Programmers_guide_to_Python VS scikit-learn

Hello everyone, I have created a website for my ebook Programmer's guide to Python. On github it wasn't easy to read due to the size, so I thought a website could be more convenient. I've considered topics which are important and which should cover most grounds in Python programming and more. My goal was to create a concise and easy to follow guide to Python programming. I am looking forward to add more content like testing and some standard libraries that we use most often. Let me know your thoughts, suggestions or improvements regarding the website or contents, anything that needs to be added or something else. The current plain learning path will stay forever free and will have no ads, the interactive mode is currently slowly under works, and so I am not much sure about it yet. Hope you find it useful, you can access the website here.

You can also use my book to fine tune your learning. It's free and I keep updating it, so I hope it helps.

Try my book once you're done with the basics.

Hello everyone, I have written a e-book named "Programmer's guide to Python", this is the second time I am making a post about it. It is designed to learn python fast by going through concepts with examples, with easy language and straightforward explanations. Only prerequisite is that you should have some basic exposure to programming. It covers most of the hot/necessary topics and more. It's a free book that you access right here on my github. I have recently worked alot and have updated alot inside out, fixing mistakes/errors, adding topics. I think the book is ready to ~90%, probably more, I'll drop the pdf version once ready. The thing is I don't have any reviewer to review it yet, so if anyone with enough experience who would like to collaborate, fix somethings, review or anything let me know, I'll add you to the contribution/reviewer list or maybe as a co-author if you put up enough work. Finally if you'll be reading it, I would like to know your thoughts/suggestions on improvements and maybe something you'd liked to be added in future. That's it, I hope this book helps you in learning python 🙌.

You can try reading my book, let me know your thoughts.

Hello everyone, I hope you're doing fine, I recently wrote Programmer's guide to Python, its a book to learn python fast. If you have prior programming knowledge and are looking to learn python, this will help you kickstart your learning. If you have previously taken basic python courses and want to solidify your learning, this is for you too. It's short, fast and free. It is designed to cover all the important aspects of python, just good enough get you building stuff with Python. I hope it benefits you in learning python. Let me know your thoughts.

Well you can use my Programmer's guide to Python to solidify your learning. I recently wrote it, it's fast and short way to learn python. I also have ml recommendations which I have curated, they are all almost free and not affiliated. Take a look here, happy learning.

You can find some resources here they are all almost free and not affiliated. I recently wrote Programmer's guide to Python which is short and fast way to learn python, also free. Do take a look.

You can use my Programmer's guide to Python to strengthen your python knowledge. Please take a look.

Programmer's guide to Python

Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.

Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!

I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.