IBM - Advanced Machine Learning and Signal Processing
- Offered byCoursera
Advanced Machine Learning and Signal Processing at Coursera Overview
Duration | 27 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Machine Learning and Signal Processing at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Advanced Machine Learning and Signal Processing at Coursera Course details
- >>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<
- This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We?ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We?ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.
- If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
Advanced Machine Learning and Signal Processing at Coursera Curriculum
Setting the stage
A warm welcome
Linear algebra
High Dimensional Vector Spaces
Supervised vs. Unsupervised Machine Learning
How ML Pipelines work
Introduction to SparkML
What is SystemML (1/2) ?
What is SystemML (2/2) ?
How to use Apache SystemML in IBM Watson Studio
Extract - Transform - Load
Object Store
IMPORTANT: How to submit your programming assignments
Machine Learning
ML Pipelines
Supervised Machine Learning
Linear Regression
LinearRegression with Apache SparkML
Linear Regression using Apache SystemML
Batch Gradient Descent using Apache SystemML
The importance of validation data to prevent overfitting
Important evaluation measures
Logistic Regression
LogisticRegression with Apache SparkML
Probabilities refresher
Rules of probability and Bayes' theorem
The Gaussian distribution
Bayesian inference
Bayesian inference - example
Maximum a posteriori estimation
Bayesian inference in Python
Why is Naive Bayes "naive"
Support Vector Machines
Support Vector Machines using Apache SparkML
Crossvalidation
Hyper-parameter tuning using GridSearch
Decision Trees
Bootstrap Aggregation (Bagging) and RandomForest
Boosting and Gradient Boosted Trees
Gradient Boosted Trees with Apache SparkML
Hyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees
Regularization
Classification evaluation measures
Linear Regression
Splitting and Overfitting
Evaluation Measures
Logistic Regression
Naive Bayes
Support Vector Machines
Testing, X-Validation, GridSearch
Enselble Learning
Regularization
Unsupervised Machine Learning
Introduction to Unsupervised Machine Learning
Introduction to Clustering: k-Means
Hierarchical Clustering
Density-based clustering (Guest Lecture Saeed Aghabozorgi)
Using K-Means in Apache SparkML
Curse of Dimensionality
Dimensionality Reduction
Principal Component Analysis
Principal Component Analysis (demo)
Covariance matrix and direction of greatest variance
Eigenvectors and eigenvalues
Projecting the data
PCA in SystemML
Reading on Clustering Evaluation and Assessment
Clustering
PCA
Digital Signal Processing in Machine Learning
Signal decomposition, time and frequency domains
Fourier Transform in action
Signal generation and phase shift
The maths behind Fourier Transform
Discrete Fourier Transform
Fourier Transform in SystemML
Fast Fourier Transform
Nonstationary signals
Scaleograms
Continous Wavelet Transform
Scaling and translation
Wavelets and Machine Learning
Wavelets transform and SVM demo
Fourier Transform
Wavelet Transform
Advanced Machine Learning and Signal Processing at Coursera Admission Process
Important Dates
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