Machine Learning A-Z™
Hands-On Python & R In Data Science
Interested in the field of Machine Learning? Then this course is for you.
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Reddit Posts and Comments
0 posts • 30 mentions • top 28 shown below
365 points • Hellr0x
100-days Data Science Challenge!
One month ago I made this post about starting my curriculum for DS/ML and got lots of great advice, suggestions, and feedback. Through this month I have not skipped a single day and I plan to continue my streak for 100 days. Also, I made some changes in my "curriculum" and wanted to provide some updates and feedback on my experience. There's tons of information and resources out there and it's really easy to get overwhelmed (Which I did before I came up with this plan), so maybe this can help others to organize better and get started.
- Linear Algebra:
- Udemy course: Become a Linear Algebra Master
- Book: Linear Algebra Done Right
- YouTube: Essence of linear algebra
I've been doing exercises from the book mainly but the Udemy course helps to explain some topics which seem confusing in the book. 3Blue1Brown YT is a great supplement as it helps to visualize all the concepts which are massive for understanding topics and application of the Linear algebra. I'm through 2/3 of the class and it already helps a lot with statistics part so it's must-do if you have not learned linear algebra before
- Statistical Learning
- Book: An Introduction to Statistical Learning with Application in R
- YouTube 1: Data Science Analytics
- YouTube 2: StatQuest
ITSL is a great introductory book and I'm halfway through. Well explained with great examples, lab works and exercises. The book uses R but as a part of python practice, I'm reproducing all the lab works and exercises in Python. Usually, it's challenging but I learn way more doing this. (If you'll need python codes for this book's lab works let me know and I can share) The DSA YT channel just follows the ITSL chapter by chapter so it's a great way to read the book make notes and watch their videos simultaneously. StatQuest is an alternative YT channel that explains ML concepts clearly. After I'm done with ITSL I plan to continue with a more advanced book from the same authors
- I use the Dataquest Data Science path and usually, I do one-two missions per day. The program is well-structured and gives what you will need at the job, but has a small number of exercises. So when you learn something it's a good idea to get some data and practice on it.
- Udemy: Machine Learning A-Z
- I use their videos after I finish the chapter in ITSL to see how t code regressions etc. But their explanation of statistics behind models is limited and vague. Anyway, a good tutorial for coding
- Book: Think Python
- Good intro book in python. I know the majority of concepts from this book but exercises are sweet and here and there I encounter some new topic.
- Mainly for SQL practice. I spend around 40 minutes to 1 hour per day (usually 5 days per week). I can solve 70-80% of easy questions on my own. Plan to move to mediums when I'm done with Dataquest specialization.
- Nothin massive yet. Mainly trying to collect, clean and organize data. Lots of you suggested getting really good at it, as usual, that's what entry-level analysts do so here I am. After a couple of days, I'm returning to my previous code to see where I can make my code more readable. Where I can replace lines of code with function not to be redundant and make more reusable code. And of course, asking for feedback. It amazes me how completely unknown people can take their time to give you comprehensive and thorough feedback!
I spend 4-5 hours minimum every day on the listed activities. I'm recording time when I actually study because it helps me to reduce the noise (scrolling on Reddit, FB, Linkedin, etc.). I'm doing 25-minute cycles (25 minutes uninterrupted study than a 5-minute break). At the end of the day, I'm writing a summary of what I learned during that day and what is the plan for the next day. These practices help a lot to stay organized and really stick to the plan. On the lazy days, I'm just reminding myself how bad I will feel If I skip the day and break the streak and how much gratification I will receive If I complete the challenge. That keeps me motivated. Plus material is really captivating for me and that's another stimulus.
What can be a good way to improve my coding, stats or math? any books, courses, or practice will you recommend continuing my journey?
Any questions, suggestions, and feedback are welcome and encouraged! :D
2 points • chaitu9701
This course is a great place to start off for data science and is also really inexpensive 5$ for lifetime access. Just go through the python part and skip the r part of the course.
1 points • mw203
Hey! I highly recommend the udemy course Machine Learning a-z. https://www.udemy.com/course/machinelearning/
1 points • ZurditoBagley
1 points • default52
Ah....I assumed you meant "uploaded the full [pirated lecture portion of the] course" like this udemy machine learning course.
I mean you COULD complete the course without doing any work...but what would be the point of getting credentials without the experience.
Conversely I could download tensorflow and help with some random handwriting recognition project, which produces the experience without credentials.
So, let me rephrase the question: if you were browsing machine learning jobs, what would you cite as your professional credentials?
1 points • Prynslion
The team that created Machine Learning A - Z. Its a popular course and I see it in a lot of websites recommended for starting in Machine Learning
1 points • suckmybumfluff
Thanks for the info friend. Is this the course you are talking about?
1 points • toe_the_unstubber
https://www.udemy.com/course/machinelearning/ It has a bunch of different machine-learning methods, not just Deep Learning. The same company also has a Deep Learning course, but I haven't taken that one yet.
1 points • cshoneybadger
Cool, a certification will definitely help you. You can try online resources in the mean time. Udemy, Udacity, Coursera, LinkedIn Learning, Linux Academy, ai.google are pretty good resources that you can use and learn from in the meantime.
I am a cloud and big data engineer and have used Udemy a lot. It is fairly cheap and has a "sale" almost every few days. A ML Engineer at my company recommended me Machine Learning A-Z™: Hands-On Python & R In Data Science course. I enver got around to finish this course and it's not exactly a certification but pretty dope for learning.
1 points • Selty_
J'ai souvenir d'un cours Udemy sur le ML (avec choix Python ou R en langage) qui était vraiment pas mal.
// ah voila, c'était lui.
1 points • TroutLaunderer
I really liked Machine Learning A-Z: Hands-On Python & R In Data Science.
There’s a lot of courses out there, but I really like this one because you get practical examples you can use immediately in the real world.
You won’t be a machine learning expert after completing it, but you will understand the fundamentals and you will be able to create models.
After you finish this, you could start creating models at your work, or you could take more in-depth courses. Andrew Ng machine learning courses on Coursera is often recommended and is much more in-depth.
1 points • irineu1000grau
I would suggest this course
I took a time a go and it's a great start.
1 points • GodBlessThisGhetto
This was my initial foray into Python and Data Science. I found it to be really good (at least as good as any other course) at providing common sense direction into how to use machine learning and gave a really good tutorial on structuring code and building out the script needed to complete tasks.
1 points • CS_Grad_Waterloo
I found "Udemy's Machine Learning A-Z™: Hands-On Python & R In Data Science" to be great. Link below:
1 points • Scutterbum
Have you tried any Udemy courses? Don't expect masters level, but it's good for getting the hang of Data science techniques.
1 points • danooo1
>here for a progressive guide through with lots of advice
I think he is talking about this one. https://www.udemy.com/course/machinelearning/
1 points • glitchdot2
Start with loading dataset with pandas, then check logistic regression, K-nearest neighbors, Decision Trees, Random Forest. And as you learn, you will see what you need to explore more.
I started with udemy course (https://www.udemy.com/course/machinelearning/), but beside some basic terminology and concepts, I didn't learn something more. Real-life project are way more complicated than the projects presented there. But first I recommend you to check the suggested youtube channel, it looks good.
1 points • starnlm
I can tell you about Ng's ML course. I completed it last year. It is very Mathematical. All the important ML algorithms are explained in great detail with their mathematical intuition. Linear regression, logistic regression, Neural networks, Support Vector Machines, Dimensionality Reduction, Anamoly Detection, and Recommender Systems are the major topics that are covered. Along with this, Ng shares his knowledge on the nuanced topics like Regularization, Gradient Decent, Pipelining and some general advices along the way.
The only drawback of the course is that you won't be applying these algorithms to real world datasets. All you will be doing is coding out the algorithms in OCTAVE or MATLAB which I think is pretty much outdated. Python and R are widely used for Machine Learning now. When you will start participating in ML competitions on Kaggle etc., you will have no idea what to do. You don't need to write down the algorithms, you'll need to simply import the module. You will end up taking another course. The course is also not updated much after it's release, it is kind of a classical course.
If knowing the Math behind the above mentioned algorithms is your aim then go for it (Probability models and Tree Models are not included in the course).
If you are more inclined towards learning the practical implementation, I suggest you enroll to Krill Eremenko's Udemy course Machine Learning A-Z™: Hands-On Python & R In Data Science'. He is a great tutor and his teaching is very early to follow. Also, if you want to understand how algorithms actually work, you MUST subscribe to StatQuest on YouTube. He is the best out there. I hope this helps.
2 points • carhawk95
Which course should I choose?
Hi guys, first time posting and the english is not my mother tounge, so sorry in case of any mistake. I really wanto to get into ML, and I'd like to purchase a Udemy course taking advantage of the discounts, however I'm between two possiblities and I don't know which one could be better for me, the courses are:
Machine Learning A-Z™: Hands-On Python & R In Data Science (https://www.udemy.com/course/machinelearning/)
Python for Data Science and Machine Learning Bootcamp (https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/)
I'd really appreciate any advice or help
1 points • kushtoma451
Try not to stress too much, just absorb everything you can in your internship. There are a few discount courses online to help you learn skills. I wouldn't expect to know everything there is to know from such courses but maybe you'll have some use with them. I'll link them below.
Just follow your mentors guidance. Study material, get that presentation ready and continue on being an active learner.
1 points • bedok77
If you're looking for online courses you can try the udemy ones.. I went through these when i was a beginner. https://www.udemy.com/course/machinelearning/ and https://www.udemy.com/course/deeplearning ). Then I did the Google Tensorflow course on coursera and the linked kaggle NYC taxi fare prediction challenge. The udemy one covers a wide range of topics from statistics, machine learning and AI but its basic, for desktop modelling, not production scale. The tensorflow course is more narrow on just linear, non-linear models and fully connected neural networks , but prepares you for production scale coding.
1 points • jolasman
Hi, I recommend getting some course from Coursera or from Udemy. I attended two courses on Udemy that explain the basic concepts of AI and give you a good background to start developing your skills by putting your hands dirty by coding.
1 points • Sphagnum_Shuffle
Thanks! There are nowadays lots of good courses/tutorials to get you started and I'm gonna provide few courses/books here that I found useful in my ML journey:
- http://faculty.marshall.usc.edu/gareth-james/ISL/ Excellent book to get you started. This books contains moderate amount of math but I found this one still easy to grasp. Book provides also nice R code snippet to test models on different datasets
- https://www.udemy.com/course/machinelearning/ This is a great (but lengthy) course to get you started in Machine Learning. This basically skips most of the math and goes straight into hands on learning with Python and R provided for this course. In my opinion this a good starting point
Those two were deal breakers for me that helped me to get into Machine Learning. Remember that learning ML is not a sprint it is a marathon
1 points • ErikPOD
Machine learning / Data science is such a huge field and sadly I don't have a optimal way to break into it. Usually I needed like 2-3 courses on each topic before I understood it.
I think this one is a good starter:
It has its problems.
- You almost just watch half of it, because you are only watching the python stuff ,not the R.
-It is a lot of just typing what they are typing.
I have taken a course from Lazy programmer in reinforcement learning, that I liked. It was the complete opposite of the course above. Few coding instructions and alot of thinking for your self. He got courses on introduction to machine learning, which i haven't taken though.
The first course I took was Andrew Ngs machine learning course.
It is very math-heavy. You code in Matlab/octave. I liked it but it don't think it is for everyone.
I am not sure if my time invested in machine learning / Data science was worth it, since I feel that I would have to invest very much more time before anyone would hire me because of my machine learning skills. I did get an offer to do my bachelor thesis on a machine-learning company. I chose to start working instead. I sometimes regret it but it is impossible to know which decision I should have made.
1 points • T-Flexercise
My favorite thing about computer science is that the kinds of things that you do as a professional are basically the same as the things you do as a beginner. They're just more complex and build on those basics. So if you want to find out if you would like coding and want to dedicate yourself to it more, the best thing to do is to find any kind of basic training course in the specific thing you're interested in, follow it all the way through, and see if you like it. Then, try to make something in that area. As you're trying to make your own thing that you're interested in, there will be questions that you don't know the answer to, and you can take additional courses to answer those questions.
My favorite place to learn the specific answers to questions is stack overflow and the documentation for the programming language I'm writing in. They'll have the most detail.
But my favorite place to learn a new skill in software is Udemy. The courses are like $12 each, and if you pick one that's popular, they're usually really good. They're almost all project based, so instead of spending hours trying to teach you all the basics before you put it together, they walk you through a project from beginning to end, so you learn how to structure a project overall and how the pieces fit together, and you have context not just for how to write a Singleton, for example, but for why you'd want to use a Singleton.
Since you mention wanting to get into data analysis, if you are already confident in math in general, I'd highly recommend this Machine Learning course https://www.udemy.com/course/machinelearning/
If you want more a friendly approach to see if you like writing software, I'd recommend either this course for Web Development: https://www.udemy.com/course/the-complete-web-developer-course-2/
Or this course if you're more interested in making video games: https://www.udemy.com/course/unitycourse/
Both are very project based, interesting, and easy to understand and apply what you've learned to your own projects.
But super duper important, if you're going to use Udemy, they go on sale all the goddamn time. All the courses say "This is $100". They don't cost $100. Wait for a sale or find a coupon code, you'll get it for under $20.
2 points • isroy9
Few free data science courses on Udemy for 3 days (Offer ending today)
- Data Science A-Z™: Real-Life Data Science Exercises Included
- R Programming A-Z™: R For Data Science With Real Exercises!
- Python A-Z™: Python For Data Science With Real Exercises!
- Tableau 10 A-Z: Hands-On Tableau Training For Data Science!
- Power BI A-Z: Hands-On Power BI Training For Data Science!
- Python for Statistical Analysis
- Machine Learning A-Z™: Hands-On Python & R In Data Science
1 points • asterisk2a
Retrain something in the IT field (Customer Service Support Role, Programming role, Sales role)?
A start could be a free course on the internet to try yourself out and see if you like it. Download VSCode (for Windows) and learn some little Python and see if you like it.
MIT Open Coursware Python late 2016 MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016 Instructor: Dr. Ana Bell
Or, Horticulture? RHS said there are not enough Horticulturists.
1 points • Manavendra4288
Several highly rated DS, ML, Python, Tableau, PowerBI video learning courses are free to purchase (only for 1 day), buy them before they are gone tomorrow. pass these to others who may be interested.