Which of these Machine Learning courses should I attend?

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Discussion Overview

The discussion revolves around selecting appropriate Machine Learning courses for an undergraduate Physics student with some programming experience but no prior knowledge in Machine Learning. The focus is on understanding the content and applicability of three specific courses, as well as their potential benefits for future studies in Physics.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation
  • Homework-related

Main Points Raised

  • One participant expresses interest in Course 2, which is tailored for Science applications, but questions its foundational coverage of Machine Learning concepts compared to Course 1.
  • Another participant suggests that after completing Course 2, the student could benefit from reading specific Machine Learning books to gain practical knowledge.
  • There is a discussion about whether Course 1 is a necessary prerequisite for Course 2, with some suggesting that it could be beneficial to take both in sequence.
  • One participant emphasizes the importance of understanding the strengths and weaknesses of different Machine Learning methods and how to effectively implement them, suggesting that Course 1 could help address these concerns.
  • A later reply proposes that all three courses could be taken without significant overlap, recommending starting with Course 1 followed by Course 2, and considering Course 3 for practical applications alongside a project.

Areas of Agreement / Disagreement

Participants express varying opinions on the necessity of taking Course 1 before Course 2, with some advocating for the foundational knowledge it provides, while others believe that Course 2 could be approached directly. There is no consensus on the order of courses or the necessity of all three.

Contextual Notes

Participants highlight the importance of practical knowledge and the application of Machine Learning in Physics, but there are uncertainties regarding the specific content and overlap of the courses. The discussion reflects differing priorities in learning approaches and the perceived relevance of each course to the participant's goals.

Who May Find This Useful

Students in STEM fields, particularly those interested in integrating Machine Learning with their studies, as well as individuals seeking to enhance their programming and analytical skills in practical applications.

Wrichik Basu
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I am currently pursuing my undergraduate studies in Physics. While I do know some programming (Java, MATLAB, Python), I have no knowledge in Machine Learning. This is an important field, and I want to study it. I have gone through some online courses, and need your help in determining which of these I should attend.

Just to make things clear, I am doing these courses in my own time, and will not get any grades/certificates. These are just to enrich my knowledge (and also gain some experience, if possible).


Course 1: Introduction to ML

Course outline:
This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

Course plan:

1642953147718.png



Course 2: Machine Learning for Engineering and Science Applications

Course Outline:
Functional programming is an elegant, concise and powerful programming paradigm. This style encourages breaking up
programming tasks into logical units that can be easily translated into provably correct code. Haskell brings together the
best features of functional programming and is increasingly being used in the industry, both for building rapid
prototypes and for actual deployment.

Course plan:

1642953483882.png



Course 3: Practical Machine Learning with Tensorflow

Course Outline:
This will be an applied Machine Learning Course jointly offered by Google and IIT Madras. We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.

Course Plan:

1642953959420.png



My thoughts:

Course 2 is definitely geared for students in Science, so I want to attend that. However, it seems that it does not give a proper basic understanding of ML, which is covered by course 1. Course 2 has more of practical applications of ML, so I believe I have to do both, but course 1 followed by 2.

I am not sure about the third one, though. It focuses on TensorFlow. Should I do this later, if I find time? Will this be useful in the future if I pursue higher studies in Physics?

Looking forward to your advice.
 
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Once you take the second course then I imagine you could jump to reading some books like:
- 100 page ML book by Burkov
- Hands-On ML using Scikit-Learn Learn, Keras, and Tensorflow by Geron

Burkov’s book is freely available online as he allowed it to be shared and if liked bought.

Geron's book gives you a soup to nuts with a lot of practical knowledge in collecting, using, and testing your ML models.
 
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jedishrfu said:
Once you take the second course then I imagine you could jump to reading some books
Thanks for the books. I am more inclined towards practical knowledge so that I can use it in physics when necessary. Will definitely follow the books you mentioned.

What is your take on the first course? Does it seem like a necessary prerequisite for the second one? Or can I directly jump to the second one?
 
it looks good and it can’t hurt to take it. Basically, you need answers to these questions:
- how to identify what method works best for a given ML problem?
- what are the pros and cons, strengths and weaknesses of a given method?
- how to prep the data and select the training data and test data?
- how to use the method effectively?
- how to diagnose what is going on when they fail?

Many of these methods are essentially black boxes where you are taught the theory of how they work but not necessarily how they are actually implemented. This means that they may fail in odd ways under rare circumstances and it would be hard to figure out what went wrong.
 
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Wrichik Basu said:
I am currently pursuing my undergraduate studies in Physics. While I do know some programming (Java, MATLAB, Python), I have no knowledge in Machine Learning. This is an important field, and I want to study it. I have gone through some online courses, and need your help in determining which of these I should attend.

Just to make things clear, I am doing these courses in my own time, and will not get any grades/certificates. These are just to enrich my knowledge (and also gain some experience, if possible).


Course 1: Introduction to ML

Course outline:Course plan:

View attachment 295891


Course 2: Machine Learning for Engineering and Science Applications

Course Outline:Course plan:

View attachment 295892


Course 3: Practical Machine Learning with Tensorflow

Course Outline:Course Plan:

View attachment 295894


My thoughts:

Course 2 is definitely geared for students in Science, so I want to attend that. However, it seems that it does not give a proper basic understanding of ML, which is covered by course 1. Course 2 has more of practical applications of ML, so I believe I have to do both, but course 1 followed by 2.

I am not sure about the third one, though. It focuses on TensorFlow. Should I do this later, if I find time? Will this be useful in the future if I pursue higher studies in Physics?

Looking forward to your advice.

You can take all 3. They don't overlap significantly. 1 and 2 both cover some important fundamentals. I would start with 1, then 2. 3 would be good to take in combination with a serious project if you have one in mind.
 
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