Video Production can be time-consuming, difficult, technical and expensive, but it doesn't have to be. This video production course is about how to do simple, easy talking head videos for a wide range of business communication needs. There is a video explosion going on in the online world. Are you unsure where to start? This course will lead you through the simplest and easiest ways to start communicating with your customers, clients, prospects and colleagues in the most effective manner: talking head video.

It is simply looking at where things happen to understand why they happen there. Geospatial Data Science is the discipline that specifically focuses on the spatial component of data science. Spatial Analysis is considered as a core infrastructure of the modern tech industry and is heavily substantiated by the business transactions of world-leading companies such as Uber, Deliveroo, Apple, Google, Intel, and evidently by the motor companies such as Tesla, BMW, and Mercedes. So, these companies are bound to hire more and more Spatial Data Analysts and Geo-Spatial Scientists. Based on these business trends, we've compiled the spatial analysis courses designed by world-class educators to help beginners gain solid foundations of spatial data analysis.

In this course, our intent is to provide just the right information to get you started on some of key technologies - Internet of Things, Blockchain, Cloud and Artificial Intelligence. The combinatorial power of these technologies would drive next generation of future applications. Internet of Things (IoT) is one of the most hyped concepts in today's technology world. However, with so much hype, there is still a lot of confusion on what does Internet of Things actually mean and what it takes to build IoT applications and how to apply it in various industries. The first topic would cover what is Internet of Thing, followed by how to realize IoT applications using an Internet of Things Architecture.

Build an image-classifier system that makes human-like decisions. Most computer vision-related tutorials jump right to implementation after a few cursory introductory notes. What sets Raveena Jayadev's post apart is that we do get a thorough, hands-on guide to building a machine learning system that recognizes digits, but not before we first understand how that system's "mental model" operates. Get a solid foundation in online (machine) learning. A popular deep learning topic, online learning has numerous industry applications in scenarios where models "must learn from new data that is constantly becoming available," as Cameron Wolfe explains in his essential (and comprehensive) introduction to the topic.

Deep Learning: Recurrent Neural Networks with Python Recurrent Neural Networks (RNNs), a class of neural networks, are essential in processing sequences such as sensor measurements, daily stock prices, etc. Description Recurrent Neural Networks (RNNs), a class of neural networks, are essential in processing sequences such as sensor measurements, daily stock prices, etc. In fact, most of the sequence modelling problems on images and videos are still hard to solve without Recurrent Neural Networks. Further, RNNs are also considered to be the general form of deep learning architecture. Hence, the understanding of RNNs is crucial in all the fields of Data Science. This course addresses all these concerns and empowers you to take your career to the next level with a masterful grip on the theoretical concepts and practical implementations of RNNs in Data Science.

In May, Google executives unveiled experimental new artificial intelligence trained with text and images they said would make internet searches more intuitive. Wednesday, Google offered a glimpse into how the tech will change the way people search the web. Starting next year, the Multitask Unified Model, or MUM, will enable Google users to combine text and image searches using Lens, a smartphone app that's also incorporated into Google search and other products. So you could, for example, take a picture of a shirt with Lens, then search for "socks with this pattern." Searching "how to fix" on an image of a bike part will surface instructional videos or blog posts.

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extension exists. Integration analysis, logistic regression is estimating the parameters of logistic model which is the form of binary regression. In order to introduce this logistic regression to the students, this course of logistic regression for text classification is generated for all the graduates and postgraduates students who wish to begin with data science and machine learning for natural language processing. This course content contains video lectures which will give you the basic understanding of theoretical concepts of logistic regression along with the overview of the Practical implementation. This course have used the application domain of movie reviews for sentiment analysis from textual data.

Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Python has become a defacto lingua franca for machine learning. It is not a difficult language to learn, but if you are not particularly familiar with the language, there are some tips that can help you learn faster or better. In this post, you will discover what is the right way to learn a programming language and how to get help. How to Learn Python for Machine Learning Photo by Federico Di Dio, some rights reserved. There are many ways to learn a language, same for natural languages like English, or programming language like Python.

If you want to learn how to perform the most useful statistical analyses in the R program, you have come to the right place. Now you don't have to scour the web endlessly in order to find how to do a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to perform a sequential regression analysis or how to compute the Cronbach's alpha. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to perform association tests in R, both parametric and non-parametric: the Pearson correlation, the Spearman and Kendall correlation, the partial correlation and the chi-square test for independence.