One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms. Prior coding or scripting knowledge is required.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In machine learning, an algorithm is trained on a data set. The algorithm then uses this training data to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, fraud detection, and stock price prediction.

Abstract: Outdoor radio map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. Radio map describes spatial signal strength distribution and provides network coverage information. A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements. However, non-uniformly positioned measurements and access obstacles can make it difficult for accurate radio map estimation (RME) and spectrum planning in many outdoor environments. In this work, we develop a two-phase learning framework for radio map estimation by integrating radio propagation model and designing a conditional generative adversarial network (cGAN).

Google opened the doors to its Machine Learning Crash Course, which had already been taken by more than 18, 000 Googlers, in March 2018.

Have you ever wished you could create your own realistic images, translate text into multiple languages, or even compose original music? If so, you'll be interested to learn about generative models -- a type of machine learning algorithm that has the power to bring these capabilities to life. Generative models are trained on a dataset and learn the underlying distribution of the data. This allows them to generate new, synthetic examples that are similar to the original dataset. But what makes generative models so special?

Your business may generate mountains of data. But are you taking the edge of the visions it would reveal? You can use machine learning, a branch of AI, to analyze your data and predicts future outcome or identify hidden patterns. Today, I'll cover two approaches named supervised and unsupervised machine learning. The significant difference between the two is how the training data is labeled.

Gone are the days when large-scale industries process all their data manually. Machine learning is the latest buzz in the field of data science. Machine learning is a part of artificial intelligence (AI) that focuses on processing data using algorithms. Large industries are showing interest in employing machine learning as it helps process high-level data, which takes extreme calculation in less time and accuracy. Data processed from machine learning helps business owners make the right decisions.

Abstract: Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, calling for research on semi-supervised learning for this area. Existing semi-supervised object detection (SSOD) methods usually assume that the labeled and unlabeled data come from the same data distribution. In autonomous driving, however, data are usually collected from different scenarios, such as different weather conditions or different times in a day. Motivated by this, we study a novel but challenging domain inconsistent SSOD problem. It involves two kinds of distribution shifts among different domains, including (1) data distribution discrepancy, and (2) class distribution shifts, making existing SSOD methods suffer from inaccurate pseudo-labels and hurting model performance.

Although most of the applications of Machine Learning in our world are based on supervised machine learning algorithms and that's why this is where most of the investment goes into this direction. However, the majority of the available data is actually unlabeled: we have the input feature X, but we do not have the labels y. From here comes the importance of unsupervised learning. There are a lot of applications for unsupervised learning for example if you want to create a system that will take a few pictures of each item on a manufacturing production line and detect which items are defective. You can fairly easily create a system that will take pictures automatically, and this might give you thousands of pictures every day.