EmamulHossen

Basic-Python

Basic_Python

Big_To_Small_Datasset

Car-Brand-Prediction-Using-ResNet50

EmamulHossen

Exploratory-Data-Analysis-EDA-

Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science

FeatureTransformation-Assignment-

Feature transformation is a technique in machine learning that changes the way features are represented in order to improve the performance of machine learning algorithms. This can be done by transforming the features to a different scale, removing outliers, or creating new features from existing

Machine-learning

Neural-Network-Implementation

OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking.

Python-OpenCV

OpenCV is a Python library that allows to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking.

EmamulHossen/Python-OpenCV

OpenCV is a Python library that allows to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking.

EmamulHossen/EmamulHossen

EmamulHossen/Car-Brand-Prediction-Using-ResNet50

EmamulHossen/Exploratory-Data-Analysis-EDA-

Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science

EmamulHossen/FeatureTransformation-Assignment-

Feature transformation is a technique in machine learning that changes the way features are represented in order to improve the performance of machine learning algorithms. This can be done by transforming the features to a different scale, removing outliers, or creating new features from existing

EmamulHossen/Neural-Network-Implementation

OpenCV is a Python library that allows you to perform image processing and computer vision tasks. It provides a wide range of features, including object detection, face recognition, and tracking.

EmamulHossen/Basic_Python

EmamulHossen/Big_To_Small_Datasset

EmamulHossen/CIFAR-10-Image-Classification-Using-CNN

EmamulHossen/CNN-Implementation-Using-ResNet50

EmamulHossen/CoustomerChunPrediction

Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.

EmamulHossen/Decision_tree_Classifier

A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks.

EmamulHossen/Deep-Learning-Project

Deep learning is a subfield of machine learning that focuses on the development and training of artificial neural networks to perform tasks without explicit programming. It is inspired by the structure and function of the human brain, utilizing interconnected layers of nodes (artificial neurons) to process and learn from data.

EmamulHossen/Drinkers_or_Not

EmamulHossen/Fake_News_Detection

Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.

EmamulHossen/Feature-Transformation

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. It is a technique by which we can boost our model performance.

EmamulHossen/Feature-Transformation-Assignment-6.3-

Feature transformation is a technique in machine learning that is used to modify the original features of a dataset in order to improve the performance of machine learning algorithms.

EmamulHossen/Feature_Engineering

Feature engineering or feature extraction or feature discovery is the process of extracting features from raw data.

EmamulHossen/Flower-Image-Classification-Using-VGG16

EmamulHossen/Gradient-Problem-in-RNN

The vanishing gradient problem is a well-known issue in training recurrent neural networks (RNNs). It occurs when gradients (derivatives of the loss with respect to the network's parameters) become too small as they are backpropagated through the network during training.

EmamulHossen/K-Means_Cluster

K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science.

EmamulHossen/K-Nearest-Neighbors-Assignment-7.1

K-Nearest Neighbors (KNN) is a simple and intuitive machine learning algorithm used for classification and regression tasks. It falls under the category of instance-based learning or lazy learning algorithms.

EmamulHossen/KNN_Assignment_7.2

The K-Nearest Neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used for both classification and regression tasks.

EmamulHossen/KNN_for_Classification-7.2

K-nearest neighbors (KNN) classification is a non-parametric supervised machine learning algorithm. It is a simple yet powerful algorithm that can be used for a variety of classification problems

EmamulHossen/LinearRegression

Linear regression is also a type of machine-learning algorithm more specifically a supervised machine-learning algorithm that learns from the labeled datasets and maps the data points to the most optimized linear functions.

EmamulHossen/Logistic_Regression

Logistic regression is a supervised machine learning algorithm mainly used for classification.

EmamulHossen/OpenCV

EmamulHossen/Spam_Email_Detection

Spam mail detection is the process of identifying and filtering out unwanted or unsolicited emails, commonly referred to as "spam," from a user's inbox.

EmamulHossen/Statistics-For-Data-Science

OpenCV, or Open Source Computer Vision Library, is a powerful open-source computer vision and image processing library widely used for tasks like object detection, image manipulation, and machine learning in computer vision applications.

EmamulHossen/Text_Data_Preprocessing_-_Vectorizer