machine learning

Machine Learning by Stanford is one of Coursera's most popular courses of all time. Taught by the MOOC platform's co-founder Andrew Ng, this Coursera-bestseller has over 2 million students and subtitles in over 6 different languages! The course provides a broad introduction to machine...

In partnership with Coursera, Imperial College London will be launching a MOOC-based master’s degree in machine learning. The program will launch in autumn 2020 and will be the first of its kind. Imperial is ranked #9 in the world by Times Higher Education and can boast numerous eminent...

In partnership with Coursera, Imperial College London has announced the launch of a MOOC-based master’s degree in machine learning. Launching in autumn 2020, this will be the first of its kind. Imperial is ranked #9 in the world by Times Higher Education and can boast numerous eminent...

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Overview >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your...

Overview 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...

Overview 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...

Overview 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...

Overview >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of...

Overview Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that...

Overview We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud...

Overview >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: Terms of Service | Qwiklabs <<< Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful...

Overview Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and...

Overview In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve...

Overview This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient...

Overview This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles...

Overview 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...

Overview 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...

Overview Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can...

Overview Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this...