GIS and Drone Technologies are both powerful tools for assisting people in analyzing the world we inhabit. Whether you are someone with GIS skills looking to add drones to your toolbelt, or a drone pilot who wants to level up their deliverable products, you are in the right place. Both these skills require very similar mindsets, such as an attention to detail, focus and accuracy. If you can do one, you can do the other, so why not do both! This course is best for those who have some familiarity with either GIS concepts or drone mapping, or those that are comfortable in learning new software packages.

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come.

Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered.

The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science. Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making. Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis.

Python 3 でクローリングして画像データを収集、加工し、画像分類器を作ってみよう。ディープラーニングによるモデル作成、改善を自分の集めたデータで実践します。Flaskでウェブアプリ化, XcodeでiOSアプリ化にも挑戦します。

LOOPING IN PYTHON: for loop, while loop in python, break and continue keywords in python, nested for loop in python.

Oxford Mathematics Public LectureDeep Maths - Machine Learning and MathematicsIn December 2021 mathematicians at Oxford and Sydney universities together with their collaborators at DeepMind announced that they had successfully used tools from machine learning to discover new patterns in mathematics. But what exactly had they done and what are its implications for the future of mathematics and mathematicians?This online event will feature short talks from each of the four collaborators, explaining their work, followed by a panel discussion addressing its wider implications. If you wish to submit a question, please email external-relations@maths.ox.ac.ukThe speakers:Alex Davies - DeepMindAndras Juhasz - University of OxfordMarc Lackenby - University of OxfordGeordie Williamson - University of SydneyThe panel will be chaired by Jon Keating, Sedleian Professor of Natural Philosophy in Oxford.This is an online only lecture which every one is free to watch: www.youtube.com/c/OxfordMathematicsThe Oxford Mathematics Public Lectures are generously supported by XTX Markets.

In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily sub-optimal. Also, the rapidly increasing size of the action space of such a system consists of a significant problem for traditional schedulers. Reinforcement learning, however, is suited to deal with issues requiring making sequential decisions towards a long-term, often remote, goal. In this work, we set ourselves on a problem that presents itself with a hierarchical structure: the task-scheduling, by a centralised agent, in a dynamic warehouse multi-agent environment and the execution of one such schedule, by decentralised agents with only partial observability thereof. We propose to use deep reinforcement learning to solve both the high-level scheduling problem and the low-level multi-agent problem of schedule execution. Finally, we also conceive the case where centralisation is impossible at test time and workers must learn how to cooperate in executing the tasks in an environment with no schedule and only partial observability.

Description: This course prepares participants to begin running data analysis on databases. Both univariate and multivariate analysis are covered with a particular focus on regression analysis. Regression analysis is done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course is on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses. Description: This course covers the required fundamentals about big data technology that will help you confidently lead a big data project in your organization.

In this course we will talk about the past, present and the future of AI. This course covers all the introductory topics to AI to get you started on the path of becoming AI specialist. You will learn about main philosophy, history and approaches of AI as well as its applications. In this course we will talk about all that you need to know to get started in the field of AI. You will get familiar with the main approaches and research fields of artificial intelligence. You will know the advantages and disadvantages of AI as well as its possible applications in the future. The course is split into 5 main sections starting from the history of AI. In this section we cover the basics and the history, next we will go into the present day applications of AI followed by the topics on the main categories and methods of AI. Lastly we will speak about cons and pros as well as the future of AI technology.