Go programmers love Go's simplicity, ease of deployment, and tooling. But what if you want to infuse a little more intelligence in your Go applications? In this course, Pachyderm data scientist Daniel Whitenack, Ph.D., shows Go pros how to build and train a predictive machine learning app that combines native Go with a cool ML algorithm called linear regression.

Apple just released the newest version of MacOS. But that doesn't mean you should install it. Despite the fact that the new software is shiny and exciting, it's also a little bit risky. So it's worth being patient, and perhaps waiting to install the new software. There's a range of reasons to wait it out until Apple has refined and updated the operating system.

Scala has emerged as an important tool for performing various data analysis tasks efficiently. This video will help you leverage popular Scala libraries and tools to perform core data analysis tasks with ease. This course will give you everything that you need to perform data analysis with Scala libraries. You will master loading raw datasets with Spark, and perform exploratory data analysis on them via plotting. Along the way you will learn what Spark has to offer when it comes to transforming datasets and how you can build a statistical model of a dataset with Spark.

AUCKLAND, NEW ZEALAND - AUGUST 03: Isuru Fernando of IBM speaks during the TILT / Fresh Directions In Healthcare Marketing seminar on August 3, 2017 in Auckland, New Zealand. TILT is an interactive seminar designed to inspire healthcare marketers to use digital tools to communicate with their customers. In May 2014, IBM announced the acquisition of an AI startup, Cognea, that developed a cognitive computing and conversational artificial intelligence platform. IBM aimed to integrate it with Watson, the company's question-answering supercomputer, for more real conversations with users. Since then, it's been a wave of sorts with technology companies like Facebook, Microsoft, Google, Amazon and others investing in their own AI efforts while acquiring several startups in the space.

About this course: Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You'll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way.

Appin Technology Lab (ATL Jaipur) is a technical training provider company. We provide comprehensive training in Industrial Automation, Information Security, BIG DATA-HADOOP, Cloud Computing, Salesforce, Embedded Systems & Robotics, Mobile Apps Development, AUTOCAD, MATLAB, Programming (Microsoft .NET, PHP, Java, C, C), Networks & Database. Appin Technology Lab is providing a broad foundation for a revolution in higher education worldwide. The advent of the Internet and other information technologies can make teaching and research readily available to scholars and students across the globe. With the changing global scenario and India turning out to be knowledge based economy like US, there is a huge requirement of technology professionals worldwide.

AWS Training and Certification helps you advance your knowledge with practical skills so you can get more out of the AWS Cloud. We now have two new courses to help you learn about how to leverage artificial intelligence (AI) solutions using AWS: Introduction to Machine Learning web-based training and Deep Learning on AWS instructor-led training. If you are looking to learn more about how you can put AI capabilities to use, we recommend that you start with the web-based training. Developers looking to learn more should then attend the one-day instructor-led training. Introduction to Machine Learning is a free 40 minute web-based training intended for developers, solutions architects, and IT decision makers who already know the foundations of working with AWS.

Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that everyone seems to be talking about. The first part will focus on introducing tensorflow, go through some applications and touch upon the architecture. This series is excerpts from a Webinar tutorial series I have conducted as part of the United Network of Professionals. Time to time I will be referring to some of the slides that I used there as part of the talk to make it clearer.

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. In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.