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
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Advanced Level
Approx. 27 hours to complete
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. 27 hours to complete
English

Offered by

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IBM

Syllabus - What you will learn from this course

Content RatingThumbs Up83%(3,014 ratings)Info
Week
1

Week 1

6 hours to complete

Setting the stage

6 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 Learning30m
ML Pipelines30m
Week
2

Week 2

10 hours to complete

Supervised Machine Learning

10 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 Regression30m
Splitting and Overfitting30m
Evaluation Measures30m
Logistic Regression30m
Naive Bayes30m
Support Vector Machines30m
Testing, X-Validation, GridSearch30m
Enselble Learning30m
Regularization30m
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
Clustering30m
PCA30m
Week
4

Week 4

6 hours to complete

Digital Signal Processing in Machine Learning

6 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 Transform30m
Wavelet Transform30m

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About the Advanced Data Science with IBM Specialization

Advanced Data Science with IBM

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