课程信息

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第 2 门课程(共 6 门)
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中级
完成时间大约为5 小时
英语(English)
字幕:英语(English)

您将获得的技能

Data ScienceArtificial Intelligence (AI)Machine LearningBig DataSpark
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 6 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为5 小时
英语(English)
字幕:英语(English)

提供方

IBM 徽标

IBM

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 2 小时

Week 1: Introduction

完成时间为 2 小时
6 个视频 (总计 44 分钟), 6 个阅读材料, 2 个测验
6 个视频
What is Big Data?11分钟
Data storage solutions5分钟
Parallel data processing strategies of Apache Spark7分钟
Functional programming basics6分钟
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6分钟
6 个阅读材料
Course Syllabus10分钟
Setup of the grading and exercise environment10分钟
Exercise 1 - working with RDD10分钟
Exercise 2 - functional programming basics with RDDs10分钟
Exercise 3 - working with DataFrames10分钟
Programming Lanuage Options for Apache Spark (optional)10分钟
2 个练习
Practice Quiz (Ungraded) - Apache Spark concepts8分钟
Apache Spark and parallel data processing
2

2

完成时间为 1 小时

Week 2: Scaling Math for Statistics on Apache Spark

完成时间为 1 小时
8 个视频 (总计 52 分钟), 3 个阅读材料, 4 个测验
8 个视频
Averages5分钟
Standard deviation3分钟
Skewness3分钟
Kurtosis2分钟
Covariance, Covariance matrices, correlation13分钟
Plotting with ApacheSpark and python's matplotlib12分钟
Dimensionality reduction4分钟
PCA5分钟
3 个阅读材料
Exercise 1 - statistics and transfomrations using DataFrames10分钟
Exercise on Plotting10分钟
Exercise on PCA10分钟
4 个练习
Practice Quiz (Ungraded) - Statistics and API usage on Spark4分钟
Parallelism in Apache Spark 
Questions on Plotting
Questions on PCA
3

3

完成时间为 1 小时

Week 3: Introduction to Apache SparkML

完成时间为 1 小时
5 个视频 (总计 34 分钟), 2 个阅读材料, 3 个测验
5 个视频
Introduction to SparkML20分钟
Extract - Transform - Load3分钟
Introduction to Clustering: k-Means3分钟
Using K-Means in Apache SparkML2分钟
2 个阅读材料
Exercise 1: Modifying a Apache SparkML Feature Engineering Pipeline10分钟
Exercise 2 - Working with Clustering and Apache SparkML10分钟
3 个练习
Practice Quiz (Ungraded) - ML Pipelines4分钟
SparkML concepts 
Practice Quiz (Ungraded) - SparkML Algorithms
4

4

完成时间为 1 小时

Week 4: Supervised and Unsupervised learning with SparkML

完成时间为 1 小时
4 个视频 (总计 18 分钟), 2 个阅读材料, 2 个测验
4 个视频
LinearRegression with Apache SparkML6分钟
Logistic Regression1分钟
LogisticRegression with Apache SparkML4分钟
2 个阅读材料
Exercise 1 - Improving Classification performance10分钟
Course Project10分钟
2 个练习
Practice Quiz (Ungraded) - SparkML Algorithms (2)4分钟
Course Project Quiz

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关于 IBM AI Engineering 专业证书

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer. You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers. Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders. In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering....
IBM AI Engineering

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