À propos de ce cours

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Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau avancé
Approx. 21 heures pour terminer
Anglais
Sous-titres : Anglais

Résultats de carrière des étudiants

56%

ont commencé une nouvelle carrière après avoir terminé ce cours

60%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

17%

a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau avancé
Approx. 21 heures pour terminer
Anglais
Sous-titres : Anglais

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Programme du cours : ce que vous apprendrez dans ce cours

Évaluation du contenuThumbs Up82%(2,357 notes)Info
Semaine
1

Semaine 1

5 heures pour terminer

Setting the stage

5 heures pour terminer
10 vidéos (Total 59 min), 2 lectures, 3 quiz
10 vidéos
Linear algebra5 min
High Dimensional Vector Spaces2 min
Supervised vs. Unsupervised Machine Learning4 min
How ML Pipelines work3 min
Introduction to SparkML20 min
What is SystemML (1/2) ?3 min
What is SystemML (2/2) ?6 min
How to use Apache SystemML in IBM Watson Studio4 min
Extract - Transform - Load3 min
2 lectures
Object Store10 min
IMPORTANT: How to submit your programming assignments10 min
2 exercices pour s'entraîner
Machine Learning12 min
ML Pipelines6 min
Semaine
2

Semaine 2

6 heures pour terminer

Supervised Machine Learning

6 heures pour terminer
26 vidéos (Total 131 min), 1 lecture, 10 quiz
26 vidéos
LinearRegression with Apache SparkML6 min
Linear Regression using Apache SystemML3 min
Batch Gradient Descent using Apache SystemML8 min
The importance of validation data to prevent overfitting3 min
Important evaluation measures2 min
Logistic Regression1 min
LogisticRegression with Apache SparkML4 min
Probabilities refresher6 min
Rules of probability and Bayes' theorem10 min
The Gaussian distribution4 min
Bayesian inference4 min
Bayesian inference - example9 min
Maximum a posteriori estimation5 min
Bayesian inference in Python8 min
Why is Naive Bayes "naive"7 min
Support Vector Machines3 min
Support Vector Machines using Apache SparkML8 min
Crossvalidation1 min
Hyper-parameter tuning using GridSearch3 min
Decision Trees2 min
Bootstrap Aggregation (Bagging) and RandomForest1 min
Boosting and Gradient Boosted Trees6 min
Gradient Boosted Trees with Apache SparkML2 min
Hyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees3 min
Regularization3 min
1 lecture
Classification evaluation measures10 min
9 exercices pour s'entraîner
Linear Regression6 min
Splitting and Overfitting2 min
Evaluation Measures2 min
Logistic Regression2 min
Naive Bayes16 min
Support Vector Machines2 min
Testing, X-Validation, GridSearch4 min
Enselble Learning4 min
Regularization4 min
Semaine
3

Semaine 3

5 heures pour terminer

Unsupervised Machine Learning

5 heures pour terminer
13 vidéos (Total 67 min), 1 lecture, 3 quiz
13 vidéos
Introduction to Clustering: k-Means3 min
Hierarchical Clustering3 min
Density-based clustering (Guest Lecture Saeed Aghabozorgi)4 min
Using K-Means in Apache SparkML2 min
Curse of Dimensionality9 min
Dimensionality Reduction4 min
Principal Component Analysis6 min
Principal Component Analysis (demo)6 min
Covariance matrix and direction of greatest variance8 min
Eigenvectors and eigenvalues8 min
Projecting the data4 min
PCA in SystemML2 min
1 lecture
Reading on Clustering Evaluation and Assessment10 min
2 exercices pour s'entraîner
Clustering4 min
PCA16 min
Semaine
4

Semaine 4

5 heures pour terminer

Digital Signal Processing in Machine Learning

5 heures pour terminer
13 vidéos (Total 108 min)
13 vidéos
Fourier Transform in action6 min
Signal generation and phase shift11 min
The maths behind Fourier Transform11 min
Discrete Fourier Transform16 min
Fourier Transform in SystemML15 min
Fast Fourier Transform7 min
Nonstationary signals5 min
Scaleograms7 min
Continous Wavelet Transform3 min
Scaling and translation3 min
Wavelets and Machine Learning3 min
Wavelets transform and SVM demo6 min
2 exercices pour s'entraîner
Fourier Transform16 min
Wavelet Transform16 min

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À propos du Spécialisation Advanced Data Science with IBM

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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