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The Data Analyst Course: Complete Data Analyst Bootcamp 2022

#artificialintelligence

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!


Build 75 Powerful Data Science & Machine Learning Projects

#artificialintelligence

Implement Machine Learning Algorithms, Learn how to improve your Machine Learning Models Real life case studies and projects to understand how things are done in the real world Make robust Machine Learning models, Master Machine Learning on Python Explore how to deploy your machine learning models. According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary. This makes Data Science a highly lucrative career choice.


The Data Analyst Course: Complete Data Analyst Bootcamp 2021

#artificialintelligence

Created by Andrei Neagoie, Daniel BourkePreview this Course - GET COUPON CODE This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery! Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).


The most impressive Youtube Channels for you to Learn AI, Machine Learning, and Data Science.

#artificialintelligence

This channel publishes interviews with data scientists from big companies like Google, Uber, Airbnb, etc. From these videos, you can get an idea of what it is like to be a data scientist and acquire valuable advice to apply in your life. A new ML Youtube channel that everyone should check out, Machine Learning 101 posts explainer videos on beginner AI concepts. The channel also posts podcasts with expert data scientists and professionals working on AI in commercial industries. FreeCodeCamp is an incredible non-profit organization. It is an open-source community that offers a collection of resources that helps people learn to code for free and create their projects.


AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

arXiv.org Artificial Intelligence

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. However, these ambitions are unlikely to be achieved in a robust manner without the broader synthesis of various mechanisms and theoretical frameworks, which, at the present time, remain scattered across numerous research threads. Accordingly, this review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements. In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. Ultimately, we conclude that the notion of architectural integration deserves more discussion, without which the field of automated ML risks stifling both its technical advantages and general uptake.


A Survey on Data Pricing: from Economics to Data Science

arXiv.org Artificial Intelligence

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.


21 amazing Youtube channels for you to learn AI, Machine Learning, and Data Science for free

#artificialintelligence

This channel publishes interviews with data scientists from big companies like Google, Uber, Airbnb, etc. From these videos, you can get an idea of what it is like to be a data scientist and acquire valuable advice to apply in your life. A new ML Youtube channel that everyone should check out, Machine Learning 101 posts explainer videos on beginner AI concepts. The channel also posts podcasts with expert data scientists and professionals working on AI in commercial industries. FreeCodeCamp is an incredible non-profit organization. It is an open-source community that offers a collection of resources that helps people learn to code for free and create their projects.


How Can You Build a Career in Data Science & Machine Learning?

#artificialintelligence

Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long. However, not knowing exactly how to start a career in machine learning can lead an aspirant in the wrong way. There should be a proper agenda on how to identify the right opportunity and approach it in a systematic way. In this article, let us see some of the essential steps that one can take towards their machine learning journey.


An Approach for Time-aware Domain-based Social Influence Prediction

arXiv.org Artificial Intelligence

Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.