everyone. i am trying to train yolov9 on some custom data on PyCharm instead of google colab as suggested by many tutorials i have watched. how do i do this?

I am currently working with the MERN stack (MongoDB, Express.js, React, Node.js) and am looking to expand my skills. I plan to start with Data Structures and Algorithms (DSA) in Java, but I also want to dive into Machine Learning. However, I am having trouble finding good resources for learning Machine Learning.

Máy in đang xài gặp các vấn đề không hoạt động tiếp được nữa do bị kẹt giấy phải làm sao đây????

I want to train a model to detect spam reviews. Not based on content of the message per se, bust stuff like frequency of messages of the user and other metadata. What do you think are the key data I need to feed the ai model

Here, i trying to build a Manhole object detection using cnn , In this model i got 95% accuracy and after training. im getting false positive like , if i test image with manhole(trained object) for detection it will draw the bounding box and i test a random image without trained object a random bounding box appears that is the problem , same in the live webcam testing but here if the object is not even detecting , getting some random bounding boxes in frame .Here i providing my code pls help

I was training the model in python the below is my code!

I am planning to create a model to predict football match outcomes using various machine learning techniques. The columns of the collected data include Home, Away, Home Team Goals, Away Team Goals, Home Team Possession, Away Team Possession, Home Team Shots on Target, Away Team Shots on Target, etc. While it seems straightforward to use this data to predict match outcomes, these figures are only available after the match has ended. I believe we should use data that is available before the match, such as club value, points, and recent match results, to make predictions. Therefore, I want the columns of the training data to include both match data and pre-match data, and I want to make predictions using pre-match data. I am also curious about what this concept is called in machine learning.

Why is it in logistic regression that the cost function looks like this:
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Let’s say we have 1000 samples of class-1 and 1000 samples of class-2. A model is traied on this data and we found the performance of the model is good. What happens to the performance of model when it is trained on a data with 10000 samples of class-1 and 90000 samples if class-2?