Applied Machine Learning in Python

Andrew Ng Machine Learning Alternatives

Hey! So first off let me just say I'm a statistics major that's going into their final year. I've taken all calc's, linear algebra's, intro to stats, probability, regression, etc.

So I've been wanting to get into ML and I know Andrew Ng's ML course is considered the holy grail so I started it. It's been around a week or two and content wise I'm on week 6. I watch the lectures at 2x speed and finish the assignment in Python for each week in like a day or so. Some of the things I learn are new (like the concepts themselves, ie. Gradient descent, Cost functions, NNs, etc) but since I'm so used to working with matrices and math in general, it's not too bad for me.

However at this point I'm getting a little bored. It honestly feels like a stats class for me and I'm at the part where we discuss bias-variance and even though I feel like I've learned a good amount of machine learning, it still feels like I don't know how to apply it to anything at all. Like if I were to do a Kaggle competition right now I would get nowhere.

So I want to switch gears for a bit and do a more applied course. The ML course I know is great and I have around 4 or 6 more weeks to go and I'll get to it eventually, but I really just want to dive into the more applied side of things for now. Like apply the knowledge of what I've learned of NNs into different datasets, etc. Like one goal I have is to implement the NEAT algorithm to a simple game.

Anyway, what do you guys recommend? I've looked around and these are what I've found: Fast.ai, deeplearning.ai, Machine learning A-Z (udemy, I have this for free), Sentdex YouTube series on ML, this course on Coursera by the University of Michigan, or googles crash course.

I just enrolled in online course machine learning in python https://www.coursera.org/learn/python-machine-learning/home/welcome?utm_medium=email&utm_source=other&utm_campaign=subscription.consolidated.s12n_free_trial_start_HcaqEQLCEeiVhxJT54Upcg

below is the link. Let me know if you want to finish it with me

Can people please stop recommending the Andrew Ng ML course? For one thing, it's in Octave and not Python, and after going through the course, which takes like 2-3 months, you will have a basic understanding of theory but you will not actually be able to grab a data set and make a machine learning model on it.

There are dozens of courses that go over the same concepts but in Python. The only thing special about it is it was one of the first MOOC's, so everyone always points to it. With a course like Applied Machine Learning in Python, you get a quick 4 week course, you'll go over the basics of ML and build models in scikit-learn, which is one of the more popular Python ML frameworks. After that, you'll be able to grab datasets from anywhere and have a foundation on how to create a prediction model from them, all using python.

I started learning Data Science from an YouTube er named Daniel Cheng. In those courses you will learn all the basic functionalities of the Pandas library.

Alongside that I started doing real life projects by watching several tutorials. These projects teaches you a lot and you can easily search your queries about those projects anywhere.

Since, Data Science is pretty big field and I don't know your exact problem but according to my experience a lot of people face problems in Machine Learning. So, in order to learn that you should check you this course Applied Machine Learning.

I hope it helps :}

https://www.coursera.org/learn/python-machine-learning

Audit this course (it's free).

https://www.coursera.org/learn/python-machine-learning

I think you probably want to look into how ROC curves and precision-recall curves are generated. These curves let you see how well your classification will work along a range of "cutoffs" (these would represent the prediction probability/confidence) so you don't have to manually adjust. ROC is more resistant to data-imbalance than precision-recall curves.

Take a look at the video lectures

https://www.coursera.org/learn/python-machine-learning/lecture/0YPe1/classifier-decision-functions

https://www.coursera.org/learn/python-machine-learning/lecture/8v6DL/precision-recall-and-roc-curves

Hello

I am in my 3rd year in undergraduate physics degree and I would like to learn about Machine Learning in Python for a potential masters project. It would be good to learn some of the maths behind it too. I have found this course by MIT launching soon, and am wondering if it looks good?

https://www.edx.org/course/machine-learning-with-python-from-linear-models-to-deep-learning

I have also found this course by Uni of Michigan:

https://www.coursera.org/learn/python-machine-learning

Or does anyone have any other suggestions?

Thanks

For me, I started with the below courses. But I don't recommend following the exact path as I started my ML learning 2 years ago. Although I didn't take any other courses until the recommended AWS path, I have been reading articles and gaining more knowledge.

1. [Andrew NG Coursera course](https://www.coursera.org/learn/machine-learning)
2. [Applied Machine Learning in Python](https://www.coursera.org/learn/python-machine-learning/home/welcome)

But I will update the post with the exact path I have taken.

Probably the best intro course out there

This is also a decent course from UMich

Dataquest is also good

I read this, this, this, this, and this and would recommened them all.

Am still learning Python so I can't give you an exact or experienced answer to that question.

​

However, I recall that one of the Youtubers whose videos I've been watching (to pick up the language) mentioned he got into Python for those exact reasons (though I think he started with some previous programming experience in other langages). Aside from his basics and intermediate python tutorials he also has tutorials on the subject of Machine Learning in Python and a series on a Python AI for StarCraft II.

​

Haven't watched those series so no idea how good they are. But I've found his basics and intermediate have been useful for some insight and to clarify some things for me. So hopefully that holds up in the others.

​

Channel can be found here:

​

https://www.youtube.com/user/sentdex/videos

​

If you already know some Python Coursera has a couple of courses on machine learning with Python that might be worth checking out. Can always audit the courses to view materials and see if fit for purpose.

​

https://www.coursera.org/learn/python-machine-learning

​

https://www.coursera.org/learn/machine-learning-with-python

​