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anomaliy_detection_with_isolation_forest

Isolation forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies, instead of the most common techniques of profiling normal points In statistics, an anomaly (a.k.a. outlier) is an observation or event that deviates so much from other events to arouse suspicion it was generated by a different mean A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found A data point is considered a contextual outlier if its value significantly deviates from the rest of the data points in the same context. Note that this means that same value may not be considered an outlier if it occurred in a different context

anomaly_detection_using_autoencoders

Anomaly detection with autoencoder using Tensorflow

apache_spark

Mastering Apache Spark

apple_stock_price_prediction_and_forecasting_using_lstm_neural_network

Apple Stock Price Prediction And Forecasting Using LSTM Neural Network

arima_and_seasonal_arima

ARIMA_and_Seasonal_ARIMA models

aws_sagemaker_projects

Collection of Data Science projects implemented on Amazon Sagemaker

bank_note_analysis

big-mart-sales-prediction-using-xgboost

Big Mart Sales prediction using XGBoost Regressor

book_recommendation_systems

Book Item based collaborative filtering

brats2020

Multimodal Brain Tumor Segmentation Challenge 2020

breast_cancer_detection

Breast cancer detection using k-nearest neighbours ans support vector machine

certificates

Certification from online courses

chat-bot

chicken-disease-image-classification

This repository hosts the code and resources for an advanced Chicken Disease Image Classification system. Leveraging the power of deep learning with TensorFlow, data version control with DVC, containerization with Docker, and automated deployment on AWS through CI/CD pipelines, our project aims to improve poultry health monitoring.

complete-machine-learning-and-data-science-zero-to-mastery

Course resources for the Complete Machine Learning and Data Science ZTM Course

complete-machine-learning-and-data-science-zero-to-mastery-

customer_segmentation_clustering_eda

divding and grouping all individual customers of a grocery business into clusters based on shared common characteristics

data-engineering-challenge

Assesment challenge

data_science_case_studies

Optimising business processes with Data Science techniques to the following 6 departments: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, (6) Production/Maintenance.

datasets

This repository contains a collection of small machine learning datasets for various purposes. These datasets can be used for learning, experimentation, and research in the field of data science and machine learning.

deep_learning_with_python

Deep learning with pythin

deeplearning.ai-specialization-

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses.

ds-projects

Collection of DS projects

eeg-letters

Build and Run a Docker Container for your Machine Learning Model

foodvisionproject

gh-events

google-data-analytics

git repo for Google data analytics course

hands-on-machine-learning

This repository is for my codes from the book "Hands on machine learning with Scikit-Learn, Keras & TensorFlow" by Aurelien Geron

kgs.mathaba-ml-model-deployment-heroku-demo

How to deploy ML models using Flask

ml_project