Machine learning course reddit. Machine Learning Crash Course with TensorFlow APIs.

Machine learning course reddit Thanks in advance. Machine Learning by Georgia Tech. More interesting might be the more advanced and graduate-level courses, that are typically harder to find. Course 4 I'm really taking my time trying to absorb everything because it finally gets into the hands on analysis/programming with Python. That's a great start. The instructors and counselors were very good, making learning both easy and enjoyable. I'm taking MIT's new (2020) machine learning course 6. I've been in this course for 8 months, and I could say that only about 5 classes were "ok". 401K subscribers in the learnmachinelearning community. The course "Learning From Data" by Abu-Mostafa is a pretty good but intensive introduction to Machine Learning. Hey guys, Just wanted to share that I've completed the "Supervised Machine Learning" course by Andrew Ng on Coursera! I dove into the basics of algorithms and model evaluation tailored for beginners, and I really feel like I've got a solid grasp on the core concepts of ML now. Here, you can feel free to ask any question regarding machine learning. I would say this course only covers the minimum basics without which you have literally no chances to understand even the description of a typical task in machine learning. This practice is important in machine learning because it makes the ML algorithm easier in creating that distinction between supervised and unsupervised machine learning. Goes into the math and theory behind machine learning and also covers many algorithms. ). 6 stars. edX: A non-profit online learning platform with machine learning courses. I like lazyprogrammer's courses on deep learning. The good news is that it can be prior experience in any subdomain in ML. However, with Coursera, arguably the most valuable component- graded labs and assignments, are locked behind subscriptions which vary in cost depending on how long you It seems like you have it figured out already. Deep learning is a form of supervised machine learning. Then, take your new skills and apply them to projects of your own interest. The list includes some introductory courses to cover all the basics of machine learning. Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy) Machine Learning with Python by IBM. The course by Volodymyr Kuleshov on Youtube is the best in case you want to master all the machine learning concepts. I started learning a couple months ago and love it. In supervised machine learning, the training data is already labeled (or annotated), allowing the system to learn more about the results desired. then move on to deep learning or any specific ml algorithms which this course does not help you achieve. A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Also, we are a beginner-friendly sub-reddit, so don't be afraid to ask questions! This can include questions that are non-technical, but still highly relevant to learning machine learning such as a systematic approach to a machine learning problem. If you're excited by projects such as GANs, I'd recommend looking at Fastai, and / or Coursera's deep learning specialization first. ai, was the best intro course to get started in the space. 036 through the MIT Open Learning Library. 10-708 - Probabilistic Graphical Models, covers modern graphical methods in ML, quite a heavy course, but definitely useful (and will become more useful in the future) 10716 - Advanced Machine Learning, a super theoretical dive into ML, very geared towards proof based methods and algorithmic guarantees of ML frameworks. ai Sep 11, 2024 · Feel free to share any educational resources of machine learning. There are many good courses on machine learning available online. so my question is for folks who have gone through the crouse does this course really give hand on pratice and if you know any materials ro refer for improving my practical learning is much appreciated. Machine learning is a rapidly evolving field, and keeping abreast of new techniques and advancements is essential. See what Reddit thinks about this specialization and how it stacks up against other Coursera offerings. The Keras model with replacement seems works fine with inference, just if I try to do "Full integer post-training quantization" then it's crashed. Please tell me some of your picks for this list. io Coursera's Machine Learning course by Andrew Ng: coursera. Machine Learning by HarvardX. I tried the MIT OCW ML courses but I definitely liked Stanford’s CS221, 229 and 230 sequence more on Youtube (for lectures) and free accompanying Coursera course (for HW, quizzes, etc). It seems like you have it figured out already. And he starts the series with linear and logistic regression which are supervised ml algorithms. The only reason I did not finish it is 80+ Free & Best Selling Discounted Courses: Python, Lean Startup, English Vocabulary, Streamlabs OBS, Google Cloud Certified Professional - Architect, Agile & Scrum in Depth, Self-Taught Programmer, Machine Learning and Many More I highly recommend MIT’s ‘No Code AI and Machine Learning’ online course. everyone within a hiring process who knows their field can judge your understanding without a certificate. Building a strong foundation, hands-on experience, and a commitment to staying informed will empower individuals to navigate the complex landscape of machine learning successfully. Roadmap for learning AI, Machine Learning, and Deep Learning to specific topics like LLMs & Stable Diffusion (Free Resources are welcome!) Help I want to build a robust understanding of AI, from core concepts like machine learning and deep learning, all the way to some of the most popular topic like Large Language Models (LLMs) and Stable This is the machine learning subreddit so I’m assuming you’re ultimately interested in applying it in that context. After 10 years and nearly 5 million enrollments, Stanford will be closing new enrollments for the Machine Learning course on Coursera from June 14, 2022. I totally regret it. Let’s take a closer look at some popular examples: Machine Learning Course Materials: A plethora of course materials exist on Reddit, many of which are free and accessible. Dec 11, 2024 · Coursera: A massive open online course platform with machine learning courses. But see, some people really jus start learning libraries first (like pandas, numpy and others). certificates make a good linkedin post though. The series is predominantly written by its stars Blake Anderson, Adam DeVine, and Anders Holm who play three recent college graduates, roommates, and co-workers at Telamericorp, a telemarketing company, living in Rancho Cucamonga, California. Hey! While browsing Reddit, I saw this Youtuber name Krish Naik mentioned various times, and how he has helped many people get a good understanding of Data Science and Machine Learning. Please share to me if any hint or guidance. Hi All! Hope you and and your families are staying healthy with Covid persisting. A subreddit dedicated to learning machine learning I created a complete overview of machine learning concepts seen in 27 data science and machine learning interviews Hey everyone, During my last interview cycle, I did 27 machine learning and data science interviews at a bunch of companies (from Google to a ~8-person YC-backed computer vision startup). However, I did try out the first course from the DeepLearning. They have a few sections that teach you prerequisite undergraduate stuff. Machine Learning Crash Course with TensorFlow APIs. u/Obvious-Strategy-379 suggestions are not just good tips. In total, the courses have 5000+ ratings and almost all of them have 4. I compiled a list of machine learning courses with video lectures. In his 6-Month Data Science Roadmap video, he mentions a course he sells called Data Science Masters with a 7-8 month duration ($50), which goes over a lot of I started with an econometric background, and then used Jose Portillas "Python for Data Science and Machine Learning" Udemy course as a starting point to teach me the ML tools. The bad news is that the number 1 thing that employers look for while hiring for machine learning roles is some form of prior experience. Perfect for anyone new to AI and machine learning. Much harder than any of the above. AI Math for ML program for a short time and did really really like it. I keep reading how the next country to "win" the AI race will be the next super power, so it sounds like there may be plenty of work in the field of AI, hence the reason this thread caught my attention. While I highly recommend ztdl, Im sure I got much more out of it because of taking the Andrew Ng machine learning course first. FTFY. After researching, it was almost a unanimous opinion that Coursera's Machine Learning by Andrew Ng / Deeplearning. One of the books I would recommend is Deep Learning by Ian Goodfellow. Thanks. Then move on to other Learning from Data (Caltech, edX): Great machine learning theory course. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. Lastly, practice continuous learning and stay curious. If you just want to do machine learning for a task, then buy or borrow a platform. Check out projects in Make Magazine for example. Although payed exists (the reason why autocorrection didn't help you), it is only correct in: . Nov 29, 2019 · Machine Learning Series (Lazy Programmer Inc. Thanks for this information. 45 votes, 31 comments. Are the courses really the same in terms of material covered, or are the course paid would make . I'm not even watching the classes because they're not worth the time. Some of the most popular ones include: Skillpro's Machine Learning course by by Juan Galvan: skillpro. Feb 17, 2025 · Reddit is brimming with valuable resources that you can leverage to learn about machine learning. If the difficulty is the only issue, I recommend you try and change your learning methods to finish the course. It includes ample exercises that involve both theoretical studies as well as empirical applications. If you just want to say "I know machine learning", then just learn about regression then cross validation. /Udemy): Taught by a data scientist/big data engineer/full stack software engineer with an impressive resume, Lazy Programmer currently has a series of 16 machine learning-focused courses on Udemy. We would like to show you a description here but the site won’t allow us. So, I'm learning ML specialization from deeplearning. I always try to watch, but 90% of the time is a waste. Check out decision trees. It will be replaced by a more in-depth Machine Learning Specialization by Stanford Online and Deeplearning. I’d recommend you pick up a text like Deep Learning (Goodfellow, Bengio, Courville) and work from the basics presented there. fastai's practical deep learning for coders part 1 should be your next stop. Then you can take Courera's new lecture on GANs. org Fast. Related Machine learning Computer science Information & communications technology Technology forward back r/InteriorDesign Interior Design is the art and science of understanding people's behavior to create functional spaces within a building. I'm starting to get into machine learning and my professor recommended me this youtube playlist with a course by Andrew Ng from Stanford (I believe it's from 2012). Like I could prove lots of machine learning convergence theorems using lyapunov functions and whatnot, and I can use a solid mathematical intuition to take stabs at architectural improvements for models, but I couldn't for example tell you the first thing about how to train a model like GPT-3 with billions of neurons and gazillions of weights and biases distributed across many machines. true. I work in education and focus on helping people how to learn better as well. Talking about Machine Learning, the first name that strikes my mind is Hero Vired’s Data Science, Artificial Intelligence & Machine Learning programme. It’s a great introduction to AI, with clear, engaging content and practical, no-code exercises. Probably, it's a good introduction to the terminology, but definitely this is no way enough to confidently read textbooks or papers in ML. ai. Any language can be used. The prevailing wisdom is that if you're looking to transition into machine learning then use these courses to build up a portfolio of work applying a variety of techniques. Also, because that course is popular it's okay if it does not help you. (Session based, so not always open, but lecture available on youtube. It only gets more technical from there which I'm looking forward to, machine learning being course 8. I will continue to update this list, as I find suitable material. At that point, see different options like Datacamp, maybe? Machine learning is just statistics with cross validation. Graduate level discussion of topics. All of the homework questions have solutions which is great, but not the discussion-based labs a That being said, if you want to excel and be a master at machine learning, you, of course, need to refer some extra materials too, to get the basics cleared, like linear algebra and probability. At the bottom of the little popup, there is an option to audit the course - choose that. Are there any good free or more cost-effective alternatives? How about LinkedIn courses? YouTube courses seem too small and basic for a better understanding. Machine Learning specialization is a beginner course that most people getting into ML take it. Select one, and on the next page click the big red "Enroll for Free". ai and will be available in June. Since I like learning in a structured way and a setting similar to academics, i thought I will start learning from mit ocw courses. Instead, doing a default dynamic post-training quantization seems can be done normally. Course in Machine Learning (CIML) by Daume (this one was my first book! It gives a high level understanding of topics, and it really helped me improve my understanding) Understanding Machine Learning: from theory to algorithms by Shai Ben-David and Shai Shalev-Shwartz. OpenCV: A computer vision library for machine learning and image processing. You will find resources like Jupyter Notebook templates for data analysis #39 in Best of Coursera: Reddsera has aggregated all Reddit submissions and comments that mention Coursera's "Machine Learning" specialization from University of Washington. If you don't include enough playing around, working on your own projects, being able to iterate and experiment quickly, and being able to learn when you need to learn something new vs learning everything or most of it in advance Machine Learning by Stanford university on Coursera by andrew NG Machine Learning Crash Course with TensorFlow APIs by google Machine learning career track by Springboard This are my picks. 186 votes, 45 comments. My close acquaintance has pursued the programme, and I can give you multiple reasons why starting your career advancement from this portal is beneficial for you. If you like delving into math and doing things by hand (such as building algorithms by computing linear algebra from scratch) definitely go for Andrew's one. Happy learning! There are SO many online machine learning classes out there today, making it really difficult to know which ones are the best for learning. He has explained the fundamentals very well. Machine Learning for Data Science and Analytics by ColumbiaX. It is useful from time to time and I like having it as reference A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Theory heavy. But I am not sure how to get started. By following these steps and tips, you’ll be well-equipped to learn machine learning on Reddit. It isvery hard to get a job in machine learning as a fresher. ai's Practical Deep Learning for Coders course: course. Hi, I'm in the Machine Learning switch up carreer. His course is much more about being hands on, building stuff. I want to learn machine learning to a level where I master the basics completely. This course is a complete waste of money. Since I've completed a number of such courses, I thought I'd put together a list of the online courses I thought had the highest quality content for machine learning, deep learning, and machine learning in I found a Coursera course - Machine Learning Introduction by Andrew Ng, but for that I'll have to pay a hefty sum of money. What I don't like about the Andrew Ng course is that it covers the mechanics of machine learning and not mathematics as such. other than these here are some of the best machine Learning as a rule of thumb: certificates are a money-making scheme only the issuer profits from. They are almost a necessity. Workaholics is a television sitcom that premiered on Comedy Central on April 6, 2011. One should go for a proper and broad understanding of ml algorithms. It has 9 courses to get through and honestly the first 3 I blew through in a few days. Go to the main page for the specialization and scroll down to see the courses. But i will strongly recommend going through NG's entire course first as Jeremy Howard (the instructor of fastai's course) doesn't go into as much detail as NG does about the theoretical stuff. Nautical context, when it means to paint a surface, or to cover with something like tar or resin in order to make it waterproof or corrosion-resistant. One benefit about this course is that it includes a textbook, "An Introduction to Statistical Learning," that expands on many of the concepts of ML algorithms. . I've already taken a ML course which gave some good rigor to dimensionality reduction (PCA, LDA), clustering (K-means, EM), neural nets (three-layer fully-connected, convolusional), and then some kernel methods. I'm taking a Linear Algebra for Machine Learning course and a Probability and Statistics for Deep Learning course through my local university's (UCSD) extension program. Build Intelligent Applications A subreddit dedicated to learning machine learning Members Online If you are looking for free courses about Computer Vision, NLP, Deep Learning or Generative AI I've created a repository with links to resources that I found to be of super high quality and helpful. The goal of the r/ArtificialIntelligence is to provide a gateway to the many different facets of the Artificial Intelligence community, and to promote discussion relating to the ideas and concepts that we know of as AI. I took Ng's machine learning course a year or two ago, and just recently finished udemy's zero to deep learning course. However, I've found a more recent updated playlist from 2022 which appears to cover the same topics but updated. fast. Start by learning how to code, then take Andrew Ng's machine learning course. ghysd dmxr kphh fwsey lxbew shx kqyhcoav injw nucud ndozxdz