Daily, Weekly, Monthly, and Yearly Goal Tips to Guide a Self-Taught Data Scientist in 2023 (December 2022, 11 minutes) A good plan is key to reaching your learning goals, and Madison Hunter is here to help with a robust roadmap for building one that is both ambitious and sustainable. How to Explore Machine Learning and Natural Language Processing as a High School Student (July 2022, 12 minutes) This helpful guide by Carolyn Wang might be framed around her own experience as a high school student, but it's a helpful introduction to ML and NLP for aspiring practitioners of all ages. The Simple Things a Data Science Beginner Needs to Know (December 2022, 11 minutes) Ken Jee's recent resource is an accessible, up-to-date primer for anyone taking their first steps in data science this year. Here Are My 3 Suggestions for Newcomers (April 2022, 5 minutes) For all the independent learners out there who choose not to follow an established curriculum, Soner Yıldırım offers a few key insights based on his own experience as a self-taught data professional. A Brief Introduction to Neural Networks: A Regression Problem (December 2022, 12 minutes) How do you go about learning a complex technical topic from scratch?

This is a third article on the topic of guided projects feedback analysis. The main idea of the topic is to analyse the responses learners are receiving on the forum page. Dataquest encourages its learners to publish their guided projects on their forum, after publishing other learners or staff members can share their opinion of the project. In our previous post we've done a basic data analysis of numerical data and dove deep into analyzing the text data of feedback posts. In this article, we'll try multiple packages to enhance our text analysis.

Researchers from Western University, SUNY Buffalo State College, University of Cincinnati, and City University of Hong Kong published a new paper in the Journal of Marketing that presents a methodological framework for managers to extract and monitor information related to products and their attributes from consumer reviews. Understanding how concrete product attributes form higher-level benefits for consumers can benefit various corporate teams. Concrete, or "engineered attributes" refer to technical specifications and product features. For example, in the context of tablet computers, such attributes include RAM, CPU, weight, and screen resolution. Understanding how combinations of these lower-level attributes form higher-level benefits, or "meta-attributes," for consumers, such as Hardware and Connectivity, can provide managers with actionable insights.

Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are just three of the most effective technology that our contemporary society has access to. They can process information in enormous quantities in a manner that no human being could expect to attain, and they'll reevaluate how we look at each part of our lives. At precisely the exact same time, they may be pretty complex to comprehend, particularly for those that aren't utilized to working together with new technologies. The dilemma is that you can not just bury your head in the sand and expect AI, ML, and NLP will move off. Since society will proceed without you and you are going to wind up getting left behind.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. It is an area of ... Read More

Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are three of the most powerful technologies that our modern society has access to. They can process data in huge quantities in a way that no human being could hope to achieve, and they will revolutionize the way we look at every aspect of our lives. At the same time, they can be pretty complicated to understand, especially for people who aren't used to working with new technologies. The problem is that you can't just bury your head in the sand and hope that AI, ML, and NLP will go away. Because society will move on without you and you'll end up getting left behind.

Two decades after its debut in the 2001 Series 7, BMW's iDrive infotainment system is among the best on the market. It's about to get even better -- think, natural language processing, gesture control and cloud-based machine learning -- with the release of its latest iteration, iDrive 8, aboard the upcoming BMW iX and i4. The system's onboard AI, BMW Intelligent Personal Assistant, will be the driver's primary point of contact when interacting with the new iDrive 8. The driver will be able to give the IPA a personalized name and then cue up various in-vehicle functions and information streams using either verbal or non-verbal commands. The new iDrive 8 has also been issued a face of sorts, "spheres of light in differing sizes and brightness levels, giving the assistant more space and new ways of expressing itself," per a BMW press release.

By Swapnil Shinde, a three-time entrepreneur, angel investor, and CEO and co-founder of Zeni, the automated finance management platform for startups. If you've found yourself thinking, "There's an AI-powered solution for everything these days," you're not far off. Today, more than 37% of organizations have implemented artificial intelligence (AI) in some form, and it is estimated that by 2021, 80% of emerging technologies will have AI foundations. When successfully executed, AI is a powerful tool for businesses and consumers alike. It can help businesses scale faster by automating time-consuming processes, create deeper connections with customers through hyper-personalized interactions, make smarter business decisions with access to real-time data from across an organization, plus so much more.

If you've found yourself thinking, "There's an AI-powered solution for everything these days," you're not far off. Today, more than 37% of organizations have implemented artificial intelligence (AI) in some form, and it is estimated that by 2021, 80% of emerging technologies will have AI foundations. When successfully executed, AI is a powerful tool for businesses and consumers alike. It can help businesses scale faster by automating time-consuming processes, create deeper connections with customers through hyper-personalized interactions, make smarter business decisions with access to real-time data from across an organization, plus so much more. But time and time again, we read about promising AI-powered startups coming up short, shutting down their businesses because they've failed to build meaningful AI solutions for the problems they hoped to solve. After 10-plus years of leveraging AI, machine learning and natural language processing to build and grow successful AI-powered platforms, I've identified three key areas where most startups and businesses go wrong when building AI-powered platforms.

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.