Bibhuti Bhsan Sahoo's repositories
Building-your-Deep-Neural-Network-Step-by-Step
Starting September 2020, notebook items in course shells will become Ungraded Labs. Paid learners will be able to access their notebooks in the new Coursera lab environment; Auditors will lose access. We strongly encourage you to download your notebooks if you are auditing this course. You can also upgrade or applying for financial aid to access premium Lab items in your course. For more information, please see this forum link Welcome to your third programming exercise of the deep learning specialization. You will implement all the building blocks of a neural network and use these building blocks in the next assignment to build a neural network of any architecture you want. By completing this assignment you will: - Develop an intuition of the over all structure of a neural network. - Write functions (e.g. forward propagation, backward propagation, logistic loss, etc...) that would help you decompose your code and ease the process of building a neural network. - Initialize/update parameters according to your desired structure. This assignment prepares you well for the upcoming assignment. Take your time to complete it and make sure you get the expected outputs when working through the different exercises. In some code blocks, you will find a "#GRADED FUNCTION: functionName" comment. Please do not modify it. After you are done, submit your work and check your results. You need to score 70% to pass. Good luck :) !
Planar-data-classification-with-a-hidden-layer
Starting September 2020, notebook items in course shells will become Ungraded Labs. Paid learners will be able to access their notebooks in the new Coursera lab environment; Auditors will lose access. We strongly encourage you to download your notebooks if you are auditing this course. You can also upgrade or applying for financial aid to access premium Lab items in your course. For more information, please see this forum link Welcome to the second programming exercise of the deep learning specialization. In this notebook you will generate red and blue points to form a flower. You will then fit a neural network to correctly classify the points. You will try different layers and see the results.By completing this assignment you will: - Develop an intuition of back-propagation and see it work on data. - Recognize that the more hidden layers you have the more complex structure you could capture. - Build all the helper functions to implement a full model with one hidden layer. This assignment prepares you well for the upcoming assignment. Take your time to complete it and make sure you get the expected outputs when working through the different exercises. In some code blocks, you will find a "#GRADED FUNCTION: functionName" comment. Please do not modify it. After you are done, submit your work and check your results. You need to score 70% to pass. Good luck :) !
Face-Mask-Detection
Internship Project under The Sparks Foundation
Excel-Fundamentals-for-Data-Analysis
MACQUARIE UNIVERSITY
Prediction-Using-Unsupervised-ML
Bibhuti bhusan sahoo GRIPFEB21 We have to perdict the optimum no of clusters and repersent its visually
Matplotlib-use-of-Histograms
Matplotlib use of Histograms .We use this to provide a informatic views on blood sugar chart
Using-Matplotlib-to-define-the-company-sales-revenue-etc
Using Matplotlib to define the company sales,revenue in barchart format
Cruise-Ship-data-set-using-Pandas-Numpy-Seaborn-Mathplotlib
This is a Cruise ship data set where we use all the tools for our research propose to gain some information about the ship, Passenger .To improve the sales
Air-Quality-Index-Analysis
For each pollutant, an AQI value of 100 generally corresponds to a concentration in ambient air equal to the level of the national short-term ambient air quality standard for the protection of public health. AQI values equal to or less than 100 are generally considered satisfactory. When AQI values are above 100, the air quality is unhealthy: first for certain groups of sensitive people, then for everyone as AQI values increase. The AQI is divided into six categories. Each category corresponds to a different level of health problem. Each category also has a specific colour. Colour allows people to quickly determine if the air quality is reaching unhealthy levels in their communities.Now let’s get started with Data Science project on Air Quality Index analysis with Python. I will recommend you to use Kaggle notebook for this task. The reason why I am recommending you to use a Kaggle notebook you will understand at the end of this article, as we are going to use some APIs provided by Kaggle so I hope you will use a Kaggle notebook for the task of Air Quality Index analysis with Python.
-Telecommunication-company
you will load a customer dataset related to a telecommunication company, clean it, use KNN (K-Nearest Neighbours to predict the category of customers, and evaluate the accuracy of your model.
-fuel-consumption-and-Carbon-dioxide-emission-of-cars-using-simple-linear-regression
we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.
GeneralElectricVI
General Electric Digital Technology Data Analytics Program at The Forage
image
Computer vision for image processing
Food-Nutrition
This is for food nutrition.
Population
This is mainly based on the R programming on population sets.
Fingerprint-Based-Voting-System
New Target Branch
gethub
A utility for cloning and fetching your remote git repositories from GitHub.