Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow. That said, there is nothing better than reading data science books to get the ball rolling. Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more. Just like other books of Headfirst, the tone of this book is friendly and conversational and the best book for data science to start with. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. There are a lot of pictures and graphics and bits on the sides that are easy to remember. You can find some good real-life examples to keep you hooked on to the book.

An essential part of argumentation-based reasoning is to identify arguments in favor and against a statement or query, select the acceptable ones, and then determine whether or not the original statement should be accepted. We present here an abstract framework that considers two independent forms of argument interaction--support and conflict--and is able to represent distinctive information associated with these arguments. This information can enable additional actions such as: (i) a more in-depth analysis of the relations between the arguments; (ii) a representation of the user's posture to help in focusing the argumentative process, optimizing the values of attributes associated with certain arguments; and (iii) an enhancement of the semantics taking advantage of the availability of richer information about argument acceptability. Thus, the classical semantic definitions are enhanced by analyzing a set of postulates they satisfy. Finally, a polynomial-time algorithm to perform the labeling process is introduced, in which the argument interactions are considered.

Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting many desirable properties, such as continual learning without forgetting, forward transfer and backward transfer of knowledge, and learning a new concept or task with only a few examples. Several lines of machine learning research, such as lifelong machine learning, few-shot learning, and transfer learning attempt to capture these properties. However, most previous approaches can only demonstrate subsets of these properties, often by different complex mechanisms. In this work, we propose a simple yet powerful unified deep learning framework that supports almost all of these properties and approaches through one central mechanism. Experiments on toy examples support our claims. We also draw connections between many peculiarities of human learning (such as memory loss and "rain man") and our framework. As academics, we often lack resources required to build and train, deep neural networks with billions of parameters on hundreds of TPUs. Thus, while our framework is still conceptual, and our experiment results are surely not SOTA, we hope that this unified lifelong learning framework inspires new work towards large-scale experiments and understanding human learning in general. This paper is summarized in two short YouTube videos: https://youtu.be/gCuUyGETbTU (part 1) and https://youtu.be/XsaGI01b-1o (part 2).

It is (2017) considered bugs as implementation errors, gaining more and more attention these days since similar to software bugs, while Cadamuro et al. explanations are necessary in several applications, (2016) defined a bug as a particularly damaging especially in high-stake domains such as healthcare, or inexplicable test error. In this paper, we follow law, transportation, and finance (Adadi and the definition of (model) bugs from Adebayo Berrada, 2018). Some researchers have explored et al. (2020) as contamination in the learning and/or various merits of explanations to humans, such as prediction pipeline that makes the model produce supporting human decision makings (Lai and Tan, incorrect predictions or learn error-causing associations.

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss the benefits of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Currently there is no clear framework or methodology on how to create such an effective mixture. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of HI and human-centered AI of the 21st century, while benefiting CS as well. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This "HI lens" provides the CS community with an overview of several ways to utilize the combination of AI and human intelligence in their projects. It also allows the AI community to gain ideas on how developing AI in CS projects can further their own field.

This course teaches you an in-depth analysis of Linear Regression. We cover the theory and coding part together for better understanding. You will lea

The editors at Solutions Review have compiled this list of the best deep learning courses on Coursera to consider if you're looking to grow your skills. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Based on artificial neural networks and representation learning, deep learning can be supervised, semi-supervised or unsupervised. Deep learning models are commonly based on convolutional neural networks but can also include propositional f formulas or latent variables organized by layer. With this in mind, we've compiled this list of the best deep learning courses on Coursera if you're looking to grow your skills for work or play.

Are you ready to start your path to becoming a Data Scientist or ML Engineer? This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

It's hard to explain the mix of emotions that spark upon seeing a photo of Frederick Douglass come alive with the click of a button. And yet, there he is, blinking and nodding as if he were just alive yesterday, as if he hadn't died in 1895, years before film recording became commonplace. His animated image and others like it -- at the same time unsettling, emotional, and a bit fantastical, are made possible by Deep Nostalgia, an artificial intelligence program from the genealogy platform MyHeritage. As far as AI-animated images go, the technology behind these Harry Potter-esque photos isn't particularly complex. Users are invited to supply old photos of their loved ones, and the program uses deep learning to apply predetermined movements to their facial features.

Podcasts are a great way to learn about novel fields and tools, as well as keeping yourself updated with the fields that you care about. I also believe that podcasts, which are mainly centered around interviews, are a great way to learn about the rock stars and superheroes of the AI world. You get a glimpse of how they think, what they are working on, and how they solved a particular problem. I would also argue that the content you get access to by listening to podcasts is unique, and you cannot access them somewhere else. In this post, I am not going into the details of why I think podcasts are great and Machine Learning learners and practitioners should listen to them. Here are the podcasts that I highly recommend for ML learners and professionals.