Which of these Machine Learning courses should I attend?

In summary, the speaker is currently pursuing their undergraduate studies in Physics and has some programming knowledge but no experience in Machine Learning. They are looking for advice on which online courses they should attend in order to enrich their knowledge and possibly gain experience. They are considering three courses, including an Introduction to ML course that covers fundamental concepts and popular algorithms, a Machine Learning course for engineering and science applications, and a Practical Machine Learning course with a focus on TensorFlow. They are interested in attending both the first and second course, but are unsure about the third one and are seeking advice on whether it would be useful for their future studies in physics. They have also been recommended to read the 100 page ML book by Burkov and Hands-On ML by Geron
  • #1
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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  • #2
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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  • #3
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?
 
  • #4
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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  • #6
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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1. What is the difference between supervised and unsupervised machine learning?

Supervised machine learning involves using a labeled dataset to train a model, while unsupervised machine learning involves using an unlabeled dataset to find patterns and relationships. In supervised learning, the goal is to predict an outcome based on known data, while in unsupervised learning, the goal is to uncover hidden patterns or groupings within the data.

2. What are the key skills that I will learn in a machine learning course?

A machine learning course will typically cover topics such as data preprocessing, model selection and evaluation, feature engineering, and various machine learning algorithms such as regression, classification, and clustering. You will also learn how to use popular machine learning tools and libraries like Scikit-learn and TensorFlow.

3. How do I know if a machine learning course is right for me?

It is important to assess your current knowledge and skills in programming, statistics, and mathematics before enrolling in a machine learning course. If you have a strong foundation in these areas and a keen interest in data analysis and problem-solving, then a machine learning course may be a good fit for you.

4. Are there any prerequisites for attending a machine learning course?

Most machine learning courses require a basic understanding of programming languages such as Python or R, as well as knowledge of linear algebra, calculus, and statistics. Some courses may also have specific requirements for prior knowledge in machine learning concepts.

5. How can I choose the best machine learning course for my needs?

When choosing a machine learning course, it is important to consider your learning goals, preferred learning style, and budget. Research the course curriculum, instructor qualifications, and student reviews to find a course that aligns with your needs and interests. You may also want to consider taking a free introductory course or attending a workshop before committing to a longer, more intensive course.

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