Mathematics for Machine Learning

I want to get started on machine learning, but I have some doubts about the prerequisites

Basically, I want to learn enough to get a "feel" of how its like to develop in the area, to see if thats what I want to specialize myself in. I searched around for a bit and saw that some of the prerequisites for understanding machine learning are linear algebra and multivariate calculus. Because of that I was thinking of enrolling myself in this specialization in coursera:

https://www.coursera.org/specializations/mathematics-machine-learning

But I am not sure exactly if thats enough, and thats what I would like to know here. However, if its not, I would like to know what else I would need to learn regarding these specific fields, keeping in mind that I am not trying to become a researcher or anything of the sort, rather I am just exploring the area. Thanks in advance.

https://www.coursera.org/specializations/mathematics-machine-learning

There is https://www.coursera.org/specializations/mathematics-machine-learning course series on coursera. If that is too basic for you then read the book 'all of statistics' from Larry Wasserman and do all the problems in the book.

Coursera has a couple of classes that might help...

https://www.coursera.org/learn/datasciencemathskills

https://www.coursera.org/specializations/mathematics-machine-learning

https://www.coursera.org/specializations/mathematics-machine-learning

Mathematics for Machine Learning | Imperial College London https://www.coursera.org/specializations/mathematics-machine-learning

If you prefer being spoonfed then choose the coursework/non-thesis option. If you have 3 months before the program starts, you should do Mathematics for Machine Learning Specialization on Coursera to refresh your math. After this, you’re all set!

Απειροστικό λογισμό (Calculus), γραμμική άλγεβρα (Linear Algebra), πιθανότητες/στατιστική (Probability theory & Statistics).

Υπάρχει και σχετική ειδίκευση στο Coursera, αλλά από ότι βλέπω δεν σου δείχνει καθόλου πιθανότητες:

https://www.coursera.org/specializations/mathematics-machine-learning

For machine learning in Python and R: https://www.udemy.com/machinelearning/ And after if you're interested in deep learning: https://www.udemy.com/deeplearning/ Andrew ng course is very interesting for the theory behind the algorithms, if you need to train yourself in mathematics you can also follow this course: https://www.coursera.org/specializations/mathematics-machine-learning

No Brasil eu ainda não tive contato com pessoas de humanas trabalhando com DS. O que eu sei é que no US tem muita gente migrando pra essa área, e muitos fazendo sucesso.

Eu respondi pra outra pessoa que interdisciplinaridade é sempre maravilhosa. Nesse caso eu considero mais ainda, dado que leis de proteção aos dados estão surgindo em muitos países (incluindo aqui).

Não acho que seja necessário uma formação técnica, desde que você tivesse conhecimento nos tópicos básicos da área.

No seu caso, aconselho começar relembrando matemática do ensino médio. Depois pega esse curso aqui:

https://www.coursera.org/specializations/mathematics-machine-learning

Pra você não desmotivar, vai fazendo em paralelo os cursos da Alura. É uma ótima porta de entrada.

Depois disso provavelmente você já vai ter uma base pra saber sozinho onde atacar. Na dúvida, consulte os livros que deixei no post. Eles são ótimos materiais de consulta, além de serem ótimos guias.

The only path to getting good at something is practicing it! It helps to start with smaller things and explore around, so a bit of spreading yourself thin at first isn't a waste of time at all!

The first time I launched a webapp I did the whole nine and built it from the ground up on a LAMP stack - way more difficult than it needed to be. Since you're just practicing and playing around, there's no need to worry too much about backend and hosting, so an easy start on web stuff in Python is Flask and learn the rest from there. The last thing I built used that and it was far easier, I had workable prototypes in a couple days and was able to iterate more easily from there.

Coursera has some classes on ML mathematics that utilize Python as well! Kaggle stuff - I think - can wait until you feel more comfortable with what's going on. Since you're a "first principles" kind of learner (me too!) it'll probably feel better to build up from basics rather than backtrack from frameworks.

You might be interested in the Mathematics for Machine Learning from Imperial College London on Coursera

You might wanna try this: Mathematics for Machine Learning Specialization

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning

There's also a Coursera specialization by the authors of the book, but I think you're better off with the book because the courses are far too short and easy.

I'm doing an MSc in AI (not at the OU). A lot of machine learning is based on linear algebra, data analysis has elements of statistics in it.

The maths can be covered by the programming. By that I mean, you won't have to code a statistical analysis method in R from scratch, but you will need to know which function will carry out that, and what parameters are needed.

Same with linear algebra in machine learning. You need to understand say, that colour pictures are represented by matrices if you wanted to do anything in computer vision, so having an understanding of how matrices work, and the operations you can carry out on them is important.

Basically, you need to understand the concepts, be able to work with vectors, matrices, stats etc., but a lot of the operations will be carried out in code.

There is quite a good course on Coursera called the Mathematics of Machine Learning if you are interested.

There are some great courses on Coursera and other places specifically on math for Machine Learning. They’re over my head, but you would likely breeze through them.

Like this one:

https://www.coursera.org/specializations/mathematics-machine-learning

I'd counter argue that machine learning is edging towards a more specialised area of CS. Don't get me wrong, it's growing in popularity but it's not something most developers go into, at least not to begin with.

Even so, there are many courses available online for specialised areas like that above. Coursera for example offers a course particularly on Mathematics for Machine Learning.

I'm willing to bet the majority of developers out there are rusty in areas like "Multivariable Calculus" or "Linear Algebra". For most, it's not something that's needed for most jobs out there.

Other than my stats text books no, lol but a great crash course I took was on Coursera. From AMII school called “Intro to Applied Machine Learning”. It gives a great intro to the type of math you’ll need without getting too complicated. If you want something a little more technical and complicated, imperial college has a good program on just the math on Coursera. Link: https://www.coursera.org/specializations/mathematics-machine-learning. Another program I did was the IBM data science professional cert. touched on a bunch of different things from the math to coding it in Python. It was a great crash course but wasn’t a big fan tbh. I’d start with AMII and Imperial college. In that order. People say Stanford has a very good course/program on Coursera. I plan to take that next. Andrew Ng put it together. Basically, if you’ve taken a few stats courses, linear algebra, multivariate calculus, this stuff will be a refresher for you.

If you've already learned single variable calculus before, then I recommend Khan Academy. It's less rigorous but it's a good refresher. As for multivariable calculus (Machine Learning probably involves linear algebra and differential equations depending on the application), you'll need to put in the work. These are usually subjects covered over at least a semester each in college and are fairly demanding classes.

If you're more interested in learning just what you need for ML and not worrying about the theory behind the concepts, another commenter recommended the Maths for ML specialization on Coursera.

Check this course out — https://www.coursera.org/specializations/mathematics-machine-learning .

Stolen content: https://www.coursera.org/specializations/mathematics-machine-learning

I’m halfway through this Imperial College course (on Coursera) called Mathematics for Machine Learning and found it to be very useful. The Linear Algebra part is very good and so is the second part multi variate calculus. Haven’t started the third part on PCA yet. https://www.coursera.org/specializations/mathematics-machine-learning

I'd recommend being familiar with matrices and matrix math. Coursera has a nice series that gives a solid foundation if you are interested in ML coursework: Math for Machine Learning

I'd recommend doing the first two courses of the mathematics for machine learning specialisation on Coursera. I would have been in a similar position to you (currently studying data science MSc) but doing this helped a lot in learning the concepts and building confidence. Best of luck!

Mathematics for machine learning

You can check out Coursera's Mathematics for Machine Learning Specialization. I haven't done the specialization but have gone through few videos from it's Linear Algebra course on YouTube and it was good.

Definitely not difficult (though I do wonder what's their deal with Abelian groups, uh). However, if to find them hard, you may have a look at the Coursera specialization and ask questions on the forum. I'm not sure that's worth the time investment (I'd rather follow the 2 fast.ai courses), but YMMV.

I did one course from this https://www.coursera.org/specializations/mathematics-machine-learning specialization. Was pure linear algebra and python coding. They cover matrices transformation in space pretty good as well as operations on vectors. Pretty intense though. I have an applied math degree but still wasn’t relaxed. Buts that’s a good thing. Don’t know what’s there next. Calculus on Coursera is free and not too bad as well.

+1 - Very well written. It also has an accompanied course @ Coursera

This user spams their linksynergy redirecting referral links all over.

For those genuinely interested in seeing what's behind the curtain, here's a direct link to the content:

https://www.coursera.org/specializations/mathematics-machine-learning

You might like to check out the specialization offered by the Imperial College on Coursera. It is free to audit the courses and covers all the subjects required for data science. https://www.coursera.org/specializations/mathematics-machine-learning

https://www.coursera.org/specializations/mathematics-machine-learning?utm_source=gg&utm_medium=sem&utm_campaign=02-MathforML-ROW&utm_content=02-MathforML-ROW&campaignid=6495089235&adgroupid=97747337443&device=c&keyword=math%20for%20machine%20learning%20coursera&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=423008820624&hide_mobile_promo&gclid=CjwKCAiAkan9BRAqEiwAP9X6US7Vf2LHaV2QysKyjude0SP8inrnFJ_Oo5MTPk6G3VvLyWvOxFFxGhoC7oEQAvD_BwE

Coursera specialization Mathematics For Machine Learning is a warm recommendation.

Third course is about PCA, which is a bit of a specific domain, but first two courses are great imho.

I found this one.

Mathematics for Machine Learning

This one might be worth looking into as well:

https://www.coursera.org/specializations/mathematics-machine-learning

The MML book suggested by others is a good resource, but I think the coursera specialization might be to a large extent covered in the courses you already passed. But from my experience learning the topics stand alone is not the most effective, or at least doesn't compare to to learning them on-demand. What I mean is that the whole topic of MML, similar to the book, is very vast and going through everything could result in poor retention. On the other hand, once you know the basics, goal-driven and motivated learning can be ridiculously effective and efficient. If you're interested to learn more about ML topics, start from there. When you need better understanding of a Mathematical topic come to resources like MML. For me, that always worked best in learning.

> Not much math in the life of ML engineer. Their main task is to implement ideas of others into actual working, scalable code.

I want to be an ML engineer. I have finished Coursera Course and now planning to do Deep Learning AI course. And also this specialization - Mathematics for Machine Learning, would that be enough to make a career switch? (currently I work as a backend dev)

Imperial has a great Coursera specialization on Mathematics for Machine Learning. Highly recommended!

I am in the same boat. I did the Introduction to Computer with Python from GTx (edX) and I am currently doing the Machine Learning Core from Microsoft (edX). Everybody also suggests the math course from Imperial College (https://www.coursera.org/specializations/mathematics-machine-learning) so I am doing that one too. For programming itself I would stay away from Data Camp. It is better to learn how to use the language for data analysis. I am in the DC area too and would be interested in forming an study group.

https://www.coursera.org/specializations/mathematics-machine-learning

How about this?

Here you go mate https://www.coursera.org/specializations/mathematics-machine-learning

Is that this course of the same name? I took that course (really, specialization) but don’t remember them ever mentioning this book....

https://www.coursera.org/specializations/mathematics-machine-learning

It may be the pure maths behind it

Although I took Linear Algebra and multi-variate calc many years ago, I plan on doing this before applying to OMSCS: https://www.coursera.org/specializations/mathematics-machine-learning and https://www.coursera.org/specializations/statistics

I highly recommend Mathematics for Machine Learning from Imperial College London:

https://www.coursera.org/specializations/mathematics-machine-learning?

It's among the most concise ways to get the linear algebra and calculus you'll need that I've seen. Doing the first two courses should give enough background for ML, AI, and (probably) DL.

A motivated and mathematically inclined student could finish those in 2-3 weeks total. Maybe 4-6 if the material is new to someone.

The other math piece that's needed for these types of courses is probability. I don't think it's unreasonable to consider these things prerequisites without offering a course in them.

If I were to lobby for another OMSCS ML course (other than the aforementioned DL), it would be CS 7545, Machine Learning Theory.

The three areas you want are probability/statistics, multivariate calculus and linear algebra.

I'd recommend the Coursera Mathematics for Machine Learning specialisation as an intro to the calculus and linear algebra you'll need: https://www.coursera.org/specializations/mathematics-machine-learning. I completed it recently and really enjoyed it.

From there, the Gilbert Strang MIT course is highly recommended. MIT also have lots of other free courses with exams, lectures and problem sets.

I don't know anybody who has taken it, but this looks promising from the description: https://www.coursera.org/specializations/mathematics-machine-learning

Look into the Mathematics for Machine Learning Specialization by Imperial College London on Coursera, which covers some of the math you may need. Imperial College London is also going to be offering an online master's program in machine learning through Coursera.

Also see deeplearning.ai, which has a Coursera specialization on deep learning.

Hi there! Great question! First of all, Data Science has become a buzzword and an umbrella term. Data Science is a combination of 3 fields - Computer Science, Math and Statistics, and the Business Domain. If you use use scientific methods, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain insights from data, I'd consider you a Data Scientist.

Topics to learn:

1. Programming (Python is extremely popular in DS)
   Python for Everybody Specialization (course)
   Automate the Boring Stuff with Python (book)
2. Math/Statistics (linear algebra, calculus, probability)
   Mathematics for Machine Learning Specialization (course)
   The Elements of Statistical Learning (book)
3. SQL
   SQL course on Udemy (course)
4. Pandas
   Kaggle - Learn Pandas (course)
5. Exploratory Data Analysis (EDA)
   Data Visualization with Python (course)
6. Machine Learning
   Machine Learning by Stanford on Coursera (course)
   DeepLearning.AI Specialization (course)
   fast.ai (course)
7. GitHub - to host your projects (more of that below)
   Git & GitHub - The Complete Git & GitHub Course (course)

Framework of learning:

1. Learn just enough to get you started
2. Do a project
3. Iterate
4. Be consistent/accountable

Practice makes it permanent! The best way to learn Data Science is by doing Data Science. And projects are a great way to learn faster, more deeply, retain information for a longer period, and showcase your skills. Learning is a never-ending process. I know this list might feel overwhelming because there's too much to learn. Break it down to small steps, learn something new everyday, and apply it to a personal project. It can be anything you're interested in! You will get a sense of accomplishment when you finish a project and will see amazing results in the long run.

Also, you don't have to wait to finish one topic to start the next one. I'd recommend learning different topics at the same time and applying them to your projects. Once you're done learning something, iterate over steps 1 and 2 by learning something new and starting a new project or improving the old one. It's a great way to stay excited and motivated throughout this journey. Once you're comfortable, you can start adding more topics.

Lastly, hold yourself accountable. Set deadlines to your learning process and be consistent. Don't try to learn everything at once or you might get overwhelmed and burnout. Accountability is key to doing anything and will be important to not give up.

Book recommendations/extra material:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Naked Statistics
• The Hundred-Page Machine Learning Book
• Practical Statistics for Data Scientists

This is not a perfect roadmap, there are tons of extra materials to learn. But hopefully I covered the basics to get you started. Data Science is HARD, so I'll finish this with a cliché motivational quote:

“The beginning is perhaps more difficult than anything else, but keep heart, it will turn out all right.” — Vincent van Gogh

Good luck :)

I haven't come across a pre-ML certificate per se, but mostly because almost all of what you need for pre-ML is taught in college at the sophomore-ish/junior-ish level.

If you're looking for formal coursework, at a minimum you should have:

• Basic introductory computer science sequence (OOP through data structures)
• Freshman/sophomore Calculus sequence through vector calculus (partial derivatives, gradients, etc)
• Linear Algebra through eigenstuff
• Calculus-based Statistics through regression

Since you're looking for in-person classes, any of that you haven't taken already is available at the community college level. (In NYC, that even includes the Calculus-based Statistics, which is usually junior-level.)

There's a book, Mathematics for Machine Learning and an accompanying Coursera course, that covers the calculus and linear algebra you'll need without all the cruft that's only relevant for like math majors and structural engineers, but I felt like their handling of vector calculus was a bit rushed and wound up taking Calc III at a community college instead.

The further you can go beyond that, the more you'll understand what you're looking at when it comes time to start reading papers - algorithm analysis, numerical analysis, real analysis, regression analysis...

(PS: Be wary of anyone who says you can just jump right in from 0 without building the foundation first. You can, kind of, from a computer science perspective, but without the math background, you'll be calling functions you don't really understand to build models you don't really understand, and doing the intellectual equivalent of throwing darts to debug your code or improve your models.)

https://www.coursera.org/specializations/mathematics-machine-learning

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Give it a try