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Advanced Machine Learning and Signal Processing

Approx. 27 hours to complete

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Course Summary

This course focuses on advanced machine learning techniques and signal processing. Students will learn how to apply these techniques in various applications such as speech recognition, image processing, and natural language processing.

Key Learning Points

Learn advanced machine learning techniques
Apply machine learning in speech recognition, image processing and natural language processing
Get hands-on experience with real-world projects

Learning Outcomes

Understand advanced machine learning techniques
Apply machine learning in real-world projects
Gain experience in speech recognition, image processing and natural language processing

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning
Familiarity with Python programming language

Course Difficulty Level

Advanced

Course Format

Online
Self-paced

Similar Courses

Applied Machine Learning
Deep Learning
Natural Language Processing

Notable People in This Field

Andrew Ng
Yann LeCun

Related Books

Description

>>> 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.

Outline

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

Summary of User Reviews

Discover the power of advanced machine learning and signal processing with this top-rated course on Coursera. Learn from industry experts and gain valuable skills that can help you succeed in your career.

Key Aspect Users Liked About This Course

One key aspect that many users thought was good is the high-quality content that is presented in a clear and concise manner.

Pros from User Reviews

In-depth coverage of advanced machine learning and signal processing techniques
Expert instructors with real-world experience
Well-organized course materials that are easy to follow
Engaging assignments and quizzes that reinforce learning
Flexible schedule and self-paced learning options

Cons from User Reviews

Some users found the course to be too challenging for beginners
The course requires a significant time commitment to complete
The course can be quite technical and may require a strong background in math and statistics
Some users felt that the course could benefit from more practical examples and applications
The course may not be suitable for those looking for a more general overview of machine learning and signal processing

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