Data Science and Machine Learning for Managers and MBAs
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Data Science and Machine Learning for Managers and MBAs

Learn machine learning algorithms from scratch, intuitive understanding, near zero math
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
4 students enrolled
Created by Kumar V
Last updated 8/2017
English
Current price: $10 Original price: $100 Discount: 90% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 6.5 hours on-demand video
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Discuss machine learning models intelligently with engineers, ask the right questions, manage machine learning projects effectively.
View Curriculum
Requirements
  • This course focuses on building your intuitive understanding of machine learning techniques.
  • Starts from scratch. 100% intuition, near 0% math
Description

Designed for MBAs and managers - people with solid business experience but rusty or non-existent math skills - this course starts from scratch, and explains the major concepts, models, tools, and metrics used in machine learning, as well as management best practices for overseeing large scale data science projects. We already cover a lot of ground, and believe in further improving course material over time.

Who is the target audience?
  • Anyone interested in machine learning can take this course.
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Curriculum For This Course
110 Lectures
06:18:24
+
Preliminaries
5 Lectures 15:33



machine learning examples from industry
06:19

what is data mining?
02:06
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manager tools
4 Lectures 15:25
manager tools you can use
03:38

programming tools your team can use
03:03

setting up your team for success
03:38

characteristics of big data (the 5 v's)
05:06
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managing data science projects
8 Lectures 26:01
steps in any data science project
03:23

collecting data - tools, data pipelines, etc.
03:28

data pre-processing
04:12

models, models, everywhere
01:37

building models and analyzing data
02:29

standardizing data sets
02:44

data snooping, lookahead bias, and other biases in analysis
04:46

presenting results with visualizations
03:22
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data science building blocks - basic concepts and ideas
17 Lectures 01:11:10
defining terms - records, features, outcomes
03:30

"wider" and "longer" data sets
03:14

parametric, semi-parametric, and non-parametric methods
03:04

types of variables
04:56

random variables and probability distributions
03:19

independent, and identically distributed random variables
02:55

univariate and multivariate random variables
03:42

classification and regression
05:17

optimization
03:54

convex functions and gradient descent
06:07

simulation (monte-carlo)
06:25

training, validation, and test data-sets
04:48

cross-validation
05:26

training and test errors
03:58

different types of sampling schemes
03:46

random, and pseudo-random number generators
02:20

over-sampling and under-sampling
04:29
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machine learning building blocks
13 Lectures 41:16
supervised and semi-supervised learning
05:50

unsupervised learning
03:06

learnability
05:00

curse of dimensionality
02:20

trade-offs in system design
01:48

bias-variance trade-off
02:14

variance reduction techniques
02:25

false positives and false negatives
01:40

evaluating model quality (precision, recall, ...)
05:13

entropy
02:46

the gini coefficient
02:14

stationarity
03:31

ergodicity
03:09
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Linear Regression Models
20 Lectures 01:02:38
types of data - cross-sectional, longitudinal, time-series
02:09

simple linear regression (ordinary least squares)
04:22

linear regression without intercept
02:50

least absolute deviation (LAD) models
02:30

multiple linear regression
05:06

correlation and causation
01:52

covariance and correlation
04:28

maximum likelihood estimation (MLE)
01:35

the central limit theorem (CLT)
02:50

the central limit theorem in action
04:47

the law of large numbers
01:45

on the normality of regression residuals
02:17

types of statistical tests and their use
01:43

visual tests Q-Q plots
02:35

statistical tests for normality
04:10

moments of a distribution, and the moment generating function
03:46

the gauss markov theorem and BLUE
01:28

the law of parsimony
01:18

things to watch for in a linear regression
06:08

evaluating a regression model
04:59
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Hypothesis Testing and OLS model metrics
7 Lectures 20:06
hypothesis testing and the p-value
02:14

steps in hypothesis testing
02:48

one-tailed and two-tailed tests
03:15

the F-test, and the t-statistic
03:58

confidence intervals
02:43

R2 and Adjusted R2
02:17

computing the R2 of a Simple Linear Regression (SLR)
02:51
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Regression model improvements, variants, and extensions
7 Lectures 24:12
stepwise regression
02:29

non-linear (polynomial) regression
02:16

the Box-Cox, and Tukey transformations
03:42

leverage points and outliers
04:27

under-specified and over-specified regressions
06:38

robust regression
02:12

generalized additive models (GAMs)
02:28
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Other Machine Learning Models - Classifiers and Regressors
19 Lectures 55:30
kNN classification and regression
04:50

the logistic regression
01:48

the pseudo-R2 for a logistic regression
06:20

classification and regression trees (CART)
03:51

bagging (bootstrap aggregating)
02:41

multinomial classifiers (one vs one, one vs many)
03:59

the voronoi tessellation
02:34

boosted trees (boosting)
02:08

random forests
03:09

dimensionality reduction
02:28

eigenvalues and eigenvectors
02:59

dimensionality reduction - principal components analysis
02:11

dimensionality reduction - regularized regressions
02:35

support vector machines
02:35

soft margin classifiers
01:32

the kernel trick
02:01

support vector regression
02:04

K means clustering
03:25

hierarchical clustering
02:20
3 More Sections
About the Instructor
Kumar V
0.0 Average rating
0 Reviews
8 Students
2 Courses
Finance + Technology + Data Science => Impact.

With BE (Hons) and MS degrees in Computer Science & Engineering, and an M7 MBA, Kumar has worked over a decade at a premier research institution, and then for several years at a tier-1 Wall Street bank. He currently works at a VC/PE fund and as a technical advisor to start-ups.