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Competitive Programming Solutions

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1

Coursera---Programming-for-Everybody-Getting-Started-with-Python-

this contains all the answers to the quizes and asssignments for "Programming for Everybody (Getting Started with Python)" on Coursera by the University of Michigan.
Roff
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2

Data-Science-Cheat-Notes-

best notes for each topic quick revision
TeX
26
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3

Coursera---Natural-Language-Processing-Specialization-by-deeplearning.ai

#Assignment Answers #About this Specialization: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Jupyter Notebook
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4

Coursera---Python-Data-Structure-Answers

Jupyter Notebook
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5

Coursera-Big-Data-Modeling-and-Management-Systems-by-University-of-California-San-Diego

This course includes five weeks of content with 2-3 modules in each. In each week, there'll be a set of lecture videos, together with a set of review questions to help you make sure you've mastered the material. Some modules also have hands on exercise in which you'll get to implement these concepts and see for yourself how they really work. When you complete the course, you'll come away with: an in-depth knowledge of why big data modeling and management is essential in preparing to gain insights from your data. knowledge of real world big data modeling and management use cases in areas such as energy and gaming. understand different kinds of data models. the ability to describe streaming data and the different challenges it presents the differences between a DBMS and a BDMS.
Python
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6

Introduction-to-Data-Science-in-python

This repository contains Ipython answers of assignments of the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera
6
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7

Coursera-Introduction-to-Big-Data-by-University-of-California-San-Diego

<h1>hare krishna</h1> Here’s an overview of our goals for you in the course. After completing this course you should be able to: - Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. - Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. - Get value out of Big Data by using a 5-step process to structure your analysis. - Identify what are and what are not big data problems and be able to recast big data problems as data science questions. - Provide an explanation of the architectural components and programming models used for scalable big data analysis. - Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. - Install and run a program using Hadoop! Throughout the course, we offer you various ways to engage and test your proficiency with these goals. Required quizzes seek to give you the opportunity to retrieve core definitions, terms, and key take-away points. We know from research that the first step in gaining proficiency with something involves repeated practice to solidify long-term memory. But, we also offer a number of optional discussion prompts where we encourage you to think about the concepts covered as they might impact your life or business. We encourage you to both contribute to these discussions and to read and respond to the posts of others. This opportunity to consider the application of new concepts to problems in your own life really helps deepen your understanding and ability to utilize the new knowledge you have learned. Finally, we know this is an introductory course, but we offer you one problem solving opportunity to give you practice in applying the Map Reduce process. Map Reduce is a core programming model for Big Data analysis and there’s no better way to make sure you really understand it than by trying it out for yourself! We hope that you will find this course both accessible, but also capable of helping you deepen your thinking about the core concepts of Big Data. Remember, this is just the start to our specialization -- but it’s also a great time to take a step back and think about why the challenges of Big Data now exist and how you might see them impacting your world -- or the world in the future!
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8

Apni-Pathshala-

daily goals for placement and 5 star codechef coder
C++
4
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9

Data-Science-IBM-Professional-Course

This professional certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs and are performed in the IBM Cloud (without any cost to you). Throughout this Professional Certificate you are exposed to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to real jobs that employers value, including: Tools: Jupyter / JupyterLab, Zeppelin notebooks, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods
Jupyter Notebook
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10

Motivation-Inspiration

journey for positive life
2
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11

Coursera_Capstone

Capstone project for the Coursera IBM Data Science Specialization
Jupyter Notebook
2
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12

OOPS-Interview-Preparation

# Hare Krishna
2
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13

Coursera-Big-Data-Integration-and-Processing-by-University-of-California-San-Diego

# Hare Krishna #Assignment and Quiz Answers # Notes #Hands On Lab
Jupyter Notebook
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14

Coursera-Machine-Learning-by-Stanford-University-Andrew-Ng-

Hare Krishna
MATLAB
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15

100-Days-of-Full-Stack-Developer

# Hare Krishna
1
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16

SVM

# Hare Krishna
Jupyter Notebook
1
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17

Red-Hat---Linux

Linux commands
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18

Logistic-Regression

#Hare krishna
Jupyter Notebook
1
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19

Self-Organizing-Maps-SOM-

# Hare Krishna
Jupyter Notebook
1
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20

Github-Guide

# Hare Krishna
1
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21

Grid-and-Randomized-Search-CV

# Hare Krishna
Jupyter Notebook
1
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22

Random-Forest

# Hare Krishna
Jupyter Notebook
1
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23

XGBoost

# Hare krishna
Python
1
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24

OpenCv-Projects

This repo contains code for Computer Vision, Deep learning, and AI Projects
C++
1
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25

PCA

# hare krishna
Jupyter Notebook
1
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26

STL---Standard-Template-Library-in-Cpp-

C++
1
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27

Coursera---Getting-and-Cleaning-Data

Getting and Cleaning Data Course Project
R
1
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28

Unix

# Hare krishna
1
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29

Facebook-HackerCup-2020

CodetillAc ... Hare krishna
C++
1
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30

datasciencecoursera

data science using r programming
1
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31

SDLC

# Hare Krishna
1
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32

Numpy

# Hare Krishna
Jupyter Notebook
1
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33

IBM-Advanced-Data-Science-Capstone

IBM-Advanced-Data-Science-Capstone- Coursera Peer Graded Ritik Dagar
Jupyter Notebook
1
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34

ANN

# Hare Krishna # Creating ANN using weight Initialization # Basic Implementation of ANN
Jupyter Notebook
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35

CNN

# Hare krishna
Jupyter Notebook
1
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36

Coding-School-of-Ghaziabad

1
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37

InterviewBit----21-day-Challenge

#KillCorona #21 Day Challenge # InterviewBit
1
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38

R-Sudio-testing

#testing
1
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39

Simple-Mario-game-in-Python

Python
1
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40

Deep-Learning-Coursera

Specialization
1
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41

K-means-Clustering

#hare krishna
Jupyter Notebook
1
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42

Data-Science-using-R-Coursera-Johns-Hopkins-University-

1
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43

RNN

# Hare Krishna
Jupyter Notebook
1
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44

Kaggle-Competitions

DataSets and Notebooks of Kaggle Competitions
1
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45

SQL

# Hare Krishna
Python
1
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46

Matplotlib

# Hare Krishna
Jupyter Notebook
1
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47

EDA

# Hare Krishna
Jupyter Notebook
1
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48

Machine-Learning-Udemy

# Hare krishna
Jupyter Notebook
1
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49

Career-Counselling-Website

Semester Project
PHP
1
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50

Covid-19-Protector

Data Analysis on Covid 19 dataset, Mask detection, Gloves Detection, Symptoms, Prevention and more
1
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51

KNN

#hare krishna
Jupyter Notebook
1
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52

MongoDB

# Hare krishna
1
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53

Algoexpert---85-Coding-Interview-Questions

Everything you need to own those tech interviews.
C++
1
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54

Sklearn

# Hare Krishna
1
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55

Data-WareHouse

# Hare Krsihna
1
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56

ML-and-AI-Projects

list of machine learning projects with there codes.
Jupyter Notebook
1
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57

Ridge-and-Lasso-Regression

#Hare Krishna
Jupyter Notebook
1
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58

Coursera-Machine-Learning-With-Big-Data-by-University-of-California-San-Diego

#Hare Krishna #RadhaVAllabh
1
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59

100DaysOfDataScience

From zero to hero in Data Science
Jupyter Notebook
1
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60

Seaborn

# Hare Krishna
Jupyter Notebook
1
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61

Boltzmann-Machine

# Hare Krishna
Jupyter Notebook
1
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62

Data-Science-Handbook-Notebooks

Hare krishna Hare krishna krishna krishna hare hare hare rama hare rama rama rama hare hare ...... Radha Vallabh Shri HariVansh Jai jai Shri Vrindavan Shri Vansaj
Jupyter Notebook
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63

Heirarchical-Clustering

#hare krishna
Jupyter Notebook
1
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64

Pandas

# Hare Krishna
Jupyter Notebook
1
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65

Competitive-coding-tips

useful study material and tips are provided in it.
C++
1
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66

Linear-Regression

# Hare Krishhna # radhe radhe # Gradient Descent .. minmize error as much as you can ... find local minima main target
Jupyter Notebook
1
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67

NLP-Basic-Level

# Hare Krishna
Python
1
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68

Auto-Encoder

# Hare krishna
Jupyter Notebook
1
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69

Multiple-Linear-Regression

# hare krishna
Jupyter Notebook
1
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70

Python-For-Data-Science

#hare krishna #radhe radhe #course 1 #basic python for data science
Jupyter Notebook
1
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71

Coursera----R-Programming

Programming Assignment 2: Lexical Scoping
R
1
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72

Analytics-Vidhya-Competitons

Machine Learning Data Science Competitions and Hackathons
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73

Polynomial-Regression

# Hare Krishna
Jupyter Notebook
1
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74

-C-mini-projects

I’ve enlisted all the mini-projects, projects, games, software and applications built using C by me in my college learning days
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75

Adaboost

# Hare Krishna
1
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76

Tableau

# Hare Krishna
1
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77

Coursera---Exploratory-Data-Analysis

Programming assignmets of course Exploratory Data Analysis
R
1
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78

DBSCAN-Clustering

# hare krishna
Jupyter Notebook
1
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79

Gradient-Boosting

#Hare krishna
1
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80

Decision-Tree

#hare krishna
Jupyter Notebook
1
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81

Feature-Engineering

# Hare Krishna
Jupyter Notebook
1
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82

NLP-Twitter-Sentiment-Analysis-Project

Create a pipeline to remove stop-words, punctuation, and perform tokenization Understand the theory and intuition behind Naive Bayes classifiers Train a Naive Bayes Classifier and assess its performance
Jupyter Notebook
1
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83

Top-10-Algorithms-and-Data-Structures-for-Competitive-Programming

Graph algorithms Dynamic programming Searching and Sorting: Number theory and Other Mathematical Geometrical and Network Flow Algorithms Data Structures
C++
1
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84

Applied-Data-Science-with-Python-Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order.
Jupyter Notebook
1
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85

IBM-Applied-AI

Artificial Intelligence (AI) is transforming our world. Whether you’re a student, developer, or a technology consultant, understanding AI and knowing how to create AI powered applications can give you an edge in your career. This Professional Certificate is designed to arm you with the skills to work as an AI Application Developer. It will give you a firm understanding of AI, its applications, and its use cases. You will become familiar with IBM Watson AI services and APIs. Even if you have no programming background, you will be able to create AI driven chatbots as well as pick up practical Python skills to work with AI. The courses will also enable you to apply pre-built AI smarts to your products and solutions. A learner with no prior knowledge of AI will learn to design, build, and deploy AI-powered applications on the web. Rather than create complex AI algorithms and interfaces from scratch, learners will use IBM Watson AI services and APIs to create smart applications with minimal coding. By the end of this Professional Certificate, learners will complete several projects that showcase proficiency in applying AI and building AI powered solutions.Applied Learning Project This Professional Certificate will give you a firm understanding of AI, its applications, and its use cases. You will become familiar with IBM Watson AI services and APIs. If you have no programming background, you will be able to create AI driven chatbots as well as pick up practical Python skills to work with AI. The courses will also enable you to apply pre-built AI smarts to your products and solutions.
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