vasulakkaraju mldeveloper01

Since I spent essentially two full days figuring out how to access a corporate MS SQL database pythonicly, I figured I should leave some notes, for future reference and to aid other souls looking to do the same.

These instructions and the commands that follow, were executed on a MAC OS 10.8.3 system. Additionally, I found this blog [post][1] especially helpful during the debugging process.

On mac os, there is a default ODBC manager, iODBC. Other Unix based systems tend to use [unixODBC][2]. Look elsewhere for a discussion about the differences between these driver managers. The only feature we care about is being able to connect to SQL databases through [pyodbc][3], and at the time of this writing [pyodbc][3] requires iODBC as its manager.

Start by installing freeTDS libraries. FreeTDS allows unix programs to talk natively with MS SQL and SyBase databases.

brew intsall freetds

100 Days Of ML Code

Hi! I am Abhini, a Machine Learning Enthusiast and this is my log for the 100DaysOfMLCode Challenge

Day 1: July 08, 2018

Today's Progress: Understood the basics of Neural Network and how to build ANN. Also practiced Python on Hackerrank.

Thoughts: Cleared up my concepts on ANN in which I had earlier found confusing like Activation and Cost functions, Batch and Stochastic Gradient Descent and Backpropagation.

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

	#############################################################################
	# Full Imports

	from __future__ import division
	import math
	import random

	"""
	This is a pure Python implementation of the K-means Clustering algorithmn. The
	original can be found here:

	# Get IAM Role name from Instance Profile Id
	curl http://169.254.169.254/latest/meta-data/iam/info

	# Get credentials
	curl http://169.254.169.254/latest/meta-data/iam/security-credentials/<role-name>

	# More info
	http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-metadata.html

	{
	"logging": {
	"version": 1,
	"disable_existing_loggers": true,
	"formatters": {
	"brief": {
	"class": "logging.Formatter",
	"datefmt": "%I:%M:%S",
	"format": "%(levelname)-8s; %(name)-15s; %(message)s"
	},

	{
	"logging": {
	"version": 1,
	"disable_existing_loggers": true,
	"formatters": {
	"brief": {
	"class": "logging.Formatter",
	"datefmt": "%I:%M:%S",
	"format": "%(levelname)-8s; %(name)-15s; %(message)s"
	},

	#/usr/bin/python3
	""" Demonstration of logging feature for a Flask App. """

	from logging.handlers import RotatingFileHandler
	from flask import Flask, request, jsonify
	from time import strftime

	__author__ = "@ivanleoncz"

	import logging

	A warning occurred (42 apples)
	An error occurred