2017. december 13., szerda

Py, R

Essentials of Machine Learning Algorithms (with Python and R Codes)
SerpentAI

the-flask-mega-tutorial-part-i-hello-world
fullstackpython/flask

What are the best tips for learning Python within one month?
datacamp.com/courses/intro-to-python-for-data-science

Building a FAQ Chatbot in Python – The Future of Information Searching
Are you interested to learn building Chatbots for answering queries? This guide demonstrates exactly the same, in Python.
A Beginner's Guide to Channel Attribution Modeling in Marketing (using Markov Chains, with a case study in R)
Let's take a look at what channel attribution is and how it ties into the concept of Markov chains, explained using case study in R.
The Ultimate Learning Path to Becoming a Data Scientist in 2018
Make most of 2018 now! Here's your learning path to learn Data Science & pursue career as Data Scientist in most structured way.


Improve Your Model Performance using Cross Validation (in Python and R)

 https://www.udemy.com/machinelearning/

BIN CLASSIFICATION

Quickstart: Binary classification with microsoftml - Machine Learning

Binary Classification Tutorial with the Keras Deep Learning Library

Py:

Statistics

Think Stats – Probability and Statistics for Programmers

Think Stats is an introduction to Probability and Statistics for Python programmers.

  • Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
  • If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.
  • Most introductory books don't cover Bayesian statistics, but Think Statsis based on the idea that Bayesian methods are too important to postpone. By taking advantage of the PMF and CDF libraries, it is possible for beginners to learn the concepts and solve challenging problems.
‘Think Stats’ is an introductory book to statistics and probability for people with a basic background in Python programming. It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs. The book also includes a case study using data from the National Institutes of Health.
One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

A Programmer’s Guide to Data Mining

What I like about this book are the chapters covering recommendation systems. It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured text and how to deal with it, in case you are thinking about getting into Natural Language Processing.


https://www.coursera.org/learn/python-analysis
CERTI MEGSZERZÉSE:
https://www.coursera.org/learn/python-machine-learning
Általános:  aCAP (Master fokozat igazolása + fenntartási költség + pár 100 $). 

TANULNI:
.........
https://www.amazon.com/Deep-Learning-Python https://www.amazon.com/Python-Data-Analysis-Cookbook
http://blog.galvanize.com/seven-python-tools-all-data-scientists-should-know-how-to-use/
 https://www.upwork.com/hiring/data/15-python-libraries-data-science/
 https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7
https://github.com/rasbt/pattern_classification/blob/master/resources/python_data_libraries.md
 https://www.datasciencecentral.com/profiles/blogs/9-python-analytics-libraries-1
http://stackabuse.com/the-best-machine-learning-libraries-in-python/
https://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html









 R:

Basic Machine Learning and Statistics

 Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
One of the most popular entries in this list, it’s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It’s filled with practical real-world examples of where and how algorithms work.
For those with an inclination towards R programming, this book even has practical examples in R. In case you’re not a programmer, don’t let that put you off. This book is a gem.

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