UDEMY_CONNECT_THE_DOTS_LINEAR_AND_LOGISTIC_REGRESSION_TUTORIAL-COMPRISED

Section

Appz

Group

COMPRISED

Size

577,00 MB

Files

15

Date

2017-03-09

NFO

                          ____                        ____      ____
      /\___       ___/\  / __/           ___/\ __    / __/     / __/  /\____
   ___\ __/__/\___\_ _ \/ /________/\____\ __/(__)___\_\______/ /_____\____ \
  /  __/  __ __  __  \   /  __  __   _   _/__    _  __/  ___  ___/ __    |/  \
 /   \|   \/ \/  \/   \ /   \/  |/  //   \  /   //  \|   \ /  \|   \/    |    \
 \    |___/\_____/\____Y    /   |\_/ \___/  \  //    :___//    :___/\    |____/
  \___:                :___/\___:            \/ \___/     \___/ ____ \___:
     _________________    __      __ :: ____    _________       \  /  _____ gRK
  __/ r e l e a s e   \/_/  \_____\/_  /    \  /         \__  ___\/__(_    \___
 _\ \ i n f o         /\             \/      \/           \ )/               _/
 \  _\__________________\            /                                       :
 _\   \                                                                      |_
 \/                                                                         _|/
 |                                                                          \_.
 | Connect the Dots: Linear and Logistic Regression                           |
 | (c) Loony Corn                                                             |
 |                                                                            |
 | Publisher ....: Udemy                                                      |
 | Level ........: All Levels                                                 |
 | Runtime ......: 290 minutes                                                |
 | Language .....: English                                                    |
 | Release Type .: Retail                                                     |
 | Release Format: ISO                                                        |
 | Store Date ...: 2017.02.23                                                 |
 | Release Date .: 2017.03.08                                                 |
 | ISO Size .....: 605,018,112                                                |
 | ISO Checksum .: D37AB934                                                   |
 | Disk Count ...: 13 * 50MB                                                  |
 | Disk Name ....: comprised_ucdllr                                           |
 |                                                                            |
 | Link/URL .....: https://v.gd/ww2vAs                                        |
 |                                                                            |
 |                                                                            |
 :          ______________/               ________             _______________|
 :___/\______) ,. ...;;; /________________\%%%%%%/_____________\ ::; ,,,: ...
 : . _________________    __  ,,. __    ___\%%%%/________ ;;: : \     _____   :
  __/                 \/_/  \_____\/_  /    \%%/         \__  ___\/__(_    \/__
 _\ \ n o t e s       /\             \/      \/           \ )/               _/
 \  _\__________________\            /                                       :
 _\   \                                                                      |_
 \/                                                                         _|/
 |                                                                          \_.
 |                                                                            |
 | Taught byáa Stanford-educated, ex-Googler and an IIT, IIM - educated      |
 |  ex-Flipkart lead analyst. This team has decades of practical experience in|
 |  quant trading, analytics and e-commerce.                                  |
 |                                                                            |
 | This course will teach youáhow to build robust linear models and do       |
 |  logistic regression ináExcel, R and Python.                              |
 |                                                                            |
 | LetÆs parse that.                                                        |
 |                                                                            |
 | Robust linear models :áLinear Regression is a powerful method for         |
 |  quantifyingáthe cause and effect relationships that affect different     |
 |  phenomena in the world around us. This course will teach youáhow to build|
 |  robust linear models that will stand up to scrutiny when you apply them to|
 |  real world situations.                                                    |
 |                                                                            |
 | Logistic regression:áLogistic regression has many cool applications       |
 |  :áanalyzing consequences of past events, allocating resources, solving   |
 |  binary classification problems using machine learning and so on. This     |
 |  course will help you understand the intuition behind logistic regression  |
 |  and how to solve it using cookie-cutter techniques.                       |
 |                                                                            |
 | Excel, R and Python : áPut what you've learnt into practice. Leverage     |
 |  these powerful analytical tools to build models for stock returns.        |
 |                                                                            |
 | What's covered?                                                            |
 |                                                                            |
 | Simple Regression :                                                        |
 |                                                                            |
 | * Method of least squares, Explaining variance, Forecasting an outcome     |
 | * Residuals, assumptions about residuals                                   |
 | * Implement simple regression in Excel, R and Python                       |
 | * Interpret regression results and avoid common pitfalls                   |
 |                                                                            |
 | Multiple Regression :                                                      |
 |                                                                            |
 | * Implement Multiple regression in Excel, R and Python                     |
 | * Introduce a categorical variable                                         |
 |                                                                            |
 | Logistic Regression :                                                      |
 |                                                                            |
 | * Applications of Logistic Regression, the link to Linear Regression and   |
 |  Machine Learning                                                          |
 | * Solving logistic regression usingáMaximum Likelihood Estimation and     |
 |  Linear Regression                                                         |
 | * Extending Binomial Logistic Regression to Multinomial Logistic Regression|
 | * Implement Logistic regression to build a model stock price movements in  |
 |  Excel, R and Python                                                       |
 |                                                                            |
 |                                                                            |
 | Talk to us!                                                                |
 |                                                                            |
 | * Mail us about anything - anything! - and we will always reply :-)        |
 |                                                                            |
 |                                                                            |
 |                                                                            |
 |                                                                            |
 |                                                                            |
 :          ______________/               ________             _______________|
 :___/\______) ::::; ::  /________________\%%%%%%/_____________\ ..::..:: .: ;.
  ;; _________________    __ .::; __    ___\%%%%/________       \     _____   ;
  __/ a b o u t       \/_/  \_____\/_  /    \%%/         \__  ___\/__(_    \/__
 _\ \ t h e g r o u p /\             \/      \/           \ )/               _/
 \  _\__________________\            /                                       :
 _\   \                                                                      |_
 \/ No news is good news!                                                   _|/
 |                                                                          \_.
 |                                                                            |
 |                                                                            |
 |                                                                            |
 :          ______________/               ________             _______________|
 :___/\______) ,.::;; ;. /________________\%%%%%%/_____________\  ...,:::; ;.;:
 ::  _________________    __  ::; __    ___\%%%%/________ ...:: \     _____   ;
  __/                 \/_/  \_____\/_  /    \%%/         \__  ___\/__(_    \/__
 _\ \ g r e e t s     /\             \/      \/           \ )/               _/
 \  _\__________________\            /                                       :
 _\   \                                                                      |_
 \/ Some hardworking groups, and maybe you?                                 _|/
 |                                                                          \_.
 |                                                                            |
 |                                                                            |
 :          ______________/               ________             _______________|
 :___/\______);;: ...::, /________________\%%%%%%/_____________\ ::.. ;;;;: : :
 ::  ... . ., :        :  ...,,  . ::: :,  \%%%%/  ::: :;,,...,  :          .
                                            \%%/ nfo by griskokare/impure!ascii
                                             \/

Files