How to create Machine Learning Program in Python

To create advanced machine learning code in Python 3, you will need to have a strong understanding of the Python programming language and of machine learning concepts and techniques. You will also need to have access to relevant libraries and frameworks, such as TensorFlow and scikit-learn.

Here are some steps you can follow to create advanced machine learning code in Python 3:

Choose a problem to solve or a task to perform using machine learning. This could be a classification problem, a regression problem, or something else.

Select a machine learning model that is appropriate for the problem or task you have chosen. This could be a decision tree, a support vector machine, or another type of model.

Collect and prepare the data that you will use to train your machine learning model. This may involve cleaning the data, scaling the data, and performing other preprocessing steps.

Split the data into training and testing sets, so that you can evaluate the performance of your model on unseen data.

Train your machine learning model on the training data using the appropriate algorithms and techniques.

Use your trained model to make predictions on the testing data, and evaluate the model's performance using metrics such as accuracy, precision, and recall.

Fine-tune your model by adjusting the parameters and hyperparameters, and repeat steps 5 and 6 until you are satisfied with the model's performance.

Use your trained and fine-tuned model to make predictions on new data, or to perform the task that you set out to do at the beginning.

This is just a general outline of the steps involved in creating advanced machine learning code in Python 3. The specific details will depend on the problem or task you are trying to solve, the machine learning model you have chosen, and the data you are working with.