You can also download code cheat sheets, checklists, and worksheets to shorten the data science learning curve. Want to level up your spreadsheet skills from intermediate to advanced? This course by Ben Collins teaches you one new high-level spreadsheet formula or technique every day for 30 days, using Google Sheets. These bite-sized tutorials will get you comfortable with manipulating data in spreadsheets in more complex ways.

Machine Learning can be split into to two categories: Supervised and Unsupervised learning. Supervised learning is when a model is trained on labelled data: This means clear input data and "solutions" for the data. Supervised learning can be split into two types: Classification and Regression. Classification is trying to identify the class that a piece of data belongs to, Regression is drawing a best-fit line in the data that can "catch" as many points as possible. An example of supervised learning is predicting if a human is male or female based on the measurements of the body.

This blog post was co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services. As schools and organizations around the world prepare for a new school year, remote learning tools have never been more critical. Educational technology, and especially AI, has a huge opportunity to facilitate new ways for educators and students to connect and learn. Today, we are excited to announce the general availability of Immersive Reader, and shine a light on how new improvements to Azure Cognitive Services can help developers build AI apps for remote education that empower everyone. Immersive Reader is an Azure Cognitive Service within the Azure AI platform that helps readers read and comprehend text.

Join us online for this 1-hour, live event. The UMaine Artificial Intelligence Fall Lunch and Learn webinar series features monthly informational seminars to discuss all things AI. The first virtual event takes place at noon on Thursday, Sept. 3 and will discuss "Advances in Artificial Intelligence" with experts in the field and an opportunity for Q&A with the panel. Visit our website to register for this and other UMaine AI events at https://ai.umaine.edu/webinars/.

Our editors have compiled this directory of the best Python books based on Amazon user reviews, rating, and ability to add business value. There are loads of free resources available online (such as Solutions Review's Data Analytics Software Buyer's Guide, visual comparison matrix, and best practices section) and those are great, but sometimes it's best to do things the old fashioned way. There are few resources that can match the in-depth, comprehensive detail of one of the best Power BI books. The editors at Solutions Review have done much of the work for you, curating this comprehensive directory of the best Python books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in the first section of this compilation have met a minimum criteria of 15 reviews and a 4-star-or-better ranking. Below you will find a library of titles from recognized industry analysts, experienced practitioners, and subject matter experts spanning the depths of Python coding for beginners all the way to advanced data science best practices for Python users. This compilation includes publications for practitioners of all skill levels. "Python Crash Course is the world's best-selling guide to the Python programming language. In the first half of the book, you'll learn basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. You'll also learn how to make your programs interactive and test your code safely before adding it to a project. In the second half, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, a set of data visualizations with Python's handy libraries, and a simple web app you can deploy online."

Adaptive traffic signal control is one key avenue for mitigating the growing consequences of traffic congestion. Incumbent solutions such as SCOOT and SCATS require regular and time-consuming calibration, can't optimise well for multiple road use modalities, and require the manual curation of many implementation plans. A recent alternative to these approaches are deep reinforcement learning algorithms, in which an agent learns how to take the most appropriate action for a given state of the system. This is guided by neural networks approximating a reward function that provides feedback to the agent regarding the performance of the actions taken, making it sensitive to the specific reward function chosen. Several authors have surveyed the reward functions used in the literature, but attributing outcome differences to reward function choice across works is problematic as there are many uncontrolled differences, as well as different outcome metrics. This paper compares the performance of agents using different reward functions in a simulation of a junction in Greater Manchester, UK, across various demand profiles, subject to real world constraints: realistic sensor inputs, controllers, calibrated demand, intergreen times and stage sequencing. The reward metrics considered are based on the time spent stopped, lost time, change in lost time, average speed, queue length, junction throughput and variations of these magnitudes. The performance of these reward functions is compared in terms of total waiting time. We find that speed maximisation resulted in the lowest average waiting times across all demand levels, displaying significantly better performance than other rewards previously introduced in the literature.

The use of machine learning (ML) systems in real-world applications entails more than just a prediction algorithm. AI for social good applications, and many real-world ML tasks in general, feature an iterative process which joins prediction, optimization, and data acquisition happen in a loop. We introduce bandit data-driven optimization, the first iterative prediction-prescription framework to formally analyze this practical routine. Bandit data-driven optimization combines the advantages of online bandit learning and offline predictive analytics in an integrated framework. It offers a flexible setup to reason about unmodeled policy objectives and unforeseen consequences. We propose PROOF, the first algorithm for this framework and show that it achieves no-regret. Using numerical simulations, we show that PROOF achieves superior performance over existing baseline.

Summarization of speech is a difficult problem due to the spontaneity of the flow, disfluencies, and other issues that are not usually encountered in written texts. Our work presents the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics, from gardening and cooking to software configuration and sports. In order to enrich the vocabulary, we use transfer learning and pretrain the model on a few large cross-domain datasets in both written and spoken English. We also do preprocessing of transcripts to restore sentence segmentation and punctuation in the output of an ASR system. The results are evaluated with ROUGE and Content-F1 scoring for the How2 and WikiHow datasets. We engage human judges to score a set of summaries randomly selected from a dataset curated from HowTo100M and YouTube. Based on blind evaluation, we achieve a level of textual fluency and utility close to that of summaries written by human content creators. The model beats current SOTA when applied to WikiHow articles that vary widely in style and topic, while showing no performance regression on the canonical CNN/DailyMail dataset. Due to the high generalizability of the model across different styles and domains, it has great potential to improve accessibility and discoverability of internet content. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.

Amazon has published videos and supplementary materials from several of its internal Machine Learning University courses.

New What you'll learn Analysis of Variance Descriptive Statistics Inferential Statistics Requirements Basic knowledge of computers Description Learn Statistics, Analytics and Data Visualization with JMP 15 to solve problems, reveal opportunities and inform decisions. Create opportunities for you or key decision-makers to discover data patterns such as customer purchase behavior, sales trends, quality defects, or production bottlenecks. What You'll Learn: Here is a summary of topics covered in this course: Hypothesis Testing Normal Distributions ANOVA Descriptive Statistics Quality Control Charts (Pareto, X Bar & R, & IMR) Linear Regression (Pearsons) Correlation Coefficient Publish sharable Analysis & Dashboards Section 2: Data Types, Column, Data Clean Up Import data from a variety of sources: Excel, Google Sheets, CSV, etc. Learn how to format specific columns and how to clean data before creating graphs / distributions / analysis. Section 3: JMP Visuals & Graphing Learn how to create individual value plots (scatter plots), bar charts, pie charts, parallel plots, heat maps, and more. Section 4: Descriptive Statistics & Quality Control ChartsLearn and create tables of descriptive statistics on JMP.