Machine learning specialization reddit. I want to follow the ML specialization.
Machine learning specialization reddit You may find yourself cutting corners on learning, and possibly not enjoying the program due to having to do so much. Animals and Pets Anime Art Cars and Motor Vehicles Crafts and DIY Culture, Race, and Ethnicity Ethics and Philosophy Fashion Food and Drink History Hobbies Law Learning and Education Military Movies Music Place Podcasts and Streamers Politics Programming Reading, Writing, and Literature Religion and Spirituality Science Tabletop Games The final capstone project in Coursera's Machine Learning and similar specializations is a worthwhile investment, IMHO. I would like to stick to a learning path that ensures I have no learning gaps and makes me job-ready. Hello, fellow redditors, data/ml engineers, and data scientists, I took huge interest in Deep Learning after finishing that Machine Learning Crash course from Google and as I'm working my way through the on that Machine Learning course from Coursera/Stanford (had done once years long ago, so, I feel like a due recycling is needed before I jump straight up into a DL course) The most popular, OG and (even after price increase) crazy cheap degree programme we all know. Hello, just got admitted for spring 2024. The deep learning specialization? (conflicted on this one because I think it'd be too soon) Read hands-on machine learning with scikit-learn, keras, and tensorflow. With all this knowledge I've decided to start with Machine Learning Specialization by Andrew Ng on Coursera and then after that specialization (that consists of 3 courses), I'll see where to head next. #39 in Best of Coursera: Reddsera has aggregated all Reddit submissions and comments that mention Coursera's "Machine Learning" specialization from University of Washington. I have finished the Deep Learning Specialization on Coursera during this quarantine period. I'm guessing that the other courses will be high-level/conceptual too. I haven't paid this much for the Deep Learning Specialization itself. 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. ai edX's Introduction to Artificial Intelligence (AI) course: edx. AI (this is a specialization) or Machine learning (from Stanford University) Edit: I kinda already took DL and was hoping it would be the same, but now people are telling me I should've start with ML the course paid would make . Check out decision trees. Also, because that course is popular it's okay if it does not help you. My plan is to delve into deep learning after I finish it through Prof. AI: Machine Learning Coursera Specialization University of Washington: Machine Learning Coursera Specialization Which of these would be a better course? I am thrilled to embark on my journey at Georgia Tech's OMSCS program this upcoming semester, but I find myself torn between two captivating specializations: Machine Learning and Computing Systems. Reply reply More replies More replies More replies Here is my target course list. I've researched the courses involved in each track and, thanks to ionic-tonic's excellent course planner , have even charted my preferred course Reviews on (Mathematics for Machine Learning and Data Science Specialization) by DeepLearning. Uses python 2. A subreddit dedicated to learning machine learning Members Online I started my ML journey in 2015 and changed from software developer to staff machine learning engineer at FAANG. ai have a 3 module course on all the math needed for ML. Go to the Machine Learning Specialization. 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. In the decade since the first Machine Learning course debuted, Python has become the primary programming language for AI applications. I want to follow the ML specialization. Build Intelligent Applications Go to the main page for the specialization and scroll down to see the courses. You need to change your mindset. The company I work for offer an increasing number of free courses. At the same time, however, I am interested in doing a project right after I finish the ML course. In light of what was once a free offering that is now paid, I have open sourced my notes and submissions for the lab assignments, in hopes people can follow along with the material. I think I know what I will be doing this summer!! I'm taking the machine learning specialization on Coursera currently on week 1 of course 3. Don't know about the new version though. See what Reddit thinks about this specialization and how it stacks up against other Coursera offerings. However I don't have as much insight, so I want to learn from Imperial College of London's specialization. I’m specifically interested in ML and Deep learning specialization but noticed that it is not available through coursera plus. Put together a list of the classes you'd like to take, then see which specialization allows you to take the courses you're most interested in. To me it was boring and too detailed in the foundations of machine learning. Here's my two cents from an industry perspective, having done ML at FAANG for several years, launching one of the top Cloud service ML API's, launching many internal models, failing quite a bit on many other projects, and having already graduated from OMSCS. I can't find much info online comparing these courses. I want to eventually move into machine learning engineering which is what made me very interested in the ML specialization in OMSCS. Although payed exists (the reason why autocorrection didn't help you), it is only correct in: . Now I had the fundamentals of machine learning and deep learning, but I still felt lost, so I called my friend, he's a data scientist, and asked him for help, he pointed out that what I'm missing is fundamentals of programming so I took algorithmic toolbox course on Coursera followed by python 3 specialization and finished it with data After researching, it was almost a unanimous opinion that Coursera's Machine Learning by Andrew Ng / Deeplearning. Just finished the regression course and it was excellent; if this level of quality continues it might be the best bet. FTFY. stanford. First IBM's data science specialization, second IBM's machine learning specialization and last Andrew NG's deep learning specialization. . If you just want to say "I know machine learning", then just learn about regression then cross validation. Also the programming modules were very specific in how they wanted you to solve them (variable names/approach) which doesn't match with the level Related Coursera e-learning Learning Learning and Education forward back r/datascience A space for data science professionals to engage in discussions and debates on the subject of data science. Ng's deep learning specialization on Coursera. My long term goal is to do machine learning engineer track after graduation. Any general suggestion on which course should I take firstly? I am not sure on order-wise. So, I'm learning ML specialization from deeplearning. University of Washington Machine Learning Track (Still being released, currently on course 2/6): Supposed to be a comprehensive overview of modern machine learning methods. Can anyone give the pros r/learnmachinelearning • If you are looking for free courses about AI, LLMs, CV, or NLP, I created the repository with links to resources that I found super high quality and helpful. And I have no idea which one to take. There are a lot of questions asking what classes one should take if one aims to specialize in machine learning. - small note: you may need to ask for special permission for your summer 2024 semester in order to take two classes. I only heard IBM's courses are more practical rather than theory, other than that no idea. I get the impression that the Machine Learning Specialization by Andrew Ng is more "elementary", and since I've already employed things like regression professionally and even had my own project in Keras, would I perhaps benefit more from the Deep Learning course which states to be "intermediate"? Hi Guys, As the title says, I have just finished Andrew NG's deep learning speciailization, I feel quite proud of it, especially having finished his Machine Learning courses prior, in like 2 months total, However, most of the code I have written so far, is just snippets of code that'd I just write in Jupyter, I feel like this course really lacks some real hands-on project work, and this is the Currently I'm most interested in the notebooks from week 3 of his 'Supervised Machine Learning: Regression and Classification' course, i. I'm on week 2 of the first course, which is super conceptual. 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. 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 Machine learning is just statistics with cross validation. edu Machine Learning specialization is a beginner course that most people getting into ML take it. The tool of choice is essentially irrelevant when first learning, as first off, syntax is viewed as easy to learn nowadays, especially with assistance tools like ChatGPT / stack overflow / virtual documentation. After finishing it I have got 3 courses that I couldn't decide which to take. Machine Learning focus: My MS in Stats was heavy in mathematical statistics, theory of linear models, statistical inference, experimentation, high dimensional stats (my research area). IMO going that far into the weeds should be for higher education with real lectures and discussion with peers/instructor. I have done some projects before, using Keras, mainly on text data. Feel free to share any educational resources of machine learning. I switched to the new one, finished it, and went on to do the Deep Learning specialization (I just started the last of five courses). At that point, see different options like Datacamp, maybe? Here is the link to the Google Doc - Deep Learning, Neural Networks, and Machine Learning I took the specialization a while ago and my notes are now about 80 pages long. I am really loving it, so far. But I don't feel confident enough to write low level Tensorflow code or Pytorch code on my own. If the difficulty is the only issue, I recommend you try and change your learning methods to finish the course. Unfortunately this is not a self-paced course, but a new session is about to start. MIT Open Learning Library's Introduction to Machine Learning. Over the years I self taught myself more machine learning based models (they are still stats models lol) like random forest, boosting, NN etc. Any advice would greatly help and sorry if this is a repetitive post, I tried looking for any posts on the new 2022 course but couldn't find any. So be open to the possibility of completing the program more slowly. Skillpro's Machine Learning course by by Juan Galvan: skillpro. It includes ample exercises that involve both theoretical studies as well as empirical applications. Machine learning (not deep learning) course doesn't use PyTorch and Keras, at least for the old version. Deep Learning specialization by deeplearning. ai and will be available in June. Like which one first. Check out projects in Make Magazine for example. The most iconic MOOC to ever exist? 'Machine Learning Engineer' also ranges from roles that are 90% software engineering, 10% algorithm etc development to the other way round. So if you want to excel as a data scientist or AI professional in industry, you are going to have to compete with quants. Which coursera course is better, Deeplearning. ai. But I believe after learning the basics of machine learning from this course, one can switch to open-source packages like scikit-learn, or use Mahout for large-scale machine learning. Dec 19, 2024 · Feel free to share any educational resources of machine learning. 7 64 bit and GraphLab software. Thanks for helping! CS 7641 Machine Learning CSE 6740 Computational Data Analysis: Learning, Mining, and Computation I'd recommend it if your situation is anything like mine: you know machine learning and just need to get up to speed on how people are doing projects with large-ish data sets and tensorflow. A subreddit dedicated for learning machine learning. Scroll down to the list of the 3 courses (do NOT click on "Enroll for Free") Click on the first course "Supervised Machine Learning: Regression and Classification" Now click on "Enroll for Free" In the pop-up, click the tiny "Audit" link at the bottom Repeat steps 3-5 for the other 2 courses But I just wanted to get a perspective on this given my experience. I’m a data science professional working in tech looking to up my skills with Deep learning (Application on vision & NLP-Language models etc). the very first course in his ML specialization. The books are written in a conversational style where concepts are explained like you were speaking to the author. fast. I thought you might use the lectures from Broderick as a way to complement the. Select one, and on the next page click the big red "Enroll for Free". Those notebooks have this cool interactive feature that lets you click on a plot to add or delete data points, and lets you explore how these changes can in For the past many months (7-8+) I've spent a lot of time learning Machine Learning Algorithms with a heavy focus on neural nets / deep learning. If you're new to machine learning, it's way too focused and the deep dives on implementation would probably be overkill and painful. and then after completion of that specialization I got enrolled in Deep Learning specialization by Andrew Ng. What do you think about the classes I want to take, any recommendations? Core CS 6515 Introduction to Graduate Algorithms CS 7641 Machine Learning Electives CS 7642 Reinforcement Learning and Decision Making CS 7643 Deep Learning CS 7650 Natural Language I am currently taking Prof Andrew Ng's machine learning course on Coursera. org Stanford University's CS229: Machine Learning course: cs229. e. These were built initially for the grads which join the company, to get up to speed, but the company got lots of requests to open source them. Welcome to /r/LearnMachineLearning!. Learn the underlying theories of Statistical Analysis, Data Modelling, and Machine Learning. So, I probably wouldn't worry much about an individual course certificate, unless you plan to complete a series of courses and do the capstone project. Machine learning is primarily applied statistical methods and that’s where most AI research is at these days. Be prepared to be trolled if you don't even know how to read the rules, read the orientation document, or do a simple Google search. I hope this helps you if you are taking the specialization too or if you are just interested. I know the required Mathematics Pre-Requisites, since I studied 3 courses on Engineering Mathematics and Applied Numerical Techniques during my Bachelor in Mechanical Engineering. It will be replaced by a more in-depth Machine Learning Specialization by Stanford Online and Deeplearning. The new Machine Learning Specialization includes an expanded list of topics that focus on the most crucial machine learning concepts (such as decision trees) and tools (such as TensorFlow). We would like to show you a description here but the site won’t allow us. I have watched the first few lectures of Andrew Ng's course and found the machine learning specialization introduction course to be even more basic and intuitive. I have found the first two courses to be very useful. Prioritize course selection over specialization. Hi everyone, I recently completed Andrew Ng's three courses in machine learning through Coursera. The better specialization is the one that allows you to take the courses you most want to take. io Coursera's Machine Learning course by Andrew Ng: coursera. 036 online course from the Open Learning Library, because it provides automatic feedback and representative exercises to learn the material. My short-term goal is to become job ready in 6-12 months and my long-term goal is to advance my career toward Machine Learning Engineering. I have an engineering degree and am aware of the math behind ML to a certain degree. Here, you can feel free to ask any question regarding machine learning. 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. AI Discussion So I'm currently doing this Specialization primarily for Linear Algebra and I wanted to ask is it worth it to continue and how much of deep knowledge I'll gain from it? Deep Learning Specialization by Andrew Ng or IBM machine learning professional certificate Help My goals are to learn ML to forecast time series data (using RNN's), which I wish to quickly apply. Coursera's Machine Learning Specialization. Thanks in advance. At the bottom of the little popup, there is an option to audit the course - choose that. If your math isn't great or rusty, I would take ISYE 6644: Simulation instead, it would count as an elective, but you'd get more out of it. org Fast. MITx's Machine Learning with Python: from Linear Models to Deep Learning. Second, I'm developing an intuitive understanding of how the algorithms work and when to Hello, I have been working on a 3 volume series of books on mathematics for machine learning. It costs 14k INR (approx 170USD) which just seems a bit much. 6. ai's Practical Deep Learning for Coders course: course. First, I am looking at the mathematical expressions and reasoning about them, then translating that to code. How prepared would the ML specialization make someone to get a job as a machine learning engineer and be successful at it? Does the specialization go very deep into machine learning, or is it just very cursory? They are different courses, one goes into machine learning and the other into deep learning. Wᴇʟᴄᴏᴍᴇ ᴛᴏ ʀ/SGExᴀᴍs – the largest community on reddit discussing education and student life in Singapore! SGExams is also more than a subreddit - we're a registered nonprofit that organises initiatives supporting students' academics, career guidance, mental health and holistic development, such as webinars and mentorship programmes. 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. If you just want to do machine learning for a task, then buy or borrow a platform. Since this is pricey and it has been a while that I'm in a classroom where discipline was mandatory, figured to ask here on how to get the most out of this course. Sometimes a 'machine learning engineer' and a 'data scientist' do similar things, depending on the role description! Sometimes it's just using pre-built models like SageMaker. ai, was the best intro course to get started in the space. I'm currently taking DeepLearning AI's MLOps Specialization via Coursera. 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. I am looking for my first formal course in machine learning and currently trying to decide between these two popular alternatives: Stanford/DeepLearning. If your math is good, you might consider taking CS 6210 AOS instead of Bayesian or if you want another ML elective, CS 7646: Machine Learning for Trading might be a good choice. In my opinion, if you're planning on taking the Deep Learning specialization, the first two courses reviews nearly all of what I learned in the Machine Learning specialization, except in more detail. Hi all, I'm thinking about getting 2 maybe 3 months of Coursera and do Andrew Ng's ML course (not the DL specialization). I got enrolled in Machine Learning Specialization by Andrew Ng. Mathematics for Machine Learning and Data Science Specialization Coursera & Deeplearning. itcb jhynw ewt ygu matmj bfi fjsugox pucezw selbn qnycao