naikshubham

Predictive-Analytics-in-Python

Build ML model with meaningful variables. Use model for predictions

Forecasting-using-Python

Used ARIMA class models to forecast the future.

Object-Oriented-Python

Object Oriented Python codes

NLP-Python

Feature Engineering for NLP in Python

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.

Machine-Learning-Interview-Questions-in-Python

Machine Learning Interview Questions in Python

ML-for-Time-Series-Data

ML on time series data like audio files. Visualizing, cleaning, feature enigineering and modeling time series data.

Deep-Learning-Using-Python

This repository contains keras/TensorFlow/Pytorch code for building Deep Learning models on datasets.

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.

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?"

PySpark-Data-Engineering-Pipelines

Spark is a tool for doing parallel computation with large datasets and it integrates well with Python.

Spoken-Language-Processing-in-Python

Load transform and transcribe audio files.

Building-Webapps-with-R-Shiny

Building interactive web applications with R shiny.

Categorical-data-ML

Deal with Categorical data to solve data problems

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?

Face-Identification

Detect face in a picture and recognize the identity of the face.

Importing-Managing-Financial-Data-in-Python

Data Science Skills for financial data

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.

Network-Analytics-Using-Python

Analyze, visualize, and make sense of networks using the powerful NetworkX library.

Optimization-ML

This repository contains solutions using analytics, machine learning and optimizations.

search-engines

Build search engine using ElasticSearch more different purposes video search, imagesearch, text search

Statistics-for-Data-Science

Statistics for Data Science using spreadsheets, python.

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.

videoanalytics

Data-Visualization-in-Python

Various data vizualizations

Feature-Engineering-ML

Feature Engineering techniques for ML

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.

Miscellaneous-ML-and-Python

The solution to the problems which I encounter while solving AI/ML/Python usecases.

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).

data-engineering

Data Engineering tasks