sandeepbansode

DP-900T00A-Azure-Data-Fundamentals

DP-900 ILT lab instructions

Git-and-Github

Here we are going to learn the basics and avance concepts in git and github.

Natural-language-processing-NLP-

Natural Language Processing (NLP) is a subfield of machine learning that makes it possible for computers to understand, analyze, manipulate and generate human language. You encounter NLP machine learning in your everyday life — from spam detection, to autocorrect, to your digital assistant (“Hey, Siri?”).

Deep-Learning

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.

Hands-On-Natural-Language-Processing-

various concepts of natural language processing by implementing them hands on in python programming language.

Time-Series

Time Series is a certain sequence of data observations that a system collects within specific periods of time — e.g., daily, monthly, or yearly. The specialized models are used to analyze the collected time-series data — describe and interpret them, as well as make certain assumptions based on shifts and odds in the collection.

machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization

awesome-mlops

A curated list of references for MLOps

Machine_Learning_Data_Science

Netflix-Movies-and-TV-Shows-Clustering

Netflix, headquartered in California, is a popular subscription streaming service and production firm. According to Statista, Netflix had approximately 220.67 million paid subscribers worldwide as of the second quarter of 2022. It is crucial that they effectively cluster the shows that are hosted on their platform in order to enhance the user experience for its subscribers. We will be able to understand the shows that are similar to and different from one another by creating clusters, which may be leveraged to offer the consumers personalized show suggestions depending on their preferences. The goal of this project is to classify/group the Netflix shows into certain clusters such that the shows within a cluster are similar to each other and the shows in different clusters are dissimilar to each other.

Basic_Python

og-aws

📙 Amazon Web Services — a practical guide

Multilable-Text-Classification

Multi-Label Text Classification means a classification task with more than two classes; each label is mutually exclusive. The classification makes the assumption that each sample is assigned to one and only one label. On the opposite hand, Multi-label classification assigns to every sample a group of target labels.

Credit-Card-Defult-Prediction

Predicting if a customer will default the next credit card payment using supervised machine learning. Python jupyter notebook attached.

Keras-Regression-housing-Dataset

In a regression problem, the aim is to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where the aim is to select a class from a list of classes

Movie_Recommendation_System_with_Sentiment_Analysis

Content-Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie.

Predicting-sentiment-of-COVID-19-tweets-Capstone-Project-2

This challenge asks you to build a classification model to predict the sentiment of COVID-19 tweets.The tweets have been pulled from Twitter and manual tagging has been done then.

Maths-For-Data-Science

Machine learning is all about maths, which in turn helps in creating an algorithm that can learn from data to make an accurate prediction. The prediction could be as simple as classifying dogs or cats from a given set of pictures or what kind of products to recommend to a customer based on past purchases. Hence, it is very important to properly understand the maths concepts behind any central machine learning algorithm. This way, it helps you pick all the right algorithms for your project in data science and machine learning. Machine learning is primarily built on mathematical prerequisites so as long as you can understand why the maths is used, you will find it more interesting. With this, you will understand why we pick one machine learning algorithm over the other and how it affects the performance of the machine learning model.

Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python

ML_Ops_Machine_Learning_Operations

With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.

sandeepbansode

Support_Vector_Machine_U.S._Airline_tweets_Sentiment_Analysis

Sentiment Analysis: Given a data of US Airline tweets and their sentiment. The task is to do sentiment analysis about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").

FLASK_End_to_end_Zomato_Restaurant_Price_Prediction_and_Deployment

awesome-github-profile-readme

😎 A curated list of awesome GitHub Profile READMEs 📝

Crop_Prediction_System

SQL_For_Data_Science

SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist.

Statistics_For_Data_Science

Statistics For Data Science

Web_Scraping

Scrapped data and saved it into .csv format

Challenging_programming_exercises_for_Python_3

Python-programming-exercises

100+ Python challenging programming exercises