Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.

If you came here looking for an introduction to water, or a synopsis of the 2003 TV series about teenage mermaids you have sadly come to the wrong place. The H2O that we will talk about is H2O.ai, a company which develops products for easy, scalable, machine learning and artificial intelligence. Machine learning and artificial intelligence (or AI for short) are topics which have had a lot of interest over the past 4-5 years. Some of this interest has come from businesses as they begin to utilise the information they collect on a day-to-day basis to streamline/automate processes or gain insight. A lot of companies are now looking to hire data scientists/engineers and in turn this is making a lot more people interested in machine learning and AI.

Amazon India is inviting applications from students for the second edition of its online Machine Learning (ML) summer school. The course aims to teach key machine learning technologies by scientists at Amazon and to make the candidates industry-ready for careers in science. The free course, which was launched in 2021, is open to students enrolled in any recognised institute in India, who are expected to graduate in 2023 or 2024. Rajeev Rastogi, vice president of applied science in ML, says, "The tutorial sessions covering the right mix of theoretical and practical knowledge will be delivered by our ML scientists who are experts in their field. This programme will be a platform to help foster ML excellence and strive towards developing applied science skills in young talent."

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Data science is a new interdisciplinary field of research that focuses on extracting value from data, integrating knowledge and methods from computer science, mathematics and statistics, and an application domain. Machine learning is the field created at the intersection of computer science and statistics, and it has many applications in data science when the application domain is taken into consideration. From a historical perspective, machine learning was considered, for the past 50 years or so, as part of artificial intelligence. It was taught mainly in computer science departments to scientists and engineers and the focus was placed, accordingly, on the mathematical and algorithmic aspects of machine learning, regardless of the application domain. Thus, although machine learning deals also with statistics, which focuses on data and does consider the application domain, up until recently, most machine learning activities took place in the context of computer science, where it began, and which focuses traditionally on algorithms.

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

"Paradoxically, data is the most under-valued and de-glamorised aspect of AI" -- Google research authors of "Data Cascades in High-Stakes AI." "Data is food for AI" -- Andrew Ng, UC Berkeley professor and pioneer of the data-centric AI philosophy. Machine learning has seen a bifurcation towards both smaller and larger models in recent years. Large-scale language models with hundreds of billions of parameters are being released regularly, and, with no signs of performance saturation, we can expect to see this trend continue. On the flip side, the field of tiny machine learning (TinyML) -- deploying machine learning models on resource-constrained microcontrollers -- is also starting to take hold. Commercial applications for TinyML already exist, from keyword spotting in smartphones (e.g., "Hey Siri" and "OK Google") to person detection for controlling intelligent lighting, HVAC, and security systems.

HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other Machine Learning (ML) approaches. Frameworks enabling fast design space exploration to find practical algorithms are necessary to make HD computing competitive with other ML techniques. To this end, we introduce HDTorch, an open-source, PyTorch-based HDC library with CUDA extensions for hypervector operations. We demonstrate HDTorch's utility by analyzing four HDC benchmark datasets in terms of accuracy, runtime, and memory consumption, utilizing both classical and online HD training methodologies. We demonstrate average (training)/inference speedups of (111x/68x)/87x for classical/online HD, respectively. Moreover, we analyze the effects of varying hyperparameters on runtime and accuracy. Finally, we demonstrate how HDTorch enables exploration of HDC strategies applied to large, real-world datasets. We perform the first-ever HD training and inference analysis of the entirety of the CHB-MIT EEG epilepsy database. Results show that the typical approach of training on a subset of the data does not necessarily generalize to the entire dataset, an important factor when developing future HD models for medical wearable devices.

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

For the past two years, PlayVS (pronounced "play versus") has been the exclusive platform for high school competitions of "League of Legends." Anyone can play "League of Legends" free at home, but to compete in officially sanctioned high school leagues, students and schools had to pay PlayVS, which charges $64 per player per season. But the exclusive agreement between Riot and PlayVS barred these leagues from hosting interscholastic matches in "League of Legends," one of the most popular competitive games in the world.