About this Course
최근 조회 44,995

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 16시간 필요

권장: 15 hours/week...

영어

자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 16시간 필요

권장: 15 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

Setting the stage

...
10 videos (Total 59 min), 1 reading, 3 quizzes
10개의 동영상
Linear algebra5m
High Dimensional Vector Spaces2m
Supervised vs. Unsupervised Machine Learning4m
How ML Pipelines work3m
Introduction to SparkML20m
What is SystemML (1/2) ?3m
What is SystemML (2/2) ?6m
How to use Apache SystemML in IBM Watson Studio4m
Extract - Transform - Load3m
1개의 읽기 자료
Object Store10m
2개 연습문제
Machine Learning12m
ML Pipelines6m
2
완료하는 데 6시간 필요

Supervised Machine Learning

...
26 videos (Total 131 min), 1 reading, 10 quizzes
26개의 동영상
LinearRegression with Apache SparkML6m
Linear Regression using Apache SystemML3m
Batch Gradient Descent using Apache SystemML8m
The importance of validation data to prevent overfitting3m
Important evaluation measures2m
Logistic Regression1m
LogisticRegression with Apache SparkML4m
Probabilities refresher6m
Rules of probability and Bayes' theorem10m
The Gaussian distribution4m
Bayesian inference4m
Bayesian inference - example9m
Maximum a posteriori estimation5m
Bayesian inference in Python8m
Why is Naive Bayes "naive"7m
Support Vector Machines3m
Support Vector Machines using Apache SparkML8m
Crossvalidation1m
Hyper-parameter tuning using GridSearch3m
Decision Trees2m
Bootstrap Aggregation (Bagging) and RandomForest1m
Boosting and Gradient Boosted Trees6m
Gradient Boosted Trees with Apache SparkML2m
Hyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees3m
Regularization3m
1개의 읽기 자료
Classification evaluation measures10m
9개 연습문제
Linear Regression6m
Splitting and Overfitting2m
Evaluation Measures2m
Logistic Regression2m
Naive Bayes16m
Support Vector Machines2m
Testing, X-Validation, GridSearch4m
Enselble Learning4m
Regularization4m
3
완료하는 데 5시간 필요

Unsupervised Machine Learning

...
13 videos (Total 67 min), 1 reading, 3 quizzes
13개의 동영상
Introduction to Clustering: k-Means3m
Hierarchical Clustering3m
Density-based clustering (Guest Lecture Saeed Aghabozorgi)4m
Using K-Means in Apache SparkML2m
Curse of Dimensionality9m
Dimensionality Reduction4m
Principal Component Analysis6m
Principal Component Analysis (demo)6m
Covariance matrix and direction of greatest variance8m
Eigenvectors and eigenvalues8m
Projecting the data4m
PCA in SystemML2m
1개의 읽기 자료
Reading on Clustering Evaluation and Assessment10m
2개 연습문제
Clustering4m
PCA16m
4
완료하는 데 5시간 필요

Digital Signal Processing in Machine Learning

...
13 videos (Total 108 min), 3 quizzes
13개의 동영상
Fourier Transform in action6m
Signal generation and phase shift11m
The maths behind Fourier Transform11m
Discrete Fourier Transform16m
Fourier Transform in SystemML15m
Fast Fourier Transform7m
Nonstationary signals5m
Scaleograms7m
Continous Wavelet Transform3m
Scaling and translation3m
Wavelets and Machine Learning3m
Wavelets transform and SVM demo6m
2개 연습문제
Fourier Transform16m
Wavelet Transform16m
4.6
40개의 리뷰Chevron Right

50%

이 강좌를 수료한 후 새로운 경력 시작하기

57%

이 강좌를 통해 확실한 경력상 이점 얻기

25%

급여 인상 또는 승진하기

Advanced Machine Learning and Signal Processing의 최상위 리뷰

대학: 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.

대학: 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!

강사

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT
Avatar

Nikolay Manchev

Data Scientist
IBM EMEA Data Science

IBM 정보

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....

Advanced Data Science with IBM 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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