Python's versatility and automation capabilities are helpful, in enabling developers to build and maintain sophisticated platforms with enhanced efficiency and innovation. Automation can save time and reduce errors in tasks such as file management, web scraping, Web Automation, API Automation and more. In this Python Automation series, we will explore various techniques and libraries in Python to automate repetitive tasks. Read more
Foss : Python for Automation - English
Outline: About Web scraping About the libraries used for web scraping process Scrape data from spoken tutorial website Python program to implement the web scrapping process Import the n..
Outline: About Spelling and Grammar Checker Python libraries used to implement the spelling and grammar checker Java and tkinter installation commands Import necessary modules and downlo..
Outline: About chatBot Building and training a chatbot Libraries required for building a chatbot Create an auto-updatable Q n A database for a chatbot Compare input questions to databas..
Outline: About log monitor Types of Logs Libraries used to log monitor Analyze log file About Syslog files Python program to Monitor logs continuously Produce log summary Plot the l..
Foss : Python for Machine Learning - English
Outline: Installation of Miniconda in Ubuntu OS Creating a conda environment for Machine Learning Activating conda environment for Machine Learning Download MLpackage.txt file Install a..
Outline: Introduction to Nearest Neighbors and K Nearest Neighbor Introduction to KNN classification Explanation about Iris dataset KNN working example using one of the iris feature Imp..
Outline: Introduction to K Nearest Neighbor Regression Various distance metrics used in KNN Importing the necessary libraries Loading the iris dataset Standard scaling of the dataset ..
Outline: About Linear Regression About Simple Linear Regression About Multiple Linear Regression About Evaluation Metrics Splitting the data into training and testing sets Implementing..
Outline: Introduction to Logistic Regression Introduction to Binary classification Introduction to Multi class classification About Purchase prediction Implementing Binary classificatio..
Outline: Implementing Multiclass classification Model Instantiation of Multiclass Classification and Model training Visualize this correlation using a heatmap Split the data into trainin..
Outline: Introduction to Decision Tree Describing the dataset Importing required Libraries Loading the dataset Encoding Categorical Features Splitting the dataset into Training and..
Outline: Introduction to Artificial Neural Networks Introduction to Multi-Layer Perceptron About ANN Architecture Explanation of Neuron Structure Importing necessary libraries Loading ..
Outline: About Support Vector Machine Introduction to Linear SVM Introduction to Non-Linear SVM Explanation of the California Housing dataset Importing necessary libraries Loading the..
Outline: Introduction to K-means Clustering Working of K-means Clustering Description about Silhouette Score Description about the customers dataset Importing required Libraries Loa..
Outline: Introduction to Ensemble Learning Introduction to Random Forest Importing Libraries Loading the dataset Data Preprocessing Train and Test Split Model Instantiation of Random ..
Foss : QCad - English
Outline: Introduction to QCAD Menu Items and Toolbar Drawing Objects Snapping Tools Using Layers
Outline: Drawing Methods in QCAD Cartesian Coordinate System Using Command line to Draw Objects Drawing Methods
Outline: Using Modification Tools Trim Copy Move Rotate
Outline: Using Modification Tools to Stretch and Mirror in QCAD Stretch Mirror
Outline: Using Modification Tools to Scale and Rotate in QCAD Scale Rotate Two