kk0202418 Goto Github PK

10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

20-ml-algorithms-from-start-to-finish

I want to solve a popular machine learning Data-set as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template to deal with machine learning problems.

207-applied-machine-learning

Materials for UC Berkeley School of Information course Applied Machine Learning

52-weeks-of-data-science

applied_deep_learning

books

Collection of Programming, Databases, Linux & Tools Books

bootcamps-201819

Teaching materials for bi-weekly machine learning bootcamps at the London School of Economics by the 2018/19 ML@LSE Committee

coms4995-s19

COMS W4995 Applied Machine Learning - Spring 19

coursera-advanced-machine-learning-specialization

Repo for coursera specialization Advanced Machine Learning by Higher School of Economics

data-science-machine-learning-ai-resources

A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more.

duke-mlss-2018

Duke Machine Learning Summer School 2018

exploratory-data-analysis

Techniques to prepare data for analysis, feature selection, and feature extraction.

fastai-v3

Starter app for fastai v3 model deployment on Render

gitproject

This is my first github repository

glearning-git-session

google-interview-university

A complete daily plan for studying to become a Google software engineer.

hands-on-data-analytics-for-beginners-with-google-colaboratory-video-

Hands-On Data Analytics for Beginners with Google Colaboratory [Video], published by Packt

intro-numerical-methods

Jupyter notebooks and other materials developed for the Columbia course APMA 4300

introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

machine-learning-by-example

Using Python, NumPy, SciPy, and Pandas to build machine learning algorithms for regression and classification.

machine-learning-course-2018

Course materials for M-5072 Fall 2018 at the Harvard Extension School

ml_notes

my-first-practice

numerical-methods-pdes

Jupyter notebook class notes for Numerical Methods for PDEs

pluralsight-vscode-samples

VS Code samples for Pluralsight course on Code

python-decoding-sensor-data

python_notes

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

refprop-wrappers

Wrappers around NIST REFPROP for languages such as Python, MATLAB, etc.