meliy-meyada / colaboratory-notebooks-ml

Collection of google colaboratory notebooks artificial intelligence.

Awesome Colaboratory Notebooks

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Collection of google colaboratory notebooks from Ultimate Data Science Bootcamp

List	Link	Description
1.	Dog breed Prediction	Convolutional Neural Network capable of identifying the breed of a dog in a supplied image.
2.	Traffic Sign Classification	Convolutional Neural Network to build train and test a traffic sign classification model. Using tensorflow and keras. It is a multiclass classification problem.
3.	Optical charactors Recognition UsingImage	text from images. After extracting the text we will apply some basic functions of OpenCV on that text to enhance it and to get more accurate results.
4.	Plant Deseases Prediction	Convolutional Neural Network which will be able to predict whether a plant is suffering from a disease.
5.	Vehicle Detect Counts	Detecting and counting vehicles in a given image or a video.
6.	Face Swap	Extract faces of human-beings from a given image. We will use a pretrained model to extract landmarks from the faces.
7.	Bird Species Prediction	Convolutional Neural Network which will be able to predict species of the bird. Use different layers and other hyperparameters for building, training and testing this multiclass classifictaion model.
8.	Intel Image Classification	Convolutional neural network and train it on this images. This is a multi class classification problem and use Keras.
9.	Language Translation using IBM Watson	Python will be used to capture the text, after which it will be sent to the IBM cloud to be translated into the chosen language. Watson AI will then be used to turn the translated text into a speech, and the file will then be output.
10.	Laptop Price Predictor	Price variations and laptop types Prediction.
11.	Course Recommendation System	Course Recommendation System using Udemy Dataset. Cosine and Linear Similarity.
12.	EDA on udemyDataset	Exploratory Data Analysis on UdemyDataset.
13.	IPL Win Probability Predictor	Indian Premier League (IPL) Win Probability Predictor.
14.	Book Genre Prediction	Book generate Predictions.
15.	Sentiment LG	Sentiment analysis of Logistic Regression.
16.	Attrition Rate	Employee Attrition Rate Prediction.
17.	Pokemon	Pokemon for Data Mining and Machine Learning.
18.	Cat vs Dogs	The project classification cat an dog using the ImageDataGenerator class from keras.preprocessing.image to load and preprocess images for training the model.
19.	Shopping Intentions	"Shopping Intentions" is a machine learning project that aims to predict whether an online shopper will complete a purchase on an e-commerce website.
20.	Gender Prediction	Predict the gender of a person based on their voice characteristics. The code plots histograms of the independent variables for male and female samples to check for any visible differences between the two classes.
21.	Location based Recommendation	Location-based recommendation using the K-Means algorithm with the Yelp dataset from Kaggle. The code then generates a countplot and a barplot of the number and names of the top 20 restaurants with the highest review count and stars.
22.	Happiness	The happiness of countries. The data set is stored in a file called "Happiness.csv" and is read into a pandas DataFrame called "happy_data". The code then checks for missing values in the data and handles. The happiness of countries. The data set is stored in a file called "Happiness.csv" and is read into a pandas DataFrame called "happy_data". The code then checks for missing values in the data and handles.
23.	Forest Fires	Forest fires. It starts by mounting Google Drive and reading in the 'fire_archive.csv' file as a Pandas DataFrame called 'forest'. It then checks for missing values, Gets descriptive statistics of the data, and plots a heatmap of the correlations between variables.creates dummy variables for the 'type' column and renames some of the columns for clarity.
24.	Car selling Price	Defines a few functions to extract certain information from the 'torque', 'mileage', 'engine', and 'max_power' columns, such as the maximum rpm or maximum engine size in cc. These extracted values are then added to the 'cars' DataFrame as new columns. The original columns are then dropped. The training and test sets are then standardized using the StandardScaler function from scikit-learn.
25.	Affairs	About people's affairs. The dataset has various columns, such as age, years married, number of children, education level, and occupation.generates a heatmap to visualize the correlation between different columns in the dataset.
26.	Mushrooms	Project to classify mushrooms as either edible or poisonous based on various characteristics of the mushrooms such as their cap shape, color, and odor.
27.	Google Apps	Preparing a dataset of Google Play Store app data for further analysis or modeling. It reads in the data from a CSV file, performs some data cleaning and preprocessing steps such as handling missing values and converting data types, and outputs a cleaned and preprocessed version of the dataset.

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