebooks (Don't have these skills? Try taking the Python for Data Science and course)

- High school mathematics or math for machine learning

It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:

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

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

Will I earn university credit for completing the Professional Certificate?

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

What will I be able to do upon completing the Professional Certificate?

Upon completing this Professional Certificate you will be able to:

- Describe what machine learning (ML), deep learning (DL), and neural networks are
- 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 back propagation 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

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