Coursera

Advanced Machine Learning and Signal Processing (Coursera)

Offered by IBM,

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
Course 2 of 4 in the Advanced Data Science with IBM Specialization.

Syllabus

WEEK 1: Setting the stage
WEEK 2: Supervised Machine Learning
WEEK 3: Unsupervised Machine Learning
WEEK 4: Digital Signal Processing in Machine Learning

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

Data Science

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Aug 31st 2026

4 Weeks

Statistics Machine Learning Data Mining

Coursera

University of Michigan

Data Augmented Technology Assisted Medical Decision Making (Coursera)

Data Science

Artificial intelligence (AI) and machine learning (ML) have the potential to increase diagnostic accuracy, decrease diagnostic errors, and improve patient outcomes. The Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) course will teach you how to use AI to augment your diagnostic decision-making.

Sep 14th 2026

4 Weeks

Healthcare Artificial Intelligence Machine Learning

Coursera

University of California, San Diego

Machine Learning With Big Data (Coursera)

Statistics & Data Analysis Data Science

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

Aug 31st 2026

5-12 Weeks

Algorithms Machine Learning Big Data

Coursera

IBM

Machine Learning Introduction for Everyone (Coursera)

Data Science

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Sep 14th 2026

3 Weeks

ML Artificial Intelligence Machine Learning

Coursera

University of Michigan

Applied Machine Learning in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial.

Sep 14th 2026

4 Weeks

Python ML Machine Learning

Coursera

Google Cloud

Introduction to Image Generation (Coursera)

CS: Information & Technology

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Sep 14th 2026

3 Weeks

Machine Learning Coursera Plus Image Generation

Coursera

New York University Tandon School of Engineering

Fundamentals of Machine Learning in Finance (Coursera)

Data Science

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.

Sep 14th 2026

4 Weeks

Finance Machine Learning Clustering

Coursera

Google Cloud

Encoder-Decoder Architecture (Coursera)

CS: Information & Technology

This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

Sep 14th 2026

1 Week

Machine Learning TensorFlow Coursera Plus

Coursera

University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Aug 31st 2026

4 Weeks

Machine Learning Models Methods

Coursera

Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Statistics & Data Analysis Data Science

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

Sep 14th 2026

5-12 Weeks

Machine Learning PGM Inference

Coursera

Google Cloud

ML Pipelines on Google Cloud (Coursera)

Data Science

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

Sep 14th 2026

4 Weeks

ML Machine Learning Google Cloud

Coursera

DeepLearning.AI

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)

CS: Software Engineering Computer Science

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

Sep 14th 2026

4 Weeks

Artificial Intelligence Machine Learning Neural Networks