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Target_Detection_SAR_Images
Recognition of Targets in SAR Images Based on a WVV Feature Using Various Machine And Deep Learning Models.Inferential_Statistics
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.Predictive_Maintenance_With_MLops
Aircraft components are susceptible to degradation, which affects directly their reliability and performance. This machine learning project will be directed to provide a framework for predicting the aircraftโs remaining useful life (RUL) based on the entire life cycle data in order to provide the necessary maintenance behavior.Handling_Imbalance_Dataset
A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes.Data-Analysis
Analyzing Data Through Python Using Pandas Library. Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.Outliers_Impact_Analysis
Outliers represent natural variations in the population, and they should be left as is in your dataset. These are called true outliers. Other outliers are problematic and should be removed because they represent measurement errors, data entry or processing errors, or poor sampling.Machine_Learning_With_DVC
A practical approach for creating machine learning CI/CD Pipeline with DVC that is data version control.Pandas_Tutorial
Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on top of the NumPy library. Pandas is fast and it has high performance & productivity for users.Flask_RestApi
Building Flask RestApi.Stock_Market_Volatility_Analysis
Time Series Analysis Using MA, ARIMA, SARIMA, ARCH & GARCH models.Fraud_Transaction_Detection_With_Mlops
ML_With_Logging
Understanding logging with machine learning implementation.Exploratory_Data_Analysis
In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.Induction_Furnace_Carbon_Sulphur_Response
This project is aimed to integrate artificial intelligence in steel Industry for better yielding and reducing production loss.Normalization_Standardization
Feature transformation is a mathematical transformation in which we apply a mathematical formula to a particular column (feature) and transform the values, which are useful for our further analysis.Movie_Recommender_System
Generalized recommendations for every user based on movie popularity and genre(Content Based)BHP
Boston House Prediction With Docker DeploymentAlgo_Trading_With_Python
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume.[1] This type of trading attempts to leverage the speed and computational resources of computers relative to human traders.Hyper_Parameter_Tuning
Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. If we don't correctly tune our hyperparameters, our estimated model parameters produce suboptimal results, as they don't minimize the loss function. This means our model makes more errors.Handling_Categorical_Features
Categorical data is a type of data that is used to group information with similar characteristics, while numerical data is a type of data that expresses information in the form of numbers.Feature_Selection_Engineering
Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.South_German_Bank_MLops
NLP_With_Deployment
South_German_Bank_Credit_Risk_MLops
This project is an end to end machine learning project with mlops approach i.e github for code versioning and for versioning data, DVC is used and model tracking too.E_Commerce_Customer_Behaviour
Chicken_Disease_Classification
Django_RestApi
Building Django RestApi.MLFlow_Tutorial
MLFlow TutorialLSTM_Tutorial
LSTM TutorialMachine_Learning_Systematic_Approaches
This repo is defining the systematic approach for solving a machine learning problem along with concept of computer vision with python for image manipulations.Love Open Source and this site? Check out how you can help us