The most successful businesses base their decisions on cold, hard facts. That is one thing that has led to the growth of big data and given data scientists everywhere job security. If you want to switch to one of the most in-demand jobs in the tech industry, starting with learning the easiest and most popular programming language, then the affordable Complete Python Data Science Bundle is all you need. Best of all, you can train at your own pace without having to take any time away from your current job. You don't even have to complete all of the courses in this bundle before you are qualified to start applying for jobs because you can jump right in with An Easy Introduction to Python and become a programmer within a matter of hours.

This course will be a part of series of Free ML Courses to become an expert of ML. This course presents the concepts of Supervised Machine Learning, Unsupervised Machine Learning, Regression and Classification. It covers implementation of Simple Linear Regression.

The technology industry has fallen in love with artificial intelligence, also known as AI. With applications that range from automated customer service to high-end data services, artificial intelligence is popping up all over the place. You can use AI for everything from "mining" social data, automating customer growth and data collection, to driving customer engagement to optimizing logistics and overall efficiency. In this special report you will learn how to successfully build, grow and expand your business using artificial intelligence. PLR eBooks has not yet entered any information about themselves yet.

Tactile sensing is critical for humans to perform everyday tasks. While significant progress has been made in analyzing object grasping from vision, it remains unclear how we can utilize tactile sensing to reason about and model the dynamics of hand-object interactions. In this work, we employ a high-resolution tactile glove to perform four different interactive activities on a diversified set of objects. We build our model on a cross-modal learning framework and generate the labels using a visual processing pipeline to supervise the tactile model, which can then be used on its own during the test time. The tactile model aims to predict the 3d locations of both the hand and the object purely from the touch data by combining a predictive model and a contrastive learning module. This framework can reason about the interaction patterns from the tactile data, hallucinate the changes in the environment, estimate the uncertainty of the prediction, and generalize to unseen objects. We also provide detailed ablation studies regarding different system designs as well as visualizations of the predicted trajectories. This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.

Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. In this work, we collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training. However, the number of reviews per product is large (320 on average), making summarization - and especially training a summarizer - impractical. Moreover, the content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates. In order to deal with both of these challenges, we formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets. The choice of the review subset is treated as a latent variable, predicted by a small and simple selector. The subset is then fed into a more powerful summarizer. For joint training, we use amortized variational inference and policy gradient methods. Our experiments demonstrate the importance of selecting informative reviews resulting in improved quality of summaries and reduced hallucinations.

Humans excel at learning long-horizon tasks from demonstrations augmented with textual commentary, as evidenced by the burgeoning popularity of tutorial videos online. Intuitively, this capability can be separated into 2 distinct subtasks - first, dividing a long-horizon demonstration sequence into semantically meaningful events; second, adapting such events into meaningful behaviors in one's own environment. Here, we present Video2Skill (V2S), which attempts to extend this capability to artificial agents by allowing a robot arm to learn from human cooking videos. We first use sequence-to-sequence Auto-Encoder style architectures to learn a temporal latent space for events in long-horizon demonstrations. We then transfer these representations to the robotic target domain, using a small amount of offline and unrelated interaction data (sequences of state-action pairs of the robot arm controlled by an expert) to adapt these events into actionable representations, i.e., skills. Through experiments, we demonstrate that our approach results in self-supervised analogy learning, where the agent learns to draw analogies between motions in human demonstration data and behaviors in the robotic environment. We also demonstrate the efficacy of our approach on model learning - demonstrating how Video2Skill utilizes prior knowledge from human demonstration to outperform traditional model learning of long-horizon dynamics. Finally, we demonstrate the utility of our approach for non-tabula rasa decision-making, i.e, utilizing video demonstration for zero-shot skill generation.

ODSC APAC 2021 is right around the corner this September 15–16th, and while there's something for everyone, NLP is sticking out as one of the focal points of this conference. Natural language processing is indeed special in the APAC region, namely because there's a greater need for diverse datasets, due to the number of different languages spoken in the region. This has lead researchers to develop novel and exciting techniques to address these concerns. At ODSC APAC in a few weeks, you'll be able to hear from these data scientists about NLP, and hear from some research institutions that focus on NLP Natural language processing (NLP) has made truly impressive progress in recent years and is being deployed in an ever-increasing range of user-facing settings. Accompanied by this progress has been a growing realization of inequities in the performance of naively-trained NLP models for users of different demographics, with minorities typically experiencing lower performance levels.

Note that there is also a repository of this article with all the resources clearly identified for you to follow in order as well. In my opinion, the best way to start learning anything is with short YouTube video introductions. This field is no exception. There are thousands of amazing videos and playlists that teach important machine learning concepts for free on this platform, and you should definitely take advantage of them. Here, I list a few of the best videos I found that will give you a great first introduction to the terms you need to know to get started in the field.

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The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating Online Hostile Posts in Regional Languages during Emergency Situations, Commonsense Knowledge Graphs, Content Authoring and Design, Deep Learning on Graphs: Methods and Applications, Designing AI for Telehealth, 9th Dialog System Technology Challenge, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, 5th International Workshop on Health Intelligence, Hybrid Artificial Intelligence, Imagining Post-COVID Education with AI, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture During Training, Meta-Learning and Co-Hosted Competition, ...