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Our Courses
Statistical Mechanics: Algorithms and Computations
In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.
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Course by
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Self Paced
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16 hours
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English
Cybersecurity Threat Vectors and Mitigation
This course provides a comprehensive overview of threat vectors and the strategies for mitigating them, and aims to equip you with the necessary skills and knowledge to safeguard against cyber threats. You’ll gain a deep understanding of the threat vectors used by attackers, discover encryption techniques, and explore different compliance concepts. This course will get you one step closer to the Microsoft Cybersecurity Analyst Professional Certificate, which requires no degree or prior experience.
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Course by
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Self Paced
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20 hours
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English
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary.
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Course by
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Self Paced
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21 hours
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English
Hyperparameter Tuning with Keras Tuner
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves.
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Course by
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Self Paced
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3 hours
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English
Geometric Algorithms
Geometric algorithms are a category of computational methods used to solve problems related to geometric shapes and their properties. These algorithms deal with objects like points, lines, polygons, and other geometric figures. In many areas of computer science such as robotics, computer graphics, virtual reality, and geographic information systems, it is necessary to store, analyze, and create or manipulate spatial data.
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Course by
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Self Paced
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18 hours
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English
Scikit-Learn to Solve Regression Machine Learning Problems
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means. This project is practical and directly applicable to many industries.
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Course by
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Self Paced
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3 hours
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English
Employee Attrition Prediction Using Machine Learning
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
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Course by
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3 hours
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English
Python Programming Fundamentals
This introductory course is designed for beginners and individuals with limited programming experience who want to embark on their software development or data science journey using Python. Throughout the course, learners will gain a solid understanding of algorithmic thinking, Python syntax, code testing, debugging techniques, and modular code development--essential skills for a successful career in software engineering, development, or data science.
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Course by
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Self Paced
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24 hours
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English
Unsupervised Machine Learning for Customer Market Segmentation
In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation.
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Course by
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Self Paced
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3 hours
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English
XG-Boost 101: Used Cars Price Prediction
In this hands-on project, we will train 3 Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regression, and XG-Boost to predict used cars prices.
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Course by
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Self Paced
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3 hours
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English
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm.
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Course by
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Self Paced
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33 hours
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English
Machine Learning for Investment Professionals
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
-\tDistinguish between supervised and unsupervised machine learning and deep learning
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Course by
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Self Paced
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17 hours
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English
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems.
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Course by
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Self Paced
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17 hours
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English
Machine Learning for Accounting with Python
This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course.
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Course by
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Self Paced
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64 hours
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English
Artificial Intelligence: Ethics & Societal Challenges
Artificial Intelligence: Ethics & Societal Challenges is a four-week course that explores ethical and societal aspects of the increasing use of artificial intelligent technologies (AI). The aim of the course is to raise awareness of ethical and societal aspects of AI and to stimulate reflection and discussion upon implications of the use of AI in society. The course consists of four modules where each module represents about one week of part-time studies. A module includes a number of lectures and readings.
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Course by
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Self Paced
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14 hours
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English
Big Data Analysis with Scala and Spark (Scala 2 version)
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.
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Course by
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Self Paced
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28 hours
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English
Machine Learning Algorithms with R in Business Analytics
One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data.
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Course by
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Self Paced
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14 hours
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English
Data mining of Clinical Databases - CDSS 1
This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
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Course by
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Self Paced
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21 hours
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English
Finding Mutations in DNA and Proteins (Bioinformatics VI)
In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome.
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Course by
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Self Paced
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24 hours
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English
Computer Science: Algorithms, Theory, and Machines
This course introduces the broader discipline of computer science to people having basic familiarity with Java programming. It covers the second half of our book Computer Science: An Interdisciplinary Approach (the first half is covered in our Coursera course Computer Science: Programming with a Purpose, to be released in the fall of 2018).
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Course by
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Self Paced
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20 hours
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English
Simulation, Algorithm Analysis, and Pointers
This course is the fourth and final course in the specialization exploring both computational thinking and beginning C programming. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components. Most people have a better understanding of what beginning C programming means! This course assumes you have the prerequisite knowledge from the previous three courses in the specialization.
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Course by
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Self Paced
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11 hours
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English
Remote Sensing Image Acquisition, Analysis and Applications
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.
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Course by
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Self Paced
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23 hours
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English
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics.
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Course by
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Self Paced
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18 hours
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English
Analysis of Algorithms
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings. All the features of this course are available for free.
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Course by
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Self Paced
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21 hours
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English
Modeling Time Series and Sequential Data
In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next.
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Course by
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Self Paced
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11 hours
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English