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advertising

Using Linear Regression for a sales prediction

binarycl_keras

book_knn

The NearestNeighbors algorithm

brain_segmentation

Class Brain segmentation AI with a neural networks

breast_cancer

Using LogisticRegression from sklearn.linear_model for breast cancer classification model

breast_cancer_detection

Using Neural Networks for a breast cancer detection

chihuahua_muffin

Using convolutional neural network (CNN) for classification model chihuahua and muffin

churn

using Random Forest Classifier for a churn dataset

clean_kidney_dataset

Example how to clean a kidney dataset

cookiecutter-personal

decision_tree_random_forest

using decision tree and random forest in a car project

deployment-ml

dogs_and_cats

Using convolutional neural network (CNN) for classification model dogs and cats

emotion

Multiclass tensorflow

emotion-ai

Model The model is a ResNet 18, which is a convolutional neural network (CNN) that has been shown to be effective for image classification tasks. The model is trained on a dataset of facial images with corresponding emotion labels.

final_project

how_to_clean_dataset

cleaning dataset from public web site

intro-deployment-ml

lr_nn

Linear Regression for Predicting Price of second hand Cars Neural Networks

marketing_clustering

Project Master Class Marketing Clustering AI with a neural networks

mi-primer-web

mongodb-intro

multiclass-keras

Multiclass classification is a machine learning problem where the goal is to predict one of multiple classes from a set of data. For example, a multiclass classification model could be used to predict the type of flower in an image, or the sentiment of a text review.

platzi

powerbi

python

python practice

rag_zephyr

Free local chatbot for consulting pdf Gradio

regression_keras

Linear regression with Keras

scikit-learn

search_embeddings

NLP search embeddings are a way of representing text data in a way that makes it easier for computers to understand and compare. They are created by training a neural network on a large corpus of text data. The neural network learns to represent each word or phrase as a vector of numbers, where similar words or phrases have similar vectors.