Pinned Repositories
2015
Public material for CS109
2017
Course material for BST 260 in Fall 2017
ai-resources
Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning
aima-data
Data files to accompany the algorithms from Norvig And Russell's "Artificial Intelligence - A Modern Approach"
aima-java
Java implementation of algorithms from Norvig And Russell's "Artificial Intelligence - A Modern Approach"
aima-python
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Analytics_with_pyspark
Analytics with PySprak
PortfolioOptimiz
Contains the R- shiny code for Portfolio Optimization
Recomendation-Engine
Recomndation Engine for Song Database
pseemakurthi's Repositories
pseemakurthi/2017
Course material for BST 260 in Fall 2017
pseemakurthi/Analytics_with_pyspark
Analytics with PySprak
pseemakurthi/COMS4995-s18
COMS W4995 Applied Machine Learning - Spring 18
pseemakurthi/data_processing_course
Some class materials for a data processing course using PySpark
pseemakurthi/datasciencectacontent
repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera
pseemakurthi/datos-exploratory-data-analysis
Datos :: Exploratory Data Analysis
pseemakurthi/Deep-Learning-Boot-Camp
A community run, 5-day PyTorch Deep Learning Bootcamp
pseemakurthi/deep_learning_keras
repo for DL algorithms via Keras with TF backend.
pseemakurthi/exl-train
pseemakurthi/full-stack-data-science
Full Stack Data Science in Python
pseemakurthi/handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
pseemakurthi/kaggle-berlin
Material of the Kaggle Berlin meetup group!
pseemakurthi/learning-apache-spark
This repository contains apache spark tutorials implemented with pypsark. For some machine learning methods, there will be comparisons between pyspark and R results.
pseemakurthi/Links_and_Articles
Links to articles that I find interesting
pseemakurthi/managers-playbook
:book: Heuristics for effective management
pseemakurthi/MLAlgorithms
Minimal and clean examples of machine learning algorithms
pseemakurthi/MLCourse-1
Materials for Data Literacy in the Age of Machine Learning
pseemakurthi/mlcourse_open
OpenDataScience Machine Learning course (yet Russian-only)
pseemakurthi/mlv-tools-tutorial
Tutorial for a new versioning Machine Learning pipeline
pseemakurthi/pandas_exercises
Practice your pandas skills!
pseemakurthi/practical-machine-learning-with-python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
pseemakurthi/Practical_RL
A course in reinforcement learning in the wild
pseemakurthi/PyData_2017
pseemakurthi/pytudes
Python programs to practice or demonstrate skills.
pseemakurthi/solutions
Solutions for projects.
pseemakurthi/Statistical-Rethinking-with-Python-and-PyMC3
Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath
pseemakurthi/t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
pseemakurthi/Technical_Book_DL
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.
pseemakurthi/tiny_python_projects
Code for Tiny Python Projects (Manning, 2020, ISBN 1617297518). Learning Python through test-driven development of games and puzzles.
pseemakurthi/TRAIN_5day
Material for training