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|My Self Starter Way To Learn Machine Learning by richardsmith702: 11:50am On Jul 11|
Machine Learning today is one of the most sought-after skills in the market. A lot of Software Engineers are picking up ML, simply because it is a highly paid skill.
So, how do you learn Machine Learning?
First things first - the prerequisites:
Basic calculus. In Machine Learning, you’d be working on a lot of optimizations that require knowledge of Calculus. It would be highly recommended that you are aware of functions, limits, differentiation, maxima, minima, etc.
Linear Algebra. When you talk about ML, you will be dealing with matrices and vectors every day. So, knowledge of Linear Algebra is a must. However, you’d also be required to know about other important topics like Eigenvalues and Eigenvectors.
Probability. Most ML algorithms try to “model” the underlying phenomena that generated the observed data. All of this modelling is probabilistic. It is therefore highly recommended that you are comfortable with the theory of Probability.
Getting into actual ML:
Take a great online course on ML. The most well-known course is the one offered by Andrew Ng (Coursera). It is a great course and it teaches you the basics of Machine Learning - Regression, classification, various ML algorithms, etc. The course also requires you to build a digit recognition system.
Once you have the basics in place, it would be a great idea to practice some problems on Kaggle. Kaggle is a well-known Machine Learning contest platform where you can compete with others in training ML models on various datasets.
Take up ML projects. This is the most important point. Ideally, you’d want to have not only ML experience but also some great projects on your resume that you can showcase.
These projects will help you distinguish yourself from other candidates. After searching a lot for courses that teach ML through projects, I found the [url]great course on Machine Learning[/url]https://www.eduonix.com/learn-machine-learning-by-building-projects?coupon_code=cs10 which uses a “project-based” learning approach. Taking such an approach is quite beneficial
You get to learn.
You get to apply.
You get to showcase projects on your resume.
I believe that taking a course for the sake of it may not be the best. Many students take the course for getting a certificate of completion.
A better approach would be to take courses so that you can attempt projects that you are eventually able to showcase on your resume. This way, you can create a great interview story.
Basically, during the interview, you can talk about the specific projects you implemented and explain the underlying concepts (“In this project, I implemented a Deep Learning model that predicted so and so with an accuracy of 95%”). This is far better than just saying “I completed so and so course where I got a certificate”.
To summarize, aim for online courses that not only help you learn but also help you build your resume.
|Re: My Self Starter Way To Learn Machine Learning by Daejoyoung: 5:58pm On Jul 13|
richardsmith702:Of course learning math and all these stuff is very important for machine learning, but not necessarily the best place to start your machine learning journey.
You can start by solving problems on kaggle using Python's pandas, scikitlearn and tensorflow, and then you work your way down by learning about the algorithms used in solving problems in those projects, and then the statistics, then finally linear algebra and calculus(as much as you need them to solve real problems).
ln my opinion the most important skill for machine learning is understanding the data, data munging and then algorithms.
|Re: My Self Starter Way To Learn Machine Learning by ANTONINEUTRON(m): 6:27pm On Jul 13|
Is machine learning valueable in nigeria?
|Re: My Self Starter Way To Learn Machine Learning by Semtu(m): 9:05pm On Jul 13|
It's even a plus that not so many people are into machine learning hence larger market for you.
Most programmers you see develop websites, softwares, applications and the likes.
To stand out, you need to be able to apply this to Data science, artificial intelligence, machine learning, deep learming
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