About this Course

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Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 21 hours to complete
English
Subtitles: English

Learner Career Outcomes

56%

started a new career after completing these courses

60%

got a tangible career benefit from this course

17%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 21 hours to complete
English
Subtitles: English

Offered by

IBM logo

IBM

Syllabus - What you will learn from this course

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Week
1

Week 1

5 hours to complete

Setting the stage

5 hours to complete
10 videos (Total 59 min), 2 readings, 3 quizzes
10 videos
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
2 readings
Object Store10m
IMPORTANT: How to submit your programming assignments10m
2 practice exercises
Machine Learning12m
ML Pipelines6m
Week
2

Week 2

6 hours to complete

Supervised Machine Learning

6 hours to complete
26 videos (Total 131 min), 1 reading, 10 quizzes
26 videos
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
1 reading
Classification evaluation measures10m
9 practice exercises
Linear Regression6m
Splitting and Overfitting2m
Evaluation Measures2m
Logistic Regression2m
Naive Bayes16m
Support Vector Machines2m
Testing, X-Validation, GridSearch4m
Enselble Learning4m
Regularization4m
Week
3

Week 3

5 hours to complete

Unsupervised Machine Learning

5 hours to complete
13 videos (Total 67 min), 1 reading, 3 quizzes
13 videos
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
1 reading
Reading on Clustering Evaluation and Assessment10m
2 practice exercises
Clustering4m
PCA16m
Week
4

Week 4

5 hours to complete

Digital Signal Processing in Machine Learning

5 hours to complete
13 videos (Total 108 min)
13 videos
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
2 practice exercises
Fourier Transform16m
Wavelet Transform16m

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About the Advanced Data Science with IBM Specialization

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

Frequently Asked Questions

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