As of this writing, there are over 100 episodes for listeners to dive into. The show explores the opportunities and techniques driving big data, data science, and AI. 67. Average episode duration: 15 minutes. Listen now. Linear Digressions. Linear Digressions. Linear Digressions. Many people are interested in indulging themselves in this field or industry but often get confused about how to start and where to start. As technology is growing the more our cities are getting harnessed with high-tech security. This podcast is very accessible to anyone who has an interest in data science with a light background in any form of math, statistics, or the sciences. Area(s) of focus: Data science and machine learning concepts applied to real-world issues . Image Source: Edynamiclearning. At the end of last year, I realized I am often missing out on new development in the industry, unknowingly reinventing the wheel, and falling short in conversations with experts. Are there other data science podcasts missing from this list that you can recommend? The Data Skeptic Podcast has a range of topics related to data science, statistics, machine learning, and the like. The hosts are good friends, and their rapport makes each episode very accessible and easy to understand. Linear Digressions Udacity Technology 4.8 • 328 Ratings; Listen on Apple Podcasts. In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. Kyle, the host, is specifically passionate about scientific methods and applying critical thinking in problem-solving. All good things must come to an end, including this podcast. AI & Data Science Technology English. ⭐ What I like about it: there is usually an overarching theme that runs for multiple episodes, the topics are covered extensively. Katie and Ben explore machine learning and data science through interesting (and often very unusual) applications. Read Re - Release: Factorization Machines by with a free trial. This weekly podcast also breaks down complex data science problems into digestible chunks that can be appreciated by casual listeners. Thanks for reading ⭐ Follow me on Medium, LinkedIn, or visit my Website. I’m organizing the list based on the target audience, reading time and what I like about it. Description It’s time for our latest installation in the series on artificial intelligence agents beating humans at games that we thought were safe from the robots. They are great at explaining complex subjects in a way that is engaging and simple for early learners. Linear regression analysis is the most widely used of all statistical techniques: it is the study of linear, additive relationships between variables. K Nearest Neighbors. Listen now. ~ Katie Malone, thank you! I also find this newsletter includes more data engineering and ML deployment types of articles. Hey, sick of the election yet? We started Linear Digressions 4 years ago… this isn’t a technical episode, just two buddies shooting the breeze about something we’ve somehow built together. Linear Digressions: Put on by Udacity, explores some interesting/fringe applications of Data Science techniques. Collaborative Data Science Symposium. I also really enjoy reading comments on articles, and Medium is the one I found having the most comments and the community is generally nice. Linear Digressions. Very casual, but often just as interesting. In each episode (238 in total) of linear digressions, ... Lex Fridman that hosts this podcast is one of the best AI and data science speakers out there. Target audience: there’s a column for everyone. Data Stories The show covers a bit about everything: data science, machine learning, model interpretability, career trajectory, etc. I’m always on the lookout for good newsletters and podcasts to follow, please leave a comment on good materials for me to follow. Upvote and share lineardigressions.com, save it to a list or send it to a friend. She was also the instructor of Udacity’s Introduction to Machine Learning course, and hosts Linear Digressions, a weekly podcast on data science and machine learning. Overall Rating - How good was this content? Photo by Perfecto Capucine on Unsplash. 7. Since January, I started regular consumption of blogs and podcasts to keep myself relevant. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Host Kai Ryssdal and our team of reporters bring you clear explorations of how economic news affects you, through stories, conversations, newsworthy numbers and more. There is a wide range of topics that range from purely technical to how to lead data science teams and other business information. What level of learner would you say is the target audience of this content? Linear Digressions. I have an Inbox Zero OCD, so I trimmed down my subscription to make sure that I get what I need without drowning in information. Linear Digressions covers the messiest of topics in the field of data science, machine learning and artificial intelligence. ... - Cohost Linear Digressions, Udacity’s podcast about Data Science Instructor (4) A Lot. Early Beginner - was totally new to this topic / had few prerequisites. Linear Digressions. 289 Avsnitt | Teknik O’Reilly newsletter is a good weekly round-up of industry insights. Listen now. Udacity Podcast: No longer in production, but featured some good episodes on the subject of AI. Data Sci Guide Content Added Dec 2015 – Dec 2016, Multithreaded (stitchfix technology blog), Doing Data Science: Straight Talk from the Frontline, Test and Code – A Podcast about Software Testing, Software Development, and Python, R for Marketing Research and Analytics (Use R! Linear Digressions logo from SoundCloud. Advanced Beginner - was new to this topic / had some prerequisites, How much did/do you personally learn/gain from this content? ⭐ What I like about it: the guests on the show are very well-versed and respected in their respective areas. The hosts, Ben Jaffe and Katie Malone, manage to break down complex data science problems and techniques into snippets of information that can be easily digested by the casual listener. (RIP Linear Digressions.) If you’re a data scientist, you know how important it is to keep your data orderly, clean, moving smoothly between different systems, well-documented… there’s a ton of work that goes into building and maintain… This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Future of Life Institute: Weekly podcast about everything that could go wrong….very wrong. The beginning episodes are good for introduction and beginners and as the theme progresses the content becomes challenging but fun. Linear digression is all about ML and Data Science, and centers on how ML is solving a multitude of issues around the world, and achieve massive breakthroughs deemed impossible before. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago. I get updates on Project Jupyter through the Jupyter Blog, and I first found out about Streamlit on Medium. Target audience: industry professionals, more towards machine learning engineers as a lot of the episodes are related to model deployment. I listen to podcasts when I get ready in the morning or when I cook dinner at night. So as my goal for 2020, I decided to set myself free of this stress. Since I also subscribe to publications that are not data science related, it helps me give my brain a break by reading up some other stuff. Linear Digressions is a relaxed, friendly podcast full of insightful conversations on a range of data science issues. Target audience: both aspiring and senior data scientists and engineers. Katie is a data science consultant, so I find her examples and frame of thinking helps me a lot in communicating with my clients. Being a data science consultant, my stress-level exploded as I constantly felt under-prepared going into client meetings. Intelligence, Deep learning enthusiasts and AI are Two separate newsletters indulging themselves in this or. Episodes ; Contact ; So long, and opinion pieces multiple weeks present these Linear. List based on the web, iPad, iPhone and Android ; listen on Apple podcasts this is. Gear up for 2019 is usually an overarching theme that runs for multiple episodes the! 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