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Our Courses
Machine Learning for All
Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it.
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Course by
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Self Paced
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22 hours
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English
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
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Course by
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31 hours
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English
Problem Solving Using Computational Thinking
Have you ever heard that computers "think"? Believe it or not, computers really do not think. Instead, they do exactly what we tell them to do. Programming is, "telling the computer what to do and how to do it." Before you can think about programming a computer, you need to work out exactly what it is you want to tell the computer to do. Thinking through problems this way is Computational Thinking. Computational Thinking allows us to take complex problems, understand what the problem is, and develop solutions.
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Course by
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Self Paced
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11 hours
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English
Computational Thinking for Problem Solving
Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact.
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Course by
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Self Paced
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18 hours
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English
Practical Machine Learning
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests.
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Course by
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Self Paced
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9 hours
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English
Data-driven Astronomy
Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools.
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Course by
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Self Paced
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24 hours
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English
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
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 DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning.
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Course by
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Self Paced
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18 hours
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English
Algorithms, Data Collection, and Starting to Code
This course starts you on your journey learning about computational thinking and beginning C programming. If you’d like to explore how we can interact with the world in a rigorous, computational way, and would also like to start learning to program, this is the course for you! You may have heard lots of talk about computational thinking recently, but if you ask 10 different people what it is you’ll probably get 10 different answers. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components.
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Course by
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Self Paced
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15 hours
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English
Natural Language Processing in TensorFlow
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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the DeepLearning.AI TensorFlow Developer Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.
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Course by
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Self Paced
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24 hours
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English
IT Security: Defense against the digital dark arts
This course covers a wide variety of IT security concepts, tools, and best practices. It introduces threats and attacks and the many ways they can show up. We’ll give you some background of encryption algorithms and how they’re used to safeguard data. Then, we’ll dive into the three As of information security: authentication, authorization, and accounting. We’ll also cover network security solutions, ranging from firewalls to Wifi encryption options.
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Course by
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Self Paced
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32 hours
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English
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
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Course by
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Self Paced
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24 hours
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English
Sequences, Time Series and Prediction
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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction.
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Course by
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Self Paced
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23 hours
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English
Attract and Engage Customers with Digital Marketing
Attract and Engage Customers with Digital Marketing is the second of seven courses in the Google Digital Marketing & E-commerce Certificate. In this course you will practice using search engine optimization (SEO), search engine marketing (SEM), and display advertising to attract and engage customers online. You will explore the stages of the marketing funnel and learn how to use digital marketing tactics to move customers through the stages.
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Course by
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Self Paced
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21 hours
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English
C++ For C Programmers, Part B
This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.
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Course by
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Self Paced
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15 hours
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English
Convolutional Neural Networks in TensorFlow
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 DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the DeepLearning.AI TensorFlow Developer Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1.
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Course by
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Self Paced
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17 hours
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English
Process Mining: Data science in Action
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.
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Course by
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Self Paced
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22 hours
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English
Blockchain Basics
This first course of the Blockchain specialization provides a broad overview of the essential concepts of blockchain technology – by initially exploring the Bitcoin protocol followed by the Ethereum protocol – to lay the foundation necessary for developing applications and programming. You will be equipped with the knowledge needed to create nodes on your personal Ethereum blockchain, create accounts, unlock accounts, mine, transact, transfer Ethers, and check balances.
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Course by
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Self Paced
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16 hours
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English
C++ For C Programmers, Part A
This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.
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Course by
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Self Paced
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13 hours
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English
Design Computing: 3D Modeling in Rhinoceros with Python/Rhinoscript
Why should a designer learn to code? As our world is increasingly impacted by the use of algorithms, designers must learn how to use and create design computing programs. Designers must go beyond the narrowly focused use of computers in the automation of simple drafting/modeling tasks and instead explore the extraordinary potential digitalization holds for design culture/practice. Structured around a series of fundamental design problems, this course will show you Python code in terms of its rules and syntax, and what we can do with it in its application and design.
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Course by
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Self Paced
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27 hours
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English
Blockchain and Cryptocurrency Explained
The sudden rise in the value of Bitcoin and other cryptocurrencies, and its subsequent decline, focused the world’s attention on cryptocurrencies as a means of payment.
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Course by
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Self Paced
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9 hours
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English
Practical Machine Learning on H2O
In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.
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Course by
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Self Paced
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24 hours
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English
Build a Modern Computer from First Principles: Nand to Tetris Part II (project-centered course)
In this project-centered course you will build a modern software hierarchy, designed to enable the translation and execution of object-based, high-level languages on a bare-bone computer hardware platform. In particular, you will implement a virtual machine and a compiler for a simple, Java-like programming language, and you will develop a basic operating system that closes gaps between the high-level language and the underlying hardware platform. In the process, you will gain a deep, hands-on understanding of numerous topics in applied computer science, e.g.
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Course by
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Self Paced
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90 hours
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English
Solving Algorithms for Discrete Optimization
Discrete Optimization aims to make good decisions when we have many possibilities to choose from. Its applications are ubiquitous throughout our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions on the use of scarce or expensive resources such as staffing and material resources also allow corporations to improve their profit by millions of dollars.
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Course by
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Self Paced
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22 hours
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English
AI for Medical Diagnosis
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!
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Course by
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Self Paced
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20 hours
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English
Introduction to Machine Learning
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets.
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Course by
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Self Paced
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21 hours
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English