In Advanced Machine Learning and Signal Processing course you will learn about the fundamentals of Linear Algebra to understand how machine learning modes work. So you will be introduced to the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. The SparkML makes up the greatest portion of this course since scalability is key to address performance bottlenecks.
You will 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 Advanced Machine Learning and Signal Processing course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. Moreover, the learner will be actually working on a self-created, and real dataset throughout the course.
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.
What you will get in the Linked 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.
Syllabus of Advanced Machine Learning and Signal Processing
- 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
- Machine Learning
- ML Pipelines
Supervised Machine Learning
- LinearRegression with Apache SparkML, Apache systemML, Logistic Regression
- Probability and Bayes’ theorem
- The Gaussian distribution
- Bayesian inference in Python
- Naive Bayes
- Support Vector Machines using Apache SparkML
- 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
9 practical exercises on above studies algorithms
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
- Principal Component Analysis
- Covariance matrix and direction of greatest variance
- Eigenvectors and eigenvalues
- Projecting the data
- PCA in SystemML
Digital Signal Processing in Machine Learning
- Signal decomposition, time, and frequency domains
- Fourier Transform, Discrete Fourier Transform, Fourier Transform in SystemML, Fast Fourier Transform
- Non-stationary signals
- Continous Wavelet Transform, Scaling and translation.
- Wavelets and Machine Learning
- Wavelets transform and SVM demo
- Fourier Transform
- Wavelet Transform
About Course Certificate and Credentials
After completion of this course, you can earn the Coursera course certificate and an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
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- Online Course
- 1-4 Weeks
- Paid Course (Paid certificate)
- Apache System Machine learning Signal processing Spark