课程信息
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第 2 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为16 小时

建议:15 hours/week...

英语(English)

字幕:英语(English)
User
学习Course的学生是
  • Data Scientists
  • Machine Learning Engineers
  • Systems Engineers
  • Risk Managers
  • Researchers
User
学习Course的学生是
  • Data Scientists
  • Machine Learning Engineers
  • Systems Engineers
  • Risk Managers
  • Researchers

第 2 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为16 小时

建议:15 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 5 小时

Setting the stage

10 个视频 (总计 59 分钟), 2 个阅读材料, 3 个测验
10 个视频
Linear algebra5分钟
High Dimensional Vector Spaces2分钟
Supervised vs. Unsupervised Machine Learning4分钟
How ML Pipelines work3分钟
Introduction to SparkML20分钟
What is SystemML (1/2) ?3分钟
What is SystemML (2/2) ?6分钟
How to use Apache SystemML in IBM Watson Studio4分钟
Extract - Transform - Load3分钟
2 个阅读材料
Object Store10分钟
IMPORTANT: How to submit your programming assignments10分钟
2 个练习
Machine Learning12分钟
ML Pipelines6分钟
2
完成时间为 6 小时

Supervised Machine Learning

26 个视频 (总计 131 分钟), 1 个阅读材料, 10 个测验
26 个视频
LinearRegression with Apache SparkML6分钟
Linear Regression using Apache SystemML3分钟
Batch Gradient Descent using Apache SystemML8分钟
The importance of validation data to prevent overfitting3分钟
Important evaluation measures2分钟
Logistic Regression1分钟
LogisticRegression with Apache SparkML4分钟
Probabilities refresher6分钟
Rules of probability and Bayes' theorem10分钟
The Gaussian distribution4分钟
Bayesian inference4分钟
Bayesian inference - example9分钟
Maximum a posteriori estimation5分钟
Bayesian inference in Python8分钟
Why is Naive Bayes "naive"7分钟
Support Vector Machines3分钟
Support Vector Machines using Apache SparkML8分钟
Crossvalidation1分钟
Hyper-parameter tuning using GridSearch3分钟
Decision Trees2分钟
Bootstrap Aggregation (Bagging) and RandomForest1分钟
Boosting and Gradient Boosted Trees6分钟
Gradient Boosted Trees with Apache SparkML2分钟
Hyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees3分钟
Regularization3分钟
1 个阅读材料
Classification evaluation measures10分钟
9 个练习
Linear Regression6分钟
Splitting and Overfitting2分钟
Evaluation Measures2分钟
Logistic Regression2分钟
Naive Bayes16分钟
Support Vector Machines2分钟
Testing, X-Validation, GridSearch4分钟
Enselble Learning4分钟
Regularization4分钟
3
完成时间为 5 小时

Unsupervised Machine Learning

13 个视频 (总计 67 分钟), 1 个阅读材料, 3 个测验
13 个视频
Introduction to Clustering: k-Means3分钟
Hierarchical Clustering3分钟
Density-based clustering (Guest Lecture Saeed Aghabozorgi)4分钟
Using K-Means in Apache SparkML2分钟
Curse of Dimensionality9分钟
Dimensionality Reduction4分钟
Principal Component Analysis6分钟
Principal Component Analysis (demo)6分钟
Covariance matrix and direction of greatest variance8分钟
Eigenvectors and eigenvalues8分钟
Projecting the data4分钟
PCA in SystemML2分钟
1 个阅读材料
Reading on Clustering Evaluation and Assessment10分钟
2 个练习
Clustering4分钟
PCA16分钟
4
完成时间为 5 小时

Digital Signal Processing in Machine Learning

13 个视频 (总计 108 分钟), 3 个测验
13 个视频
Fourier Transform in action6分钟
Signal generation and phase shift11分钟
The maths behind Fourier Transform11分钟
Discrete Fourier Transform16分钟
Fourier Transform in SystemML15分钟
Fast Fourier Transform7分钟
Nonstationary signals5分钟
Scaleograms7分钟
Continous Wavelet Transform3分钟
Scaling and translation3分钟
Wavelets and Machine Learning3分钟
Wavelets transform and SVM demo6分钟
2 个练习
Fourier Transform16分钟
Wavelet Transform16分钟
4.5
69 个审阅Chevron Right

56%

完成这些课程后已开始新的职业生涯

60%

通过此课程获得实实在在的工作福利

17%

加薪或升职

来自Advanced Machine Learning and Signal Processing的热门评论

创建者 ASep 8th 2018

A career changer course, thanks the hand-ons which is second to none, i have gained experience which on other online course can produce, thanks to IBM for this course which timely and excellent.

创建者 JJJan 1st 2019

Such great material. I really loved working out the notebooks. I have to go back and redo the IoT starter exercise to get better accuracy, but this was awesome!

讲师

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT
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Nikolay Manchev

Senior Data Scientist
IBM EMEA Data Science (2015-2019)

关于 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....

关于 Advanced Data Science with IBM 专项课程

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

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