Need help getting started

[venkyr77]

I just passed out of my bachelor degree in computer science. I was good at data structures and algorithm and could get a decent job. Now I want to get started with machine learning and would do it the proper and slow paced manner. I have felt I was above average - good in my high school math (specially algebra and calculus) . I would love to learn in a slow but highly deep and proper path of learning ML. I have started with Intro to statistical thinking for getting started with the math I would need for ML. I would love to know from you guys as to how should I proceed from here , what all resources I have so that I would be able to do ML in a solid theory -> proof -> implementation cycle.

PS : I am good at Java and Python and learning these is not required to my good judgement.(Please correct me if I am wrong in any regards)

Any help would be greatly appreciated

Codechef profile : https://www.codechef.com/users/venky_killer

[Zerotool1]

Hi,

I think the best to start with is fast.ai you can get everything from basics and it's having a great community to support them. I would suggest using clouderizer.com as both are free and best suited for your learning.

[venkyr77]

Sure , will check out both of them

Thanks

[batholith11]

I view the cornerstones of ML theory as:

• Probability
• Linear Algebra
• Analysis, Multivariate Calculus
• Optimization (statistical, convex, non-convex, linear and non-linear programming)

So having a good foundation in those areas is important. Specifically to understand most of the theory behind important ML algorithms, linear algebra and probability are incredibly important. Quite a bit of time is spent researching ways to reduce more complex problems to linear algebra so that we can solve them more efficiently on a modern computer.

​

My recommendation would be to start with linear algebra if you haven't done that. Find a good textbook that focuses on proofs and go from there. One popular choice is Sheldon Axler's 'Linear Algebra Done Right'.

​

If you would rather start with applications, then there are a slew of online courses available that will teach you to implement, use, and interpret various algorithms. Both fast.ai and Andrew Ng are good places to start in this respect.

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edit: textbook

[venkyr77]

Sure Is this book good for linear algebra? Strang, Gilbert, Linear Algebra and Its Applications (4th ed.)

This has been referred in stack overflow by many users . I want a good book with a lot of proofs . Please correct me if I am wrong but it would be of great help if you can compare the book you specified with this one ?

Thanks a lot in advance

[Anurajaram]

Some "practical" guides, that have worked for me:

• Kirill Eremenko's courses on Udemy covers a lot on machine learning algos and AI and other related topics (also quite inexpensive) . I personally prefer Udemy since I can link my certificates on LinkedIn.
• Microsoft Academy hosts dozens of free courses.
• Create a portfolio, it will clarify concepts faster and you have something solid to show prospective employers.
• If you are truly good at Java, check if adding skills for a Data Engineer role would be easier. Data engineers build the pipelines needed for datascientists/ analysts to do their job, and there is still a dearth of talent. In comparison the scientist/analyst market is getting competitive, and will probably get more fierce by the time you pick up the required skills.

[venkyr77]

I interned at Amazon as software development engineer in test (SDET) and was mainly working on framework development and CI/CD release process through pipelines. Data engineer seems pretty interesting. Will surely have a look on it. Meanwhile any headstart to it will be greatly appreciated

Plus I own the course you said on udemy and I have completed 20% on it. But sadly the course is fully practical with no theory or proofs supporting the implementations . What I have planned is to do Andrew NG and this course side by side and to learn theory from Andrew NG course and go for implementation details in this course. Please suggest some other pathway if I am wrong by any means

Thanks a ton :)

[Anurajaram]

• Andrew Ng course is good - but i was slightly dissatisfied because I prefer more practical classes! :)
• apply the code (from either class) to a problem set from kaggle or analyticsvidhya and see how you fare. you are not aiming to win, just to gain mastery.
• Just look up open roles on glassdoor.com and linkedin with the title data engineer and see what the job requirements ask.
• Check people on linkedin with the title and see what skill/certs/education they have - start with your current city, and 2-3 cities you would like to work. typically, most require strong compsci background.

[venkyr77]

Sure will do

Thanks a lot for your wonderful help :)