rajeshsaxena18's repositories
CKAD-exercises
A set of exercises to prepare for Certified Kubernetes Application Developer exam by Cloud Native Computing Foundation
it-cert-automation-practice
Google IT Automation with Python Professional Certificate - Practice files
awesome-sre
A curated list of Site Reliability and Production Engineering resources.
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Machine-Learning-with-Python-IBM
Machine learning with Python IBM Coursera 2020
sre-university
A complete study plan to become a Site Reliability Engineer.
coursera-learning
This repository is aimed to record my learning progress in Coursera.
datasets
A collection of datasets of ML problem solving
professional-certificate-programs
This repository contains all the projects and labs I worked on while pursuing professional certificate programs, specializations, and bootcamp. [Areas: Deep Learning, Machine Learning, Applied Data Science].
Automating-Real-World-Tasks-with-Python
'Automating Real-World Tasks with Python' by Google . A online course via coursera
Data-Analysis-with-Python-IBM
Data Analysis with Python IBM
customer_churn_analysis
In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.
Introduction-to-Deep-Learning-and-Neural-Networks
Coursera - Introduction to Deep Learning and Neural Networks with Keras (Offered By IBM)
SiteReliabilityEngineering
Notes on Site Reliability Engineering. Leave a 🌟 if you found this useful!
Coursera-IBM-Machine-Learning-with-Python-Final-Project
The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final grade of the course.