Machine Learning Algorithms (Coursera)

Machine Learning Algorithms (Coursera)

In this course you will: understand the naïve Bayesian algorithm; understand the Support Vector Machine algorithm; understand the Decision Tree algorithm; understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

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

Syllabus

WEEK 1: Naïve Bayes
WEEK 2: Support Vector Machine
WEEK 3: Decision Tree
WEEK 4: Clustering

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

Related Courses

Introduction to Artificial Intelligence (AI) (Coursera) Coursera
IBM

Introduction to Artificial Intelligence (AI) (Coursera)

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

Jun 22nd 2026
4 Weeks
Application of AI, InsurTech, and Real Estate Technology (Coursera) Coursera
University of Pennsylvania

Application of AI, InsurTech, and Real Estate Technology (Coursera)

In this course, you’ll learn about the emerging technologies in Artificial Intelligence and Machine Learning that are utilized in InsurTech and Real Estate Tech. Professor Chris Geczy of the Wharton School has designed this course to help you navigate the complex world of insurance and real estate tech, and understand how FinTech plays a role in the future of the industry. Through study and analysis of Artificial Intelligence and Machine Learning, you’ll learn how InsurTech is redefining the insurance industry.

Jun 22nd 2026
4 Weeks
Sequence Models (Coursera) Coursera
DeepLearning.AI

Sequence Models (Coursera)

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

Jun 22nd 2026
3 Weeks
Matrix Methods (Coursera) Coursera
University of Minnesota

Matrix Methods (Coursera)

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Jun 22nd 2026
5-12 Weeks
Machine Learning Foundations: A Case Study Approach (Coursera) Coursera
University of Washington

Machine Learning Foundations: A Case Study Approach (Coursera)

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Jun 22nd 2026
5-12 Weeks
Machine Learning with Python (Coursera) Coursera
IBM

Machine Learning with Python (Coursera)

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Jun 22nd 2026
5-12 Weeks
Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud (Coursera) Coursera
University of Illinois at Urbana-Champaign

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud (Coursera)

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information.

Jun 22nd 2026
4 Weeks
Structuring Machine Learning Projects (Coursera) Coursera
DeepLearning.AI

Structuring Machine Learning Projects (Coursera)

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.

Jun 22nd 2026
2 Weeks
Getting started with TensorFlow 2 (Coursera) Coursera
Imperial College London

Getting started with TensorFlow 2 (Coursera)

Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

Jun 22nd 2026
5-12 Weeks