Data Scientists are most in demand & enjoy one of the top-paying jobs in the industry, with an average salary of $120,000 as per the data from Glassdoor and Indeed. So whom is the course for? If you are a student, an IT professional, an analyst, a scientist or an academic and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. If you've got some programming or scripting knowledge, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This course is a short course on Data science to enable you to start learning & using the fundamentals immediately.

About this course: Machine learning is transforming the world around us. To become successful, you'd better know what kinds of problems can be solved with machine learning, and how they can be solved. Don't know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system.

About this course: For many years now, people have been improving their tools, studying the forces of nature and bringing them under control, using the energy of the nature to operate their machines. Last century is noted for the creation of machines which can operate other machines. Nowadays the creation of devices that interact with the physical world is available to anyone. Our course consists of a series of practical problems on making things that work independently: they make their own decisions, act, move, communicate with each other and people around, and control other devices. We will demonstrate how to assemble such devices and programme them using the Arduino platform as a basis.

We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumptions. We give the first computationally efficient parameter-free algorithms that work in arbitrary Banach spaces under mild smoothness assumptions; previous results applied only to Hilbert spaces. We further derive new oracle inequalities for matrix classes, non-nested convex sets, and $\mathbb{R}^{d}$ with generic regularizers. Finally, we generalize these results by providing oracle inequalities for arbitrary non-linear classes in the online supervised learning model. These results are all derived through a unified meta-algorithm scheme using a novel "multi-scale" algorithm for prediction with expert advice based on random playout, which may be of independent interest.

Machine learning (ML) is touted as the most critical skill of current times. Artificial intelligence (AI), an application of ML, is becoming pervasive. From autonomous vehicles to self-tuned databases, AI and ML are found everywhere. Industry analysts often refer to AI-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by AI and ML.

Professor Andrew Ng is the former chief scientist at Baidu, where he led the company's Artificial Intelligence Group. He is an adjunct professor at Stanford University. In 2011 he led the development of Stanford University's main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera.

Why To Pay EXTRA & Not To Take These all BENEFITS At The Least Possible Price!! ENROLL NOW VIA below LINK ONLY!!! HURRY NOW!!! In this Industrial SolidWorks: Deep Learning Of Machine Drawing course, I Akash Raj will teach you how to create sketch, parts and drawing file using the variety of tools in SolidWorks. This course is designed for the absolute beginner, meaning no previous experience with SolidWorks is required. If anyone wants to fill up his/her gap in SolidWorks, then this is also right course for them. Exercise files– to help you become proficient with the material.

A neural network is often mentioned but covers only a small part of machine learning. There is much more to explore. There are a lot of interested people out there but many do not know where to start. The difficult question basically is how to start actually learning it? Especially beginners might get discouraged because of statistics and math which is an integral part of machine learning. None the less you do not need to be a math expert to apply machine learning.

About this course: The manufacturing industry is making a digital transformation, allowing companies to customize production through advances in machine learning, sustainable design, generative design, and collaboration, with integrated design and manufacturing processes. This course introduces innovations in CAD and digital manufacturing, speaking to the rapid changes taking place that are forever transforming the future of making. This course will also explore foundational concepts behind Autodesk Fusion 360 CAD/CAM. Fusion 360 is a cloud-based CAD/CAM tool for collaborative product development that combines industrial design, mechanical engineering, and machine tool programming into one software solution. Through a series of lectures and hands-on exercises, this course provides the core philosophy behind the software.

About this course: Most professions these days require more than general intelligence. They require in addition the ability to collect, analyze and think about data. Personal life is enriched when these same skills are applied to problems in everyday life involving judgment and choice. This course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research. Concepts are defined briefly and breezily and then applied to many examples drawn from business, the media and everyday life.