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Machine Learning Simplified
by Andrew Wolf • 2022
Supervised learning, gently: pipeline, optimization, and the real reasons your model overfits.
4.87
193
Machine Learning Simplified Summary
Machine Learning Simplified 92%
Supervised learning, gently: pipeline, optimization, and the real reasons your model overfits.
by Andrew Wolf 2022
4.87
193 ratings
Python Machine Learning Case Studies
by Danish Haroon • 2017
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
2.60
5
Python Machine Learning Case Studies Summary
Python Machine Learning Case Studies 91%
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
by Danish Haroon 2017
2.60
5 ratings
Practical Statistics for Data Scientists
by Peter Bruce • 2020
The statistics data scientists actually use, from EDA to ensembles, with working R and Python code.
4.21
261
Practical Statistics for Data Scientists Summary
Practical Statistics for Data Scientists 90%
The statistics data scientists actually use, from EDA to ensembles, with working R and Python code.
by Peter Bruce 2020
4.21
261 ratings
Low-Code AI
by Gwendolyn Stripling • 2023
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
4.33
6
Low-Code AI Summary
Low-Code AI 90%
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
by Gwendolyn Stripling 2023
4.33
6 ratings
Predictive Analytics For Dummies
by Anasse Bari • 2013
A guide to building models that forecast, classify, and find hidden patterns, without a math degree.
3.74
111
Predictive Analytics For Dummies Summary
Predictive Analytics For Dummies 90%
A guide to building models that forecast, classify, and find hidden patterns, without a math degree.
by Anasse Bari 2013
3.74
111 ratings
The Elements of Statistical Learning
by Trevor Hastie • 2001
The unified mathematical engine under linear regression, trees, and neural nets.
4.43
2k+
The Elements of Statistical Learning Summary
The Elements of Statistical Learning 90%
The unified mathematical engine under linear regression, trees, and neural nets.
by Trevor Hastie 2001
4.43
2k+ ratings
SPSS Survival Manual
by Julie Pallant • 2001
Your SPSS survival guide: plain-English steps from data entry to ANOVA, no stats panic required.
4.11
372
SPSS Survival Manual Summary
SPSS Survival Manual 90%
Your SPSS survival guide: plain-English steps from data entry to ANOVA, no stats panic required.
by Julie Pallant 2001
4.11
372 ratings
Python Data Science Handbook
by Jake VanderPlas • 2016
Four libraries, one workflow: the Python path from raw data to validated predictions.
4.29
676
Python Data Science Handbook Summary
Python Data Science Handbook 90%
Four libraries, one workflow: the Python path from raw data to validated predictions.
by Jake VanderPlas 2016
4.29
676 ratings
Practical Time Series Analysis
by Aileen Nielsen • 2019
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
3.77
64
Practical Time Series Analysis Summary
Practical Time Series Analysis 90%
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
by Aileen Nielsen 2019
3.77
64 ratings
Statistics 101
by David Borman • 2018
A single-volume stats class: summarize data, test hunches, and build forecasts, minus the jargon.
2.94
147
Statistics 101 Summary
Statistics 101 90%
A single-volume stats class: summarize data, test hunches, and build forecasts, minus the jargon.
by David Borman 2018
2.94
147 ratings
Forecasting
by Rob J. Hyndman • 2013
Build forecasts that work: test on unseen data, beat simple benchmarks, reconcile every level.
4.39
318
Forecasting Summary
Forecasting 89%
Build forecasts that work: test on unseen data, beat simple benchmarks, reconcile every level.
by Rob J. Hyndman 2013
4.39
318 ratings
Machine Learning with R
by Brett Lantz • 2015
Build the algorithms that catch spam, flag fraud, and recommend products: a hands-on guide in R.
4.20
312
Machine Learning with R Summary
Machine Learning with R 89%
Build the algorithms that catch spam, flag fraud, and recommend products: a hands-on guide in R.
by Brett Lantz 2015
4.20
312 ratings
Fundamentals of Machine Learning for Predictive Data Analytics
by John D. Kelleher • 2015
Pick the right machine learning approach for any business problem, guided by real case studies.
4.35
105
Fundamentals of Machine Learning for Predictive Data Analytics Summary
Fundamentals of Machine Learning for Predictive Data Analytics 89%
Pick the right machine learning approach for any business problem, guided by real case studies.
by John D. Kelleher 2015
4.35
105 ratings
Data Science for Business
by Foster Provost • 2013
Why overfitting sinks most data projects, and the conceptual tools that stop it.
4.13
3k+
Data Science for Business Summary
Data Science for Business 89%
Why overfitting sinks most data projects, and the conceptual tools that stop it.
by Foster Provost 2013
4.13
3k+ ratings
Introduction To Machine Learning
by Ethem Alpaydin • 2004
The mathematical core of machine learning, taught without cutting corners: from Bayes to boosting.
3.78
251
Introduction To Machine Learning Summary
Introduction To Machine Learning 89%
The mathematical core of machine learning, taught without cutting corners: from Bayes to boosting.
by Ethem Alpaydin 2004
3.78
251 ratings
Credit Scoring and Its Applications
by Lyn C. Thomas • 1987
How the algorithms that decide who gets a loan actually work, and how to build them.
3.62
13
Credit Scoring and Its Applications Summary
Credit Scoring and Its Applications 89%
How the algorithms that decide who gets a loan actually work, and how to build them.
by Lyn C. Thomas 1987
3.62
13 ratings
The Art of Statistics
by David Spiegelhalter • 2019
Numbers are mute. How statisticians make them speak, and why p-values and correlation aren't enough.
4.15
6k+
The Art of Statistics Summary
The Art of Statistics 89%
Numbers are mute. How statisticians make them speak, and why p-values and correlation aren't enough.
by David Spiegelhalter 2019
4.15
6k+ ratings
The Hundred-Page Machine Learning Book
by Andriy Burkov • 2019
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
4.25
1k+
The Hundred-Page Machine Learning Book Summary
The Hundred-Page Machine Learning Book 89%
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
by Andriy Burkov 2019
4.25
1k+ ratings
Practical Statistics for Data Scientists
by Peter Bruce • 2017
The statistical methods data scientists use daily, from boxplots to bootstrap, minus the proofs.
4.02
545
Practical Statistics for Data Scientists Summary
Practical Statistics for Data Scientists 89%
The statistical methods data scientists use daily, from boxplots to bootstrap, minus the proofs.
by Peter Bruce 2017
4.02
545 ratings
Data Smart
by John W. Foreman • 2013
Real machine learning inside Excel: cluster customers, forecast demand, optimize decisions. No code.
4.12
1k+
Data Smart Summary
Data Smart 89%
Real machine learning inside Excel: cluster customers, forecast demand, optimize decisions. No code.
by John W. Foreman 2013
4.12
1k+ ratings
Think Stats
by Allen B. Downey • 2011
Statistics taught through code: why simulation and real data beat memorization every time.
3.64
469
Think Stats Summary
Think Stats 89%
Statistics taught through code: why simulation and real data beat memorization every time.
by Allen B. Downey 2011
3.64
469 ratings
Machine Learning for Dummies
by John Paul Mueller • 2016
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
3.53
139
Machine Learning for Dummies Summary
Machine Learning for Dummies 89%
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
by John Paul Mueller 2016
3.53
139 ratings
Doing Data Science
by Cathy O'Neil • 2013
Your model is lying to you. The ninety percent of data science that algorithms won't fix.
3.73
567
Doing Data Science Summary
Doing Data Science 89%
Your model is lying to you. The ninety percent of data science that algorithms won't fix.
by Cathy O'Neil 2013
3.73
567 ratings
AIQ
by Nick Polson • 2018
What a 19th-century nurse and a lost submarine reveal about the algorithms that now run your life.
4.15
742
AIQ Summary
AIQ 89%
What a 19th-century nurse and a lost submarine reveal about the algorithms that now run your life.
by Nick Polson 2018
4.15
742 ratings
Predictive Analytics
by Eric Siegel • 2013
Companies know whether you'll leave, buy, or default before you've decided. Here's how.
3.66
2k+
Predictive Analytics Summary
Predictive Analytics 89%
Companies know whether you'll leave, buy, or default before you've decided. Here's how.
by Eric Siegel 2013
3.66
2k+ ratings
Research Methodology
by C.R. Kothari • 1985
The gap between a research question and a publishable finding, bridged step by step.
3.88
306
Research Methodology Summary
Research Methodology 89%
The gap between a research question and a publishable finding, bridged step by step.
by C.R. Kothari 1985
3.88
306 ratings
Machine Learning For Absolute Beginners
by Oliver Theobald • 2017
How machine learning algorithms think, from regression to neural networks, no math required.
4.12
474
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 88%
How machine learning algorithms think, from regression to neural networks, no math required.
by Oliver Theobald 2017
4.12
474 ratings
How to Measure Anything
by Douglas W. Hubbard • 1985
That thing you call immeasurable? Redefine measurement; the toolkit is simpler than anyone admits.
3.90
4k+
How to Measure Anything Summary
How to Measure Anything 88%
That thing you call immeasurable? Redefine measurement; the toolkit is simpler than anyone admits.
by Douglas W. Hubbard 1985
3.90
4k+ ratings
Naked Statistics
by Charles Wheelan • 2012
Statistics explained so well you'll stop fearing the numbers and start questioning the headlines.
3.96
15k+
Naked Statistics Summary
Naked Statistics 88%
Statistics explained so well you'll stop fearing the numbers and start questioning the headlines.
by Charles Wheelan 2012
3.96
15k+ ratings
Numsense! Data Science for the Layman
by Annalyn Ng • 2017
Data science without the math: twelve techniques, what they do, and why intuition alone falls short.
4.14
622
Numsense! Data Science for the Layman Summary
Numsense! Data Science for the Layman 88%
Data science without the math: twelve techniques, what they do, and why intuition alone falls short.
by Annalyn Ng 2017
4.14
622 ratings
Data Science
by John D. Kelleher • 2018
Turning raw data into decisions requires a defined process, not just algorithms and statistics.
3.90
876
Data Science Summary
Data Science 88%
Turning raw data into decisions requires a defined process, not just algorithms and statistics.
by John D. Kelleher 2018
3.90
876 ratings
Becoming a Data Head
by Alex J. Gutman • 2021
The critical thinking toolkit for anyone who works with data but never plans to code.
4.18
480
Becoming a Data Head Summary
Becoming a Data Head 88%
The critical thinking toolkit for anyone who works with data but never plans to code.
by Alex J. Gutman 2021
4.18
480 ratings
Big Data
by Viktor Mayer-Schönberger • 2013
The era of sampling, clean data, and causal certainty is over. What replaces it is stranger.
3.69
9k+
Big Data Summary
Big Data 88%
The era of sampling, clean data, and causal certainty is over. What replaces it is stranger.
by Viktor Mayer-Schönberger 2013
3.69
9k+ ratings
Python for Finance
by Yves Hilpisch • 2012
Python's finance stack: vectorized data, Monte Carlo methods, deploying models from Excel to web.
3.80
249
Python for Finance Summary
Python for Finance 88%
Python's finance stack: vectorized data, Monte Carlo methods, deploying models from Excel to web.
by Yves Hilpisch 2012
3.80
249 ratings
Algorithms of the Intelligent Web
by Haralambos Marmanis • 2009
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
3.62
130
Algorithms of the Intelligent Web Summary
Algorithms of the Intelligent Web 88%
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
by Haralambos Marmanis 2009
3.62
130 ratings
Introduction to Machine Learning with Python
by Andreas C. Müller • 2015
Build models that generalize, not just memorize: a Python data scientist's guide.
4.33
600
Introduction to Machine Learning with Python Summary
Introduction to Machine Learning with Python 88%
Build models that generalize, not just memorize: a Python data scientist's guide.
by Andreas C. Müller 2015
4.33
600 ratings
The Numbers Game
by Michael Blastland • 2008
Most numbers in the news mislead you; a few mental habits reveal the real story.
3.65
344
The Numbers Game Summary
The Numbers Game 88%
Most numbers in the news mislead you; a few mental habits reveal the real story.
by Michael Blastland 2008
3.65
344 ratings
The Math of Life and Death
by Kit Yates • 2019
Key mathematical ideas govern medicine, law, and media, and your intuition gets each one wrong.
3.90
2k+
The Math of Life and Death Summary
The Math of Life and Death 88%
Key mathematical ideas govern medicine, law, and media, and your intuition gets each one wrong.
by Kit Yates 2019
3.90
2k+ ratings
Python for Data Analysis
by Wes McKinney • 2011
The pandas creator teaches the full Python data pipeline: messy files in, statistical models out.
4.17
2k+
Python for Data Analysis Summary
Python for Data Analysis 88%
The pandas creator teaches the full Python data pipeline: messy files in, statistical models out.
by Wes McKinney 2011
4.17
2k+ ratings
The Model Thinker
by Scott E. Page • 2018
Big data without many models is just noise. The case for thinking through multiple frameworks.
3.92
955
The Model Thinker Summary
The Model Thinker 88%
Big data without many models is just noise. The case for thinking through multiple frameworks.
by Scott E. Page 2018
3.92
955 ratings
Machine Learning For Absolute Beginners
by Oliver Theobald • 2018
A plain English path from zero to a working model: the algorithms, data prep, and code.
3.96
786
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 88%
A plain English path from zero to a working model: the algorithms, data prep, and code.
by Oliver Theobald 2018
3.96
786 ratings
The Flaw of Averages
by Sam L. Savage • 2009
A single average can sink your plan. The missing skill: thinking in distributions.
3.86
574
The Flaw of Averages Summary
The Flaw of Averages 88%
A single average can sink your plan. The missing skill: thinking in distributions.
by Sam L. Savage 2009
3.86
574 ratings
The Signal and the Noise
by Nate Silver • 2012
Why weather forecasters improve while economists flounder, and what that reveals about prediction.
3.97
53k+
The Signal and the Noise Summary
The Signal and the Noise 88%
Why weather forecasters improve while economists flounder, and what that reveals about prediction.
by Nate Silver 2012
3.97
53k+ ratings
Practical SQL
by Anthony DeBarros • 2022
Write real SQL: load data, join tables, run statistics, map locations, and query JSON in PostgreSQL.
4.26
239
Practical SQL Summary
Practical SQL 88%
Write real SQL: load data, join tables, run statistics, map locations, and query JSON in PostgreSQL.
by Anthony DeBarros 2022
4.26
239 ratings
AI for Data Science
by Zacharias Voulgaris • 2018
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
4.55
20
AI for Data Science Summary
AI for Data Science 88%
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
by Zacharias Voulgaris 2018
4.55
20 ratings
Statistics for Dummies
by Deborah J. Rumsey • 2003
The numbers behind the headline, the margin of error they hid, and how to spot the difference.
3.66
667
Statistics for Dummies Summary
Statistics for Dummies 88%
The numbers behind the headline, the margin of error they hid, and how to spot the difference.
by Deborah J. Rumsey 2003
3.66
667 ratings
The Economist Numbers Guide
by The Economist • 2001
A 50% raise then 50% cut leaves you at 75%. The business math that stops costly errors.
3.84
67
The Economist Numbers Guide Summary
The Economist Numbers Guide 88%
A 50% raise then 50% cut leaves you at 75%. The business math that stops costly errors.
by The Economist 2001
3.84
67 ratings
Competing on Analytics
by Thomas H. Davenport • 2007
Why some companies (like Amazon and Capital One) win by making bets on data, not hunches.
3.69
2k+
Competing on Analytics Summary
Competing on Analytics 88%
Why some companies (like Amazon and Capital One) win by making bets on data, not hunches.
by Thomas H. Davenport 2007
3.69
2k+ ratings
The Data Detective
by Tim Harford • 2020
Your emotions react to statistics before your brain does. Ten rules for getting the numbers right.
4.12
8k+
The Data Detective Summary
The Data Detective 88%
Your emotions react to statistics before your brain does. Ten rules for getting the numbers right.
by Tim Harford 2020
4.12
8k+ ratings
The Book of Why
by Judea Pearl • 2018
Data can't tell you why. A mathematical science of cause and effect finally can.
3.93
7k+
The Book of Why Summary
The Book of Why 88%
Data can't tell you why. A mathematical science of cause and effect finally can.
by Judea Pearl 2018
3.93
7k+ ratings
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