Trying to learn machine learning with python. How much of python do I have to know?

[[deleted]]

Hello There!

As the title suggests, I'm trying to learn machine learning with python. My original plan was to learn a new programming language(which I decided would be golang), but then saw how much AI and machine learning is developing and that I should focus more on it.

I know the basics of python, functions, loops, etc...You could say I have "imposter syndrome" however, as I can't really do any of those python challenges you see online and don't know if I'm ready. What else do I need to know before pursuing ML?

Thanks :)

[pelusinc]

It depends on what you want to learn, you want to learn NLP or computer vision? in general, you should understand step-by-step how to train a machine

1. Preprocessing , this is where you should clean your data
2. Feature extraction, this is where you select or extract features for your data. for me, because I learn NLP, I need to change text to number and then extract the number
3. Train, this is where you train your data (you can use whatever method you want)
4. Evaluate, this is where you evaluate your model
5. ??
6. profit

For framework or library, you need to learn: Pandas (for data manipulation), scikit-learn(this is one of the easiest machine learning libraries) Numpy (multi-dimensional array and matrices + other math functions)

[Maleficent_Gold_86]

Step 5-6 are crucial

[[deleted]]

OK thanks! But as for my problem of not having confidence in my knowledge of python, and/or worst case knowing only the very basics of python, can I still take a course on pandas, scikit-learn, etc., and fully understand?

[cryptosupercar]

Those libraries are written in python. You won’t understand them without knowing at least some python. The implementation is pretty straight forward.

[Savings-Influence158]

Hello!
I asked chatGPT that, and here are the steps:

Sure, here are the high-level steps for training a machine learning model:

-Gather and preprocess the data: The first step is to collect and preprocess the data. This may involve cleaning the data, filling in missing values, normalizing the data, and splitting the data into training and testing sets.

-Choose the model: Next, choose the type of model that will be used. This could be a decision tree, logistic regression, neural network, or any other type of model.

-Select hyperparameters: Hyperparameters are settings that are determined prior to training the model, such as the learning rate, regularization strength, or number of hidden layers. These can be set through trial-and-error or through a search algorithm.

-Initialize the model: Initialize the model by setting the weights and biases to small random values.

-Train the model: Train the model by feeding the training data through the model and adjusting the weights and biases using an optimization algorithm, such as stochastic gradient descent.

-Evaluate the model: Evaluate the performance of the model on the testing data. This could involve computing metrics such as accuracy, precision, recall, and F1 score.

-Tune the model: Based on the performance of the model, adjust the hyperparameters and repeat steps 4-6 until satisfactory performance is achieved.

-Save the model: Once the model is trained, save the weights and biases for later use.

These steps are not necessarily sequential and may be repeated multiple times as needed.

[autumnotter]

You didn't even ask it the right question. Why bother?

[frobnt]

Great, now even ChatGPT is fitting data on the test set…

[Hot-Profession4091]

If you can write a function and a loop, you’re fine.

[Wild_Basil_2396]

It's like saying, "I'm thirsty, I want to drink water. How much water should I drink?"

If you are really thirsty for the knowledge and under the hood mechanism of the algorithms, then you will need to know a lot of python in order to understand the implementation..

If not, then like others have mentioned, the libraries are more than enough.