Many scientific fields -- including biology, health, education, and the social sciences -- use machine learning (ML) to help them analyze data at an unprecedented scale. However, ML researchers who develop advanced methods rarely provide detailed tutorials showing how to apply these methods. Existing tutorials are often costly to participants, presume extensive programming knowledge, and are not tailored to specific application fields. In an attempt to democratize ML methods, we organized a year-long, free, online tutorial series targeted at teaching advanced natural language processing (NLP) methods to computational social science (CSS) scholars. Two organizers worked with fifteen subject matter experts to develop one-hour presentations with hands-on Python code for a range of ML methods and use cases, from data pre-processing to analyzing temporal variation of language change. Although live participation was more limited than expected, a comparison of pre- and post-tutorial surveys showed an increase in participants' perceived knowledge of almost one point on a 7-point Likert scale. Furthermore, participants asked thoughtful questions during tutorials and engaged readily with tutorial content afterwards, as demonstrated by 10K~total views of posted tutorial recordings. In this report, we summarize our organizational efforts and distill five principles for democratizing ML+X tutorials. We hope future organizers improve upon these principles and continue to lower barriers to developing ML skills for researchers of all fields.

Author: Murat Karakaya Date created….. 17 09 2021 Date published… 11 03 2022 Last modified…. We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment. We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. You can access all the codes, videos, and posts of this tutorial series from the links below. In this tutorial series, there are several parts to cover the Text Classification with various Deep Learning Models topics.

This multiple session tutorial is designed to train researchers and practitioners to begin analyzing synchrophasor (i.e., PMU) and point on wave data. The course covers concepts from power engineering and data science, and will show attendees how to develop efficient workflows for analyzing and visualizing time series data at scale. The first day of the course will cover foundational concepts from power systems engineering, and will relate PMU data to physical properties of the grid. The session will discuss phasor calculation, and methods for using phasor data to compute frequency. We will close with a summary of best practices and lessons learned from using PMU data in industry.

WARNING: Do not confuse this article with "Mastering TensorFlow Tensors in 5 Easy Steps"! If you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! In this tutorial, we will focus on TensorFlow Variables.

We are going to commence our new tutorial series about Machine Learning. We will use python as programming language for this tutorial series. This article gives a brief introduction to Machine Learning. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Machine Learning models consists algorithms that can learn and make predictions from data.Machine Learning has been evolved from prediction making and computational learning theory in artificial intelligence.It helps computers to learn and perform a certain task based on past experience.These models can be based on following: In this article we saw a brief introduction to Machine Learning and in next article we will see how to install Anaconda.

I've been working on a tutorial series for creating self-driving cars in Grand Theft Auto 5 for a bit now. The most recent creation is the result of day or so worth of collecting training data, and about 4 days of actual training of the model. It's currently a 30-layer convolutional neural network, it works purely on a frame-by-frame basis with no preprocessing other than an image resize and grayscale. It makes actions based on the current frame's pixel data, with no memory of what it's been doing. I plan to eventually incorporate some form of memory with something like recurrent layers, but...baby steps at a time!

Machine learning (ML) is all the hotness right now. There is literally a new story every week about a complicated problem that was solved using ML. Most of my coworkers and friends in the industry have expressed interest in learning about ML, but have not been able gain a foot hold. I believe the problem is that the current books and online classes are overwhelming; scaring away students before they have a chance to build up confidence. The problem I had when I started learning ML was that the tutorials insisted on teaching the math that is fundamental for machine learning to work.

Have you ever wanted to know the sentiment of people on Twitter around a particular subject? Did you ever wonder what tweeps think about the latest breaking news? In this tutorial series, learn how to build an app to get data from Twitter and then use the data for cognitive insights by using IBM Watson services such as Tone Analyzer, Visual Recognition, and Alchemy. Upon completion of the series, you will know how to deploy Watson starter packs on IBM Bluemix and will be able to extend the starter packs to fit your specific business needs. This tutorial uses tweets as the data source.