If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As the Head Coach of The Data School, I need to be on top of all things Tableau so that I can pass that knowledge onto our trainees. What I really hope they pick up is my method for learning and my constant to desire to get better at my craft. While that's fantastic, unless they have an approach to problem solving, a thirst for learning, and a passion for sharing, I have failed as their coach, teacher, and mentor. A poster and web site to assist designers and journalists to select the optimal symbology for data visualisations, by the Financial Times Visual Journalism Team. The FT Visual Vocabulary is at the core of a newsroom-wide training session aimed at improving chart literacy.
If you're interested in the exciting world of data science, but don't know where to start, Data School is here to help. Data science can be an overwhelming field. Many people will tell you that you can't become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. So, what exactly is data science? This workflow doesn't necessarily require advanced mathematics, a mastery of deep learning, or many of the other skills listed above.
Machine learning is the predictive heart of big data analytics, and one of the key skills that separates data scientists from mere analysts. But getting started with machine learning can be a challenge. Here are a few ways beginners can get off the ground with their machine learning adventure. Machine learning is a vast field with many different specialties, so it's quite easy for a beginner to get overwhelmed. For instance, one specialty called deep learning powers many of today's artificial intelligence breakthroughs.
Have you heard about machine learning, but you don't really understand what it's good for? Or you understand the basic idea, but you're struggling to apply it using Python? In this video, I'll explain the essential ideas behind machine learning. Then, we'll build our first machine learning model in just a few lines of code using Python's scikit-learn library. This is a recording of a webcast hosted by Trey Hunner of Weekly Python Chat: http://www.weeklypython.chat
Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. Subscribe to the Data School newsletter: http://www.dataschool.io/subscribe/ OTHER RESOURCES My scikit-learn video series: https://www.youtube.com/playlist?list... My pandas video series: https://www.youtube.com/playlist?list... JOIN THE DATA SCHOOL COMMUNITY Blog: http://www.dataschool.io