Pinned Repositories
Bank-Cheque-OCR
Used computer vision with OCR to extract feilds from bank cheques, thereby automating the process of cheque processing in Banks.
Deep-Learning-Using-Python
This repository contains keras/TensorFlow/Pytorch code for building Deep Learning models on datasets.
Forecasting-using-Python
Used ARIMA class models to forecast the future.
Grand-Travel-Booking-Portal-using-Flask
Travel booking Webapp using Flask and python.
Image-Clustering-Using-Convnets-Transfer-Learning-and-K-Means-
Image Clustering using Convnets Transfer Learning and KMeans. Builds clusters of similar Images.
ML-for-Time-Series-Data
ML on time series data like audio files. Visualizing, cleaning, feature enigineering and modeling time series data.
NLP-Python
Feature Engineering for NLP in Python
Object-Oriented-Python
Object Oriented Python codes
Parallel-Programming-in-Python
Python is now well established as a major platform for data analysis and data science. For many data scientists, the largest limitation of Python is that all data must fit into the resident memory of the available workstation. Further, traditionally, Python has only been able to utilize one CPU. Data scientists constantly ask, "How can I read and process large amounts of data?" and "How can I make use of more computational processing resources?"
Predictive-Analytics-in-Python
Build ML model with meaningful variables. Use model for predictions
naikshubham's Repositories
naikshubham/Predictive-Analytics-in-Python
Build ML model with meaningful variables. Use model for predictions
naikshubham/Forecasting-using-Python
Used ARIMA class models to forecast the future.
naikshubham/Object-Oriented-Python
Object Oriented Python codes
naikshubham/NLP-Python
Feature Engineering for NLP in Python
naikshubham/Biomedical-Image-Analysis
Fundamentals of image analysis using NumPy, SciPy, and Matplotlib. We'll navigate through a whole-body CT scan, segment a cardiac MRI time series, and determine whether Alzheimer’s disease changes brain structure.
naikshubham/Machine-Learning-Interview-Questions-in-Python
Machine Learning Interview Questions in Python
naikshubham/ML-for-Time-Series-Data
ML on time series data like audio files. Visualizing, cleaning, feature enigineering and modeling time series data.
naikshubham/Deep-Learning-Using-Python
This repository contains keras/TensorFlow/Pytorch code for building Deep Learning models on datasets.
naikshubham/Designing-Machine-Learning-Workflows
Deploying machine learning models in production seems easy with modern tools, but often ends in disappointment as the model performs worse in production than in development. How to exhaustively tune every aspect of our model in development; how to make the best possible use of available domain expertise; how to monitor our model in performance and deal with any performance deterioration; and finally how to deal with poorly or scarcely labelled data. Digging deep into the cutting edge of sklearn, and dealing with real-life datasets from hot areas like personalized healthcare and cybersecurity.
naikshubham/Parallel-Programming-in-Python
Python is now well established as a major platform for data analysis and data science. For many data scientists, the largest limitation of Python is that all data must fit into the resident memory of the available workstation. Further, traditionally, Python has only been able to utilize one CPU. Data scientists constantly ask, "How can I read and process large amounts of data?" and "How can I make use of more computational processing resources?"
naikshubham/PySpark-Data-Engineering-Pipelines
Spark is a tool for doing parallel computation with large datasets and it integrates well with Python.
naikshubham/Spoken-Language-Processing-in-Python
Load transform and transcribe audio files.
naikshubham/Building-Webapps-with-R-Shiny
Building interactive web applications with R shiny.
naikshubham/Categorical-data-ML
Deal with Categorical data to solve data problems
naikshubham/Credit-Risk-Modeling
Prepare credit application data. After that, apply machine learning and business rules to reduce risk and ensure profitability.Ever applied for a credit card or loan, we know that financial firms process our information before making a decision. This is because giving us a loan can have a serious financial impact on their business. But how do they make a decision?
naikshubham/Face-Identification
Detect face in a picture and recognize the identity of the face.
naikshubham/Importing-Managing-Financial-Data-in-Python
Data Science Skills for financial data
naikshubham/Market-Basket-Analysis
What do Amazon product recommendations and Netflix movie suggestions have in common? They both rely on Market Basket Analysis, which is a powerful tool for translating vast amounts of customer transaction and viewing data into simple rules for product promotion and recommendation.Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, aggregation and pruning, and visualization.
naikshubham/Network-Analytics-Using-Python
Analyze, visualize, and make sense of networks using the powerful NetworkX library.
naikshubham/Optimization-ML
This repository contains solutions using analytics, machine learning and optimizations.
naikshubham/search-engines
Build search engine using ElasticSearch more different purposes video search, imagesearch, text search
naikshubham/Statistics-for-Data-Science
Statistics for Data Science using spreadsheets, python.
naikshubham/Unit-Testing-in-Python-for-Data-Science
Every data science project needs unit testing. It comes with huge benefits - saving a lot of development and maintenance time, improving documentation, increasing end-user trust and reducing downtime of productive systems. As a result, unit testing has become a must-have skill in the industry, used by almost every company.
naikshubham/videoanalytics
naikshubham/Data-Visualization-in-Python
Various data vizualizations
naikshubham/Feature-Engineering-ML
Feature Engineering techniques for ML
naikshubham/Generalized-Linear-Models-GLM
Extend regression toolbox with the logistic and Poisson models, by learning how to fit, understand, assess model performance and finally use the model to make predictions on new data.
naikshubham/Miscellaneous-ML-and-Python
The solution to the problems which I encounter while solving AI/ML/Python usecases.
naikshubham/Recommendation-Engines
collaborative filtering and content-based filtering, measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE).
naikshubham/data-engineering
Data Engineering tasks