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CNN is mainly used in image analysis tasks like Image recognition, Object detection & Segmentation.CNNs are used for image classification and recognition because of its high accuracy..The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.
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Hypothesis-Testing
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Web-Srcaping
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Python_Basics
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DSA-Cpp
C++
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Data-scienctist_track
It contains all python workJupyter Notebook
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Auto-Correction
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Hand_Pose_Detection
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Fake_news_detection
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Mahnoor-Rana
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History-of-Scala-Language
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Regularization
“Dropout” in machine learning refers to the process of randomly ignoring certain nodes in a layer during training.Dropout is used as a regularization technique — it prevents overfitting by ensuring that no units are codependentJupyter Notebook
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Visualization_with_seaborn
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated to the data structures from pandas. Seaborn aims to make visualization the central part of exploring and understanding data. It provides dataset-oriented APIs, so that we can switch between different visual representations for same variables for better understanding of dataset.Jupyter Notebook
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haberman_survival_dataset
Jupyter Notebook
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Python_ToolBox-2-
Understanding and implementing the list, dictionary, set, and generator comprehensions in python.Comprehensions are constructs that allow sequences to be built from other sequences.List comprehension is a way to define and create lists in Python in a concise way.Dictionary comprehensions are used when the input is in the form of a dictionary or a key: value pair.List comprehensions are to lists, as generator expressions are to generators.The syntax and the way of working of generator expressions are precisely like a list comprehension except that they use round brackets instead of square ones.Jupyter Notebook
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K-NN
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Text-Sentiment-Analysis
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Housing_Dataset
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Machine-Learning-with-scikit-learn
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Nobel-Prize-Winners
Jupyter Notebook
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Website_UI
It is a practice project by using HTML5 and CSS3HTML
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Image_Classification
The dataset contains the images of cats and dogs. We train the model to predict whether it is cat or dog and is that model predict in correct way? And how much accurate our model is .?Jupyter Notebook
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Word_Embedding
In natural language processing, word embedding is a term used for the representation of words for text analysis, typically in the form of a real-valued vector that encodes the meaning of the word such that the words that are closer in the vector space are expected to be similar in meaning.Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation.Jupyter Notebook
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Android_App_On_Google_Play
Project The Android App Market on Google Play Project Description Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. This project lets you apply the skills from Manipulating DataFrames with pandas and Python Data Science Toolbox (Part 1). We recommend that you take those courses before starting this project. The data for this project was scraped from the Google Play website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows: apps.csv: contains all the details of the applications on Google Play. There are 13 features that describe a given app. user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed and attributed with three new features: Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity. Project Tasks Google Play Store apps and reviews Data cleaning Exploring app categories Distribution of app ratings Size and price of an app Relation between app category and app price Filter out "junk" apps Popularity of paid apps vs free apps Sentiment analysis of user reviewsJupyter Notebook
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Housing-Datanew
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Cinemma_ticket_dataset
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Cross_validation-and-10-Cross_validation
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dplyr_msleep
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Investigating_netflix_movies
Netflix! What started in 1997 as a DVD rental service has since exploded into the largest entertainment/media company by market capitalization, boasting over 200 million subscribers as of January 2021.Jupyter Notebook
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MtcarsVisualization.r
VisualizationR
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Python_Toolbox
Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: web development (server-side), software development, mathematics, system scripting. The notebook contains better understandings of built-in-functions and user-defined functions.Jupyter Notebook
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Hand_Digits_Recognition
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Digit_recognition_neural_network
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Name-Entity-Recognition
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Churn_prediction
Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones.Jupyter Notebook
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Precision_Recall_and_F1_score
Precision quantifies the number of positive class predictions that actually belong to the positive class.Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.F-Measure provides a single score that balances both the concerns of precision and recall in one number.Jupyter Notebook
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Intermediate_Seaborn_Visualization
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.Seaborn is a library that uses Matplotlib underneath to plot graphs. It will be used to visualize random distributions.It uses simple code for visualizing regression datasets and so on.Jupyter Notebook
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Dates-and-Times-python-
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Sparks-Foundation-GRIP-Internship
Jupyter Notebook
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DataBasemtcar.r
R
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tidyr_select
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Bit_Coin
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Cleaning-Data-in-Python
Jupyter Notebook
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Snake_game
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Fashion_mnist
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.Jupyter Notebook
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Employee-System
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Gradient_Descent
Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updatesJupyter Notebook
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Batch_Gradient_descent_vs_Mini_Gradient_Descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.In Batch Gradient Descent, all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters. So that’s just one step of gradient descent in one epoch.In Batch Gradient Descent we were considering all the examples for every step of Gradient Descent. But what if our dataset is very huge. Deep learning models crave for data. The more the data the more chances of a model to be good.SGD can be used for larger datasets. It converges faster when the dataset is large as it causes updates to the parameters more frequently.the average cost over the epochs in mini-batch gradient descent fluctuates because we are averaging a small number of examples at a time.Jupyter Notebook
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taskmate
Python
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World_Population
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heart_disease_project
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Importing-Data
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Funding_Countries_EDA
The CEO of Spark Funds wants to understand the global trends in investments so that she can take the investment decisions effectively. Business and Data Understanding Spark Funds has two minor constraints for investments: It wants to invest between 5 to 15 million USD per round of investment It wants to invest only in English-speaking countries because of the ease of communication with the companies it would invest in .Jupyter Notebook
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Stroke_prediction_EDA
According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status.Jupyter Notebook
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Cost_Of_Living_Index_2022
Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index does not include accommodation expenses such as rent or mortgage.Jupyter Notebook
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Functions
A function is a block of code which only runs when it is called. You can pass data, known as parameters, into a function. A function can return data as a result.Jupyter Notebook
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Exploratory-Data-Analysis
Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.Jupyter Notebook
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Web_Scraping
Web scraping is data scraping used for extracting data from websites. The web scraping software may directly.Web scraping is legal if you scrape data publicly available on the internet. But some kinds of data are protected by international regulations, so be careful scraping personal data, intellectual property, or confidential data. access the World Wide Web using the Hypertext.Jupyter Notebook
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IRIS_classification
The Iris flower data set is a multivariate data set.he use of multiple measurements in taxonomic problems.The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machinesJupyter Notebook
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Height_Of_Male_and_Female
In this Project we have to identify the tallest or shortest height of male and female per country and then visualize.The dataset contains six columns: • Rank • Country Name • Male height in Cm • Female height in Cm • Male height in Ft • Female height in FtJupyter Notebook
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