500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

automated-eda-libraries

Data cleaning and Exploratory data analysis is the challenging task for everyone. Around 80% of time is taken for the data cleaning and EDA and remaining 20% is for model building and all other process. Because of it's time complexity, reasearchers introduced a more Automated libraries for perform Automated EDA and data cleaning operations with few lines of code.It helps to makes our work easier within the time.

av-jabathob-may2021

awesome-github-profile-readme-templates

This repository contains best profile readme's for your reference.

azureml-examples

Official community-driven Azure Machine Learning examples, tested with GitHub Actions.

bike-sharing-project

cal

cardio-vascular-disease

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. CVD includes coronary artery diseases (CAD) such as angina and myocardial infarction (commonly known as a heart attack). In this repository i've made a project on Cardio Vascular Disease from scratch.

color-detection-using-opencv

Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into color. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the color. Since childhood, we have mapped certain lights with their color names. We will be using the somewhat same strategy to detect color names.

combination-of-all-machine-learning-projects

This repository consists of all my machine learning projects around 12 projects deployed at one place to make simplicity for the end users.

covid-19-

covid-live-tracker

In this repository,The live detaction of covid cases,deaths,Recoverd cases are visualize with help of different visualization techniques. Exploratory Data Analysis,Graphs,plots.

covid19-chatbot

Many of the questions patients have about Covid-19 are questions that other patients have as well, like What are symptoms of Covid-19? or What should I do if I fear I have been exposed? or Where can I get tested? While each patient has a unique medical history, many of these questions lend themselves to standardized answers that should reflect the most up-to-date knowledge and guidelines about the virus and be readily accessible for patients. Across a wide range of industries, chatbots have been an efficiency-enhancing way for business teams to interact with their customers. Chatbots are conversational agents that leverage machine learning and natural language processing to understand intent in order to reply with appropriate answers, and they have advantages relevant to the present crisis.1-3 First, they are accessible any time, allowing patients to obtain answers 24/7 and to avoid wait times on hold before reaching a human. Second, chatbots have a higher handling capacity than any human.4 A single chat bot can simultaneously have conversations with thousands of people no matter what time of day. Regardless of the volume of calls, every question may be answered immediately.

data-visualization

Visualizing statistical relationships Statistical analysis is a process of understanding how variables in a dataset relate to each other and how those relationships depend on other variables. Visualization can be a core component of this process because, when data are visualized properly, the human visual system can see trends and patterns that indicate a relationship. 1. Numerical Data Ploting relplot() scatterplot() lineplot() 2. Categorical Data Ploting catplot() boxplot() stripplot() swarmplot() etc... 3. Visualizing Distribution of the Data distplot() kdeplot() jointplot() rugplot() 4. Linear Regression and Relationship regplot() lmplot()

dbt_test

deep-learning-projects

diabetes-prediction-deployment

drowsiness-detection-system-using-open-cv-

The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.

exploratory-data-analysis-

in statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA),[1] which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.

face-and-eye-detection-using-opencv

The objective is to identify the human faces through the web cam by forming a rectangular shaped object around the face. It helps to identify the person's faces for security control. Soon i will post projects using Open cv

fake-news-using-keras-tensorflow

feature-engineering

flight-price-deployement-in-streamlit

forest-cover-classification

A short description I want to give of how I am going to solve this project before starting. Our goal in this project is to classify which forest type it is from the data given. This study area includes 4 Wilderness Areas located in the Roosevelt National Forest of Northern Colorado. These area represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological process rather than forest management practices. Each observation is 30m x 30m forest cover type determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from the data originally obtained from US Geological Survey (USGS) and USFS data. I have been given a total of 54 attributes/features, (excluding 1 target variable) these attributes contain Binary and Quantative attributes, and I need to predict which Forest Cover-Type is it from the given features. I will first explore the data, visualize it, know what the data wants to tell us. Remove any missing values and features that have null values and scale the data within a specific range. Also perform dimensionality reduction procedure where I will use 4 models to tell us which are useful in order to predict the target variable, and then using features which gives us hgih score in the most models. Those 4 Models are: Extra Trees Classifier (ETC) Random Forest (RF) AdaBoost Classifier (ADBC) Gradient Boosting Classifier (GBC) Split the data 75%-25%, train-test set respectively. Will use 10 K-fold Cross Validation on train set. Feed the training data to the Naive Bayes (Our Benchmark Model) and evaluate the result. Training will be done on the Solution Models that I have chose, those are:- K-Nearest Neighbour (KNN), Random Forest (RF), Stochastic Gradient Descent Classifier (SGDC), Extra Trees Classifier (ETC), Logistic Regression (LG)

happymonk-

hussainweb

hyper-parameter-tuning-techniques

ineuron

interview-prepartion-data-science

kaggle-competition-project