shajidHossainHemal

Car-Game

Made with graphics.h library

Collaborative-Filtering-Recommendation-Engine

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library. Collaborative filtering is based on the fact that relationships exist between products and people's interests. Many recommendation systems use collaborative filtering to find these relationships and to give an accurate recommendation of a product that the user might like or be interested in. Collaborative filtering has basically two approaches: user-based and item-based. User-based collaborative filtering is based on the user similarity or neighborhood. Item-based collaborative filtering is based on similarity among items. Let's first look at the intuition behind the user-based approach. In user-based collaborative filtering, we have an active user for whom the recommendation is aimed. The collaborative filtering engine first looks for users who are similar. That is users who share the active users rating patterns. Collaborative filtering basis this similarity on things like history, preference, and choices that users make when buying, watching, or enjoying something.

Content-based-Recommendation-System

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.

Coursera---Crash-Course-On-Python-By-Google

Course material from Coursera - Crash Course On Python By Google.

Coursera---Data-Visualization-with-Python-by-IBM

Course materials from Coursera - Databases and SQL for Data Science by IBM part of IBM's Data Science Professional Certificate program.

Coursera---Databases-and-SQL-for-Data-Science-by-IBM

Course materials from Coursera - Databases and SQL for Data Science by IBM

Coursera--Machine-Learning-with-Python-by-IBM

About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

Coursera-Data-Analysis-with-Python-by-IBM

Course Materials from Coursera-Data Analysis with Python by IBM part of IBM's Data Science Professional Certificate specialization course.

Coursera-IBM-s-Data-Science-Methodoloy

Course Materials

Coursera_Capstone

shajidHossainHemal/Collaborative-Filtering-Recommendation-Engine

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library. Collaborative filtering is based on the fact that relationships exist between products and people's interests. Many recommendation systems use collaborative filtering to find these relationships and to give an accurate recommendation of a product that the user might like or be interested in. Collaborative filtering has basically two approaches: user-based and item-based. User-based collaborative filtering is based on the user similarity or neighborhood. Item-based collaborative filtering is based on similarity among items. Let's first look at the intuition behind the user-based approach. In user-based collaborative filtering, we have an active user for whom the recommendation is aimed. The collaborative filtering engine first looks for users who are similar. That is users who share the active users rating patterns. Collaborative filtering basis this similarity on things like history, preference, and choices that users make when buying, watching, or enjoying something.

shajidHossainHemal/Coursera--Machine-Learning-with-Python-by-IBM

About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

shajidHossainHemal/Car-Game

Made with graphics.h library

shajidHossainHemal/Content-based-Recommendation-System

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.

shajidHossainHemal/Coursera---Crash-Course-On-Python-By-Google

Course material from Coursera - Crash Course On Python By Google.

shajidHossainHemal/Coursera---Data-Visualization-with-Python-by-IBM

Course materials from Coursera - Databases and SQL for Data Science by IBM part of IBM's Data Science Professional Certificate program.

shajidHossainHemal/Coursera---Databases-and-SQL-for-Data-Science-by-IBM

Course materials from Coursera - Databases and SQL for Data Science by IBM

shajidHossainHemal/Coursera-Data-Analysis-with-Python-by-IBM

Course Materials from Coursera-Data Analysis with Python by IBM part of IBM's Data Science Professional Certificate specialization course.

shajidHossainHemal/Coursera-IBM-s-Data-Science-Methodoloy

Course Materials

shajidHossainHemal/Coursera_Capstone

shajidHossainHemal/Cowboy-Runner

Third game from Udemy - Master Unity By Building 6 Fully Featured Games From Scratch course.

shajidHossainHemal/Ecommerce-Website-Shopper

This project was implemented using HTML, Bootstrap, CSS and PHP

shajidHossainHemal/Flappy-Bird

Second game from Udemy - Master Unity By Building 6 Fully Featured Games From Scratch Course

shajidHossainHemal/Hands-On-Machine-Learning-with-Scikit-Learnand-TensorFlow

Hands-On-Machine-Learning-with-Scikit-Learnand-TensorFlow

shajidHossainHemal/Jack-The-Giant

Udemy - Master Unity By Building 6 Fully Featured Games From Scratch

shajidHossainHemal/Leetcode-Problems

This repository is dedicated to solved leetcode problems.

shajidHossainHemal/Memory-Puzzle

Fifth game from Udemy - Master Unity By Building 6 Fully Featured Games From Scratch Course

shajidHossainHemal/Processing-Cats-and-Dogs-Dataset

Using Tensor Flow.

shajidHossainHemal/Processing-Fashion-MNIST-Dataset

Using Tensor Flow

shajidHossainHemal/Python-for-Data-Science-and-AI-IBM

Courses materials from Coursera's Python for Data Science and AI IBM course.

shajidHossainHemal/Role-Playing-Game-Project-in-Unity

From Udemy's RPG Core Combat Creator: Learn Intermediate Unity C# Coding course by GameDev.tv

shajidHossainHemal/Samsung-Prep

shajidHossainHemal/Scraping-books.toscrape.com-with-Scrapy

This project scrapes book details (including price, stock availability etc) listed in http://books.toscrape.com/. To scrape this site, I used Scrapy which is a Python Framework.

shajidHossainHemal/Scraping-imdb-top-movies-list

shajidHossainHemal/Spider-Cave

Second game from Udemy - Master Unity By Building 6 Fully Featured Games From Scratch Course. Created in Unity 2019.4.1f1

shajidHossainHemal/TargetSMSNG

A repository for my practice to crack the interviews of SRI-N

shajidHossainHemal/tech-companies-in-bangladesh

List of tech companies to help developers to find job.

shajidHossainHemal/Thesis-Codes

This repository contains the python notebooks for my thesis.

shajidHossainHemal/Vuforia-Marker-based-Augmented-Reality

Single image marker based Augmented Reality project in Unity 2019.4.1f1.