Inrecent years, data science has become an increasingly popular field due to the explosion of data and the need to extract valuable insights from it. While traditional education can be expensive and time-consuming, many aspiring data scientists turn to YouTube to learn the necessary skills. In this article, we've compiled a list of the best YouTube channels for learning data science for free in 2023. We cover a range of topics, including mathematics, programming, data analysis, machine learning and deep learning, career tips and guidance, interview preparation, and staying updated with the latest trends in the field. Whether you're a beginner or an experienced data scientist, these channels can help you improve your skills and knowledge in data science without breaking the bank.

A neuroscience researcher responds to Jonathan Parks-Ramage's "The Preschool." There's a famous saying, often credited to Isaac Asimov, that "today's science fiction is tomorrow's science fact." Whoever said it, they weren't wrong. The Dick Tracy two-way wrist radio is now commonplace, the Jetsons' videophone is ubiquitous (and more compact), and stun guns are often carried by police officers--although they never say "Set your phasers on stun." Today's science fiction is chock-full of ideas that are not yet fully realized, such as interstellar exploration, time travel, alien communication, teleportation, and cybernetics.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. Amanda sat at her desk, picking at the same $30 Little Gem salad she ordered daily, suffering a small burning sensation in her gut that was triggered either by acid reflux or the dying embers of her rapidly expiring conscience. Of course, it was standard procedure for her husband to demand that the security firm Dark Metal surveil potential new hires for any of his multibillion-dollar companies, but this was the first time Amanda had been involved in contracting the private intelligence agency herself. Seedlings is your venture, Reid had promised her, even though he'd named himself CEO. I want you to take the lead on this. Amanda was COO of Seedlings and reported to her husband, who dismissed Amanda's concerns about the legal ramifications of their actions. Worrying about the law was something poor people did, Reid insisted. Besides, she'd never seen Reid do anything that nefarious with this type of information. But Maggie Everett was the type of candidate that pleased Reid. Amanda had done her job, which was to find Maggie, and the people at Dark Metal had done theirs, which was to surveil her and create a comprehensive biographical profile. This seemed like overkill to Amanda. Maggie wasn't in the running to become a high-profile executive at one of Reid's billion-dollar firms. She was being interviewed to work at a preschool. Certainly, Seedlings differed from other private preschools--there was the possibility Maggie would be exposed to confidential information. But this was what NDAs were for. Unleashing a network of spies upon a poor teacher who would ultimately be responsible for 10 toddlers seemed like an absurd waste of resources. And this was just Phase 1. Phase 2 would have to wait until after Maggie was hired, of course. Amanda reopened Dark Metal's inch-thick dossier. The logline: Maggie was smart but stupid. Smart: She'd majored in English at Yale, then received an MFA in creative writing from Brown, and finally a master's in early childhood education from Columbia. Stupid: She'd accumulated $103,345 in student debt, which she'd never pay off unless she took a job somewhere like Seedlings.

The education and learning system has undergone significant changes since the outbreak of Covid-19. Online learning has become more prevalent as many schools and universities have shifted their classes online, using video conferencing and other digital tools to deliver instruction. Since the pandemic, some institutions have adopted a hybrid approach, combining both online and in-person teaching. This has led to an increased reliance on technology in the classroom, and professors have had to learn how to use new technology tools, including AI platforms, to effectively teach in an online environment. And these AI, and other emerging technologies,systems are incredibly important when we talk about security of the learning space.

Here's a question that's been percolating since ChatGPT abruptly entered the mainstream: Does AI provide more avenues to enhance and augment education, or drive it into obsolescence? According to Under 30 Europe lister Joel Hellermark, the future of artificial intelligence and machine learning is rife with possibilities that can help the ways in which humans learn and collaborate, not replace them. He offered the calculator as a comparison: "If we think about it just like an insanely powerful calculator, you'd want everyone to just learn to use the calculator. Why should you sit there and do a bunch of calculations? The 26-year-old cofounder of software company Sana Labs has been immersed in the coding space since taking online Stanford courses at just 13 years old in Sweden. Now, at his startup, he's built an AI-driven software to help businesses manage workforce onboarding and training. The program pulls from correspondences, documents and the internet to answer questions and help train employees. Sana introduced the product to the world just as it was shutting down in 2020, and initially offered their platform to hospitals free of charge (over 2,000 took them up on the offer). Sana has since landed paying clients, including Klarna, Merck and Electrolux, and has raised $54.5 million. Hellermark, who dropped out of school at 19 to start the company, envisions a near future where the content we interact with is presented to us dynamically and with our personal contexts in play. "We're so used to creating content and then someone consumes the exact thing that you created– that goes all the way back to the printing press," says Hellermark. "It hasn't changed that much since.

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.

The size and the computational load of fine-tuning large-scale pre-trained neural network are becoming two major obstacles in adopting machine learning in many applications. Continual learning (CL) can serve as a remedy through enabling knowledge-transfer across sequentially arriving tasks which relaxes the need to fine-tune all network weights from scratch. However, existing CL algorithms primarily consider learning unimodal vision-only or language-only tasks. We develop a transformer-based CL architecture for learning bimodal vision-and-language tasks based on increasing the number of the learnable parameters dynamically and using knowledge distillation. The new additional parameters are used to specialize the network for each task. Our approach enables sharing information between the tasks while addressing the challenge of catastrophic forgetting. Our approach is scalable learning to a large number of tasks because it requires little memory and time overhead. Our model reaches state-of-the-art performance on challenging vision-and-language tasks.

Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging problem. CKE from text is known for suffering from the inherent sparsity and reporting bias of commonsense in text. Visual perception, on the other hand, contains rich commonsense knowledge about real-world entities, e.g., (person, can_hold, bottle), which can serve as promising sources for acquiring grounded commonsense knowledge. In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances. To address the problem, CLEVER leverages vision-language pre-training models for deep understanding of each image in the bag, and selects informative instances from the bag to summarize commonsense entity relations via a novel contrastive attention mechanism. Comprehensive experimental results in held-out and human evaluation show that CLEVER can extract commonsense knowledge in promising quality, outperforming pre-trained language model-based methods by 3.9 AUC and 6.4 mAUC points. The predicted commonsense scores show strong correlation with human judgment with a 0.78 Spearman coefficient. Moreover, the extracted commonsense can also be grounded into images with reasonable interpretability. The data and codes can be obtained at https://github.com/thunlp/CLEVER.

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks.