Name: TAMMA RAVINDRA REDDY
Type: User
Company: @ACTIFY DATA LABS
Bio: Data Science | Data Analytics | Machine Learning | Deep Learning | Statistics | OCR | Geo-Spatial Analytics | Image Processing
Location: BANGALORE
Blog: https://ravindrareddytamma.wixsite.com/profile
TAMMA RAVINDRA REDDY's Projects
Resources for AI
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Data Science at the Command Line
A R Package that can be readily installed to get Numerical and Factor Summary of Columns in a DataFrame
Datasets that I generally use for trainings, workshops
The case is financial time-series prediction with cryptocurrencies and it integrates knowledge from various sources - Crypto Currencies, Quantitative Finance, and Machine learning. The data consists of time-series of various cryptocurrencies with open, high, low, close prices and volumes from different crypto exchanges. The goal is to build a successful investing/trading model on the cryptocurrency markets.
Contains ebooks
Tricks and Tweaks regarding Exploratory Data Analysis
Practiced Hive Queries along with Databases
Quick References and Detailed Notes on LLM's
Optimal Implementation of all ML Algorithms can be Found!
Machine Learning Materials
Small,Tricky & Efficient ML Problems are Solved
Contains all the required metrics for a classification model
All of my certificates from different learning's can be found here.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Literature and latest techniques related to product level forecast and inventory management
Basics of Python Subsetting and Filtering as well as little bit DV with Python
Twaeks and Tricks regarding Python
Functional Programming with Python
Config files for my GitHub profile.
scikit-learn: machine learning in Python
A game theoretic approach to explain the output of any machine learning model.
Interpreting a Machine Learning Model
VIP cheatsheets for Stanford's CS 229 Machine Learning
A complete repository that can be used as reference for implementing statistics using R