In this Machine Learning tutorial we will learn about How to Handle Fake News with Machine Learning. In today's fast-paced digital world, spreading fake news has become a significant concern. With the increasing ease of access to social media platforms and other online sources of information, it has become more challenging to distinguish between real and fake news. In this project-based article, we will learn how to build a machine-learning model to detect fake news accurately. This article was published as a part of the Data Science Blogathon.

Created by Lazy Programmer Inc., Lazy Programmer Team 13.5 hours on-demand video course This Math 0-1: Calculus for Data Science & Machine Learning course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this Math 0-1: Calculus for Data Science & Machine Learning course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.

Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINNs integrate the physical constraints by minimizing the partial differential equations (PDEs) residuals on a set of collocation points. The distribution of these collocation points appears to have a huge impact on the performance of PINNs and the assessment of the sampling methods for these points is still an active topic. In this paper, we propose a Fixed-Budget Online Adaptive Learning (FBOAL) method, which decomposes the domain into sub-domains, for training collocation points based on local maxima and local minima of the PDEs residuals. The effectiveness of FBOAL is demonstrated for non-parameterized and parameterized problems. The comparison with other adaptive sampling methods is also illustrated. The numerical results demonstrate important gains in terms of the accuracy and computational cost of PINNs with FBOAL over the classical PINNs with non-adaptive collocation points. We also apply FBOAL in a complex industrial application involving coupling between mechanical and thermal fields. We show that FBOAL is able to identify the high-gradient locations and even give better predictions for some physical fields than the classical PINNs with collocation points sampled on a pre-adapted finite element mesh built thanks to numerical expert knowledge. From the present study, it is expected that the use of FBOAL will help to improve the conventional numerical solver in the construction of the mesh.

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. This work focuses on the context of online and blended learning and the use case of student success prediction models. We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. Our analyses cover five course pairs that differ in one educationally relevant aspect and two popular instance-based explainable AI methods (LIME and SHAP). We quantitatively compare the distances between the explanations across courses and methods. We then validate the explanations of LIME and SHAP with 26 semi-structured interviews of university-level educators regarding which features they believe contribute most to student success, which explanations they trust most, and how they could transform these insights into actionable course design decisions. Our results show that quantitatively, explainers significantly disagree with each other about what is important, and qualitatively, experts themselves do not agree on which explanations are most trustworthy. All code, extended results, and the interview protocol are provided at https://github.com/epfl-ml4ed/trusting-explainers.

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world.How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics:state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth.We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the community to use and further develop behavior trees, associated tools, and analysis techniques.

In this day and age, the requirement for experienced professionals in Artificial Intelligence (AI) is rapidly increasing along with the progression of technology and automation. Artificial Intelligence Course in Delhi is created to provide students with the aptitude and expertise to become successful AI professionals. The course begins with an overview of the fundamentals of AI such as its history, definition, and applications, followed by the different types of AI, its tools and techniques, and the ethical and social implications it has on the society. Furthermore, students will be educated on natural language processing, computer vision, robotics, and cognitive computing to develop AI-based solutions. The course also covers the use of AI in finance, healthcare, and other industries, as well as the strategies needed to overcome the challenges associated with developing AI solutions.

February may technically be the shortest month but it certainly can feel long… I think I sensed a slight brightening in the morning light but I may have been mistaken… Maybe time for a bit of distraction with a wrap up of data science developments in the last month. Don't miss out on more ChatGPT fun and games in the middle section! Following is the March edition of our Royal Statistical Society Data Science and AI Section newsletter. Hopefully some interesting topics and titbits to feed your data science curiosity. If you like these, do please send on to your friends- we are looking to build a strong community of data science practitioners.

Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq)} Imitation Learning (IL) problem. The proposed Seq2Seq model produces a robot-environment interaction sequence to estimate the partially observable environment state variables. Then, the observed interaction sequence is transformed to a control sequence for the task itself. The proposed Seq2Seq IL for tactile feedback-based manipulation is experimentally validated on a door-open task in a simulated environment and a snap-on insertion task with a real robot. The model is able to learn both tasks from only 50 expert demonstrations, while state-of-the-art reinforcement learning and imitation learning methods fail.

Machine Learning Essentials (2023) Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8 projects! This hands-on course is Machine Learning Essentials (2023) designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions" Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is Machine Learning Essentials (2023) designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

ChatGPT is an AI program that can talk to anyone on the internet via text in a conversational mode. Meaning, the tool can create descriptive texts based on questions, keywords, etc. The AI and ML company OpenAI developed this tool using various ML models. One of the major ML model that the developer use is the Reinforcement Learning model. The AI and ML tool utilizes trial and error, rewards, and human feedback to learn. Slowly, it develops a database of learning models.