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🎯 Recommendation Systems

Technical and conceptual explorations of the algorithms that decide what you see, buy, and listen to next. These books pull back the curtain on the invisible curators shaping modern life.
Algorithms of the Intelligent Web
by Haralambos Marmanis • 2009
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
3.62
130
🤖 Machine Learning🌐 Web Applications📄 Text Mining
Algorithms of the Intelligent Web Summary
Algorithms of the Intelligent Web 90%
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
by Haralambos Marmanis 2009
3.62
130 ratings
🤖 Machine Learning🌐 Web Applications📄 Text Mining
Machine Learning
by Dr Ruchi Doshi • 2021
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
5.00
1
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Machine Learning Summary
Machine Learning 88%
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
by Dr Ruchi Doshi 2021
5.00
1 ratings
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Rage Inside the Machine
by Robert Elliott Smith • 2019
Algorithms reduce reality to neat categories. Prejudice is not a bug but the predictable exhaust.
3.97
145
🧩 Algorithmic Bias🤖 AI Ethics💻 Tech Criticism
Rage Inside the Machine Summary
Rage Inside the Machine 88%
Algorithms reduce reality to neat categories. Prejudice is not a bug but the predictable exhaust.
by Robert Elliott Smith 2019
3.97
145 ratings
🧩 Algorithmic Bias🤖 AI Ethics💻 Tech Criticism
Everyday Chaos
by David Weinberger • 2019
A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.
3.78
202
🔬 Complexity Science🔄 Systems Thinking🤖 Machine Learning
Everyday Chaos Summary
Everyday Chaos 88%
A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.
by David Weinberger 2019
3.78
202 ratings
🔬 Complexity Science🔄 Systems Thinking🤖 Machine Learning
The Master Algorithm
by Pedro Domingos • 2015
Machine learning's rival schools share a hidden core. Finding it could unlock general intelligence.
3.73
6k+
🤖 Machine Learning📚 Popular Science🧠 Superintelligence
The Master Algorithm Summary
The Master Algorithm 87%
Machine learning's rival schools share a hidden core. Finding it could unlock general intelligence.
by Pedro Domingos 2015
3.73
6k+ ratings
🤖 Machine Learning📚 Popular Science🧠 Superintelligence
Filterworld
by Kyle Chayka • 2024
Your Netflix queue reshapes what artists make: the global aesthetic is now algorithm-friendly.
3.62
5k+
🔄 Algorithmic Curation🏛️ Cultural Homogenization👀 Attention Economy
Filterworld Summary
Filterworld 87%
Your Netflix queue reshapes what artists make: the global aesthetic is now algorithm-friendly.
by Kyle Chayka 2024
3.62
5k+ ratings
🔄 Algorithmic Curation🏛️ Cultural Homogenization👀 Attention Economy
The Ethical Algorithm
by Michael Kearns • 2019
Algorithms don't do ethics by default. Making them fair takes math, and the math demands trade-offs.
4.10
671
⚖️ Algorithmic Bias🔒 Data Privacy🤖 Ai Ethics
The Ethical Algorithm Summary
The Ethical Algorithm 87%
Algorithms don't do ethics by default. Making them fair takes math, and the math demands trade-offs.
by Michael Kearns 2019
4.10
671 ratings
⚖️ Algorithmic Bias🔒 Data Privacy🤖 Ai Ethics
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 Basics📊 Data Science Basics🏷️ Supervised Learning
Machine Learning for Dummies Summary
Machine Learning for Dummies 87%
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
by John Paul Mueller 2016
3.53
139 ratings
🧠 Machine Learning Basics📊 Data Science Basics🏷️ Supervised Learning
Automate This
by Christopher Steiner • 2012
Algorithms took Wall Street. Now they compose music, allocate kidneys, and profile personalities.
3.82
4k+
📈 Algorithmic Trading🤖 Automation And Society💻 Computational Thinking
Automate This Summary
Automate This 87%
Algorithms took Wall Street. Now they compose music, allocate kidneys, and profile personalities.
by Christopher Steiner 2012
3.82
4k+ ratings
📈 Algorithmic Trading🤖 Automation And Society💻 Computational Thinking
The Alignment Problem
by Brian Christian • 2020
AI learns our blind spots along with our data. Correcting that is the central problem in computing.
4.34
5k+
🎯 Ai Alignment🤖 Machine Learning Ethics⚖️ Algorithmic Bias
The Alignment Problem Summary
The Alignment Problem 87%
AI learns our blind spots along with our data. Correcting that is the central problem in computing.
by Brian Christian 2020
4.34
5k+ ratings
🎯 Ai Alignment🤖 Machine Learning Ethics⚖️ Algorithmic Bias
Artificial Intelligence and Machine Learning for Business
by Steven Finlay • 2018
ML projects fail for business reasons, not technical ones. The rules for getting it right.
4.14
207
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
Artificial Intelligence and Machine Learning for Business Summary
Artificial Intelligence and Machine Learning for Business 87%
ML projects fail for business reasons, not technical ones. The rules for getting it right.
by Steven Finlay 2018
4.14
207 ratings
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
The AI Playbook
by Eric Siegel • 2024
How to get machine learning out of the lab: sell the operational improvement, not the algorithm.
3.98
127
🏢 Enterprise Technology🤖 Applied Ai🔄 Change Management
The AI Playbook Summary
The AI Playbook 87%
How to get machine learning out of the lab: sell the operational improvement, not the algorithm.
by Eric Siegel 2024
3.98
127 ratings
🏢 Enterprise Technology🤖 Applied Ai🔄 Change Management
Competing in the Age of AI
by Marco Iansiti • 2020
AI dissolves the old limits of scale, scope, and learning, and demands a new corporate architecture.
3.82
1k+
🤖 Ai Business Models🏗️ Platform Strategy🔗 Data Network Effects
Competing in the Age of AI Summary
Competing in the Age of AI 87%
AI dissolves the old limits of scale, scope, and learning, and demands a new corporate architecture.
by Marco Iansiti 2020
3.82
1k+ ratings
🤖 Ai Business Models🏗️ Platform Strategy🔗 Data Network Effects
Python for Geeks
by Muhammad Asif • 2021
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
4.50
8
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
Python for Geeks Summary
Python for Geeks 87%
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
by Muhammad Asif 2021
4.50
8 ratings
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
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 Basics🐍 Python For Data Science🧹 Data Preparation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 87%
A plain English path from zero to a working model: the algorithms, data prep, and code.
by Oliver Theobald 2018
3.96
786 ratings
🤖 Machine Learning Basics🐍 Python For Data Science🧹 Data Preparation
Hello World
by Hannah Fry • 2018
When algorithms police, diagnose, and drive, blind trust becomes the real danger.
4.11
12k+
🧭 Data Ethics📊 Algorithmic Bias👥 Human In The Loop
Hello World Summary
Hello World 87%
When algorithms police, diagnose, and drive, blind trust becomes the real danger.
by Hannah Fry 2018
4.11
12k+ ratings
🧭 Data Ethics📊 Algorithmic Bias👥 Human In The Loop
AI for Data Science
by Zacharias Voulgaris • 2018
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
4.55
20
📊 Data Science🧠 Deep Learning⚙️ Optimization Algorithms
AI for Data Science Summary
AI for Data Science 87%
The real AI toolkit goes beyond deep learning: optimization, fuzzy logic, ensembles.
by Zacharias Voulgaris 2018
4.55
20 ratings
📊 Data Science🧠 Deep Learning⚙️ Optimization Algorithms
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🧹 Data Preparation⚖️ Model Evaluation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 87%
How machine learning algorithms think, from regression to neural networks, no math required.
by Oliver Theobald 2017
4.12
474 ratings
🧠 Machine Learning🧹 Data Preparation⚖️ Model Evaluation
Low-Code AI
by Gwendolyn Stripling • 2023
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
4.33
6
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Low-Code AI Summary
Low-Code AI 87%
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
by Gwendolyn Stripling 2023
4.33
6 ratings
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Artificial Intelligence (WIRED guides)
by Matthew Burgess • 2021
AI masters chess, spots cancer, drives cars. But who trains it, and on whose data?
3.71
147
🖥️ Machine Learning🧠 Deep Learning⚖️ Ai Ethics
Artificial Intelligence (WIRED guides) Summary
Artificial Intelligence (WIRED guides) 87%
AI masters chess, spots cancer, drives cars. But who trains it, and on whose data?
by Matthew Burgess 2021
3.71
147 ratings
🖥️ Machine Learning🧠 Deep Learning⚖️ Ai Ethics
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
🧠 Data Literacy🤖 Machine Learning Basics📈 Predictive Analytics
Numsense! Data Science for the Layman Summary
Numsense! Data Science for the Layman 87%
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 Literacy🤖 Machine Learning Basics📈 Predictive Analytics
How AI Thinks
by Nigel Toon • 2024
It's a learning engine, not a thinking mind. The revolution is here, and it's not sentient.
3.62
322
💻 Computing History🧠 Machine Learning⚖️ Ai Ethics
How AI Thinks Summary
How AI Thinks 87%
It's a learning engine, not a thinking mind. The revolution is here, and it's not sentient.
by Nigel Toon 2024
3.62
322 ratings
💻 Computing History🧠 Machine Learning⚖️ Ai Ethics
Robin Hood Math
by Noah Giansiracusa • 2025
Algorithms price your insurance, rank your resume, curate your reality. The math that fights back.
3.91
185
🧮 Algorithmic Literacy🛡️ Digital Self-Defense📐 Applied Mathematics
Robin Hood Math Summary
Robin Hood Math 87%
Algorithms price your insurance, rank your resume, curate your reality. The math that fights back.
by Noah Giansiracusa 2025
3.91
185 ratings
🧮 Algorithmic Literacy🛡️ Digital Self-Defense📐 Applied Mathematics
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+
📊 Data Science🔮 Predictive Analytics📈 Big Data
Predictive Analytics Summary
Predictive Analytics 87%
Companies know whether you'll leave, buy, or default before you've decided. Here's how.
by Eric Siegel 2013
3.66
2k+ ratings
📊 Data Science🔮 Predictive Analytics📈 Big Data
AI and Machine Learning for Coders
by Laurence Moroney • 2021
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
4.09
108
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
AI and Machine Learning for Coders Summary
AI and Machine Learning for Coders 87%
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
by Laurence Moroney 2021
4.09
108 ratings
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
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+
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
The Hundred-Page Machine Learning Book Summary
The Hundred-Page Machine Learning Book 87%
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
by Andriy Burkov 2019
4.25
1k+ ratings
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
The Long Tail
by Chris Anderson • 2006
Digital shelves flip the economics: niche products collectively now outsell blockbusters.
3.82
30k+
🖥️ Digital Economics🎯 Niche Markets🔍 Algorithmic Curation
The Long Tail Summary
The Long Tail 87%
Digital shelves flip the economics: niche products collectively now outsell blockbusters.
by Chris Anderson 2006
3.82
30k+ ratings
🖥️ Digital Economics🎯 Niche Markets🔍 Algorithmic Curation
Weapons of Math Destruction
by Cathy O'Neil • 2016
How the algorithms that score your life lock in poverty, deepen racism, and erode democracy.
3.87
30k+
🧩 Algorithmic Bias📊 Big Data Ethics🔒 Data Privacy
Weapons of Math Destruction Summary
Weapons of Math Destruction 87%
How the algorithms that score your life lock in poverty, deepen racism, and erode democracy.
by Cathy O'Neil 2016
3.87
30k+ ratings
🧩 Algorithmic Bias📊 Big Data Ethics🔒 Data Privacy
Artificial Intelligence Basics
by Tom Taulli • 2019
AI without the math: what machine learning does and where automation fits in your business.
3.50
210
🧠 Ai Fundamentals🏭 Ai Adoption🔄 Digital Transformation
Artificial Intelligence Basics Summary
Artificial Intelligence Basics 87%
AI without the math: what machine learning does and where automation fits in your business.
by Tom Taulli 2019
3.50
210 ratings
🧠 Ai Fundamentals🏭 Ai Adoption🔄 Digital Transformation
Hadoop
by Tom White • 2009
How to turn cheap servers into a petabyte-scale data platform, from HDFS to real-time streaming.
3.93
1k+
📊 Big Data🖧 Distributed Systems🐘 Hadoop Ecosystem
Hadoop Summary
Hadoop 87%
How to turn cheap servers into a petabyte-scale data platform, from HDFS to real-time streaming.
by Tom White 2009
3.93
1k+ ratings
📊 Big Data🖧 Distributed Systems🐘 Hadoop Ecosystem
The Inevitable
by Kevin Kelly • 2016
Twelve forces are bending civilization in one direction, and we have just left the gate.
3.89
12k+
🔄 Digital Economy🔮 Technological Forecasting📊 Information Economy
The Inevitable Summary
The Inevitable 87%
Twelve forces are bending civilization in one direction, and we have just left the gate.
by Kevin Kelly 2016
3.89
12k+ ratings
🔄 Digital Economy🔮 Technological Forecasting📊 Information Economy
Bursts
by Albert-László Barabási • 2010
Why your email bursts, your travels repeat, and history rhymes: one hidden law of priority.
3.30
1k+
🕸️ Complex Systems🔗 Network Science📉 Power Laws
Bursts Summary
Bursts 87%
Why your email bursts, your travels repeat, and history rhymes: one hidden law of priority.
by Albert-László Barabási 2010
3.30
1k+ ratings
🕸️ Complex Systems🔗 Network Science📉 Power Laws
Google Hacks
by Rael Dornfest • 2003
Hidden syntax, tools, and 800 million Usenet messages: how to find anything with Google.
3.68
301
🔍 Internet Research🔎 Search Techniques💻 Hacker Culture
Google Hacks Summary
Google Hacks 87%
Hidden syntax, tools, and 800 million Usenet messages: how to find anything with Google.
by Rael Dornfest 2003
3.68
301 ratings
🔍 Internet Research🔎 Search Techniques💻 Hacker Culture
Artificial Intelligence in Practice
by Bernard Marr • 2019
Robots patrol Walmart aisles. Netflix guesses your next watch. How 50 companies already run on AI.
3.41
251
🤖 Ai In Business📋 Corporate Case Studies🧠 Applied Machine Learning
Artificial Intelligence in Practice Summary
Artificial Intelligence in Practice 87%
Robots patrol Walmart aisles. Netflix guesses your next watch. How 50 companies already run on AI.
by Bernard Marr 2019
3.41
251 ratings
🤖 Ai In Business📋 Corporate Case Studies🧠 Applied Machine Learning
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
💻 R Language🤖 Machine Learning📈 Predictive Modeling
Machine Learning with R Summary
Machine Learning with R 87%
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
💻 R Language🤖 Machine Learning📈 Predictive Modeling
Everything Is Miscellaneous
by David Weinberger • 2007
Atoms forced one shelf per book. Bits let every fact live in many places at once.
3.74
2k+
📐 Information Architecture🏷️ Folksonomies🗂️ Classification Systems
Everything Is Miscellaneous Summary
Everything Is Miscellaneous 87%
Atoms forced one shelf per book. Bits let every fact live in many places at once.
by David Weinberger 2007
3.74
2k+ ratings
📐 Information Architecture🏷️ Folksonomies🗂️ Classification Systems
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
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Python Machine Learning Case Studies Summary
Python Machine Learning Case Studies 87%
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
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Superagency
by Reid Hoffman • 2025
AI multiplies human agency. That power will concentrate or spread. The choice is ours.
3.33
1k+
🏛️ Ai Policy🌅 Tech Optimism⚖️ Ai Ethics
Superagency Summary
Superagency 87%
AI multiplies human agency. That power will concentrate or spread. The choice is ours.
by Reid Hoffman 2025
3.33
1k+ ratings
🏛️ Ai Policy🌅 Tech Optimism⚖️ Ai Ethics
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+
💰 Data As Asset📈 Predictive Analytics🧹 Imperfect Data
Big Data Summary
Big Data 87%
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
💰 Data As Asset📈 Predictive Analytics🧹 Imperfect Data
Python Data Science Handbook
by Jake VanderPlas • 2016
Four libraries, one workflow: the Python path from raw data to validated predictions.
4.29
676
📊 Data Science🤖 Machine Learning🐍 Scientific Python
Python Data Science Handbook Summary
Python Data Science Handbook 87%
Four libraries, one workflow: the Python path from raw data to validated predictions.
by Jake VanderPlas 2016
4.29
676 ratings
📊 Data Science🤖 Machine Learning🐍 Scientific Python
Neural Networks, Fuzzy Logic And Genetic Algorithms
by S. Rajasekaran • 2004
Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.
4.21
151
🖥️ Soft Computing🧠 Neural Networks🌫️ Fuzzy Logic
Neural Networks, Fuzzy Logic And Genetic Algorithms Summary
Neural Networks, Fuzzy Logic And Genetic Algorithms 87%
Soft computing's three pillars each have blind spots. This is how they fill each other's gaps.
by S. Rajasekaran 2004
4.21
151 ratings
🖥️ Soft Computing🧠 Neural Networks🌫️ Fuzzy Logic
Practicing Trustworthy Machine Learning
by Yada Pruksachatkun • 2023
Privacy, fairness, explainability, robustness: the engineering guide for ML models that earn trust.
4.95
111
🤖 Responsible Ai⚙️ Ml Engineering⚖️ Model Fairness
Practicing Trustworthy Machine Learning Summary
Practicing Trustworthy Machine Learning 87%
Privacy, fairness, explainability, robustness: the engineering guide for ML models that earn trust.
by Yada Pruksachatkun 2023
4.95
111 ratings
🤖 Responsible Ai⚙️ Ml Engineering⚖️ Model Fairness
Software Requirements
by Karl Wiegers • 1999
Nearly half of all software defects trace back to bad requirements. The fix starts here.
4.14
859
🏗️ Requirements Engineering👤 User-Centered Design✅ Software Quality
Software Requirements Summary
Software Requirements 87%
Nearly half of all software defects trace back to bad requirements. The fix starts here.
by Karl Wiegers 1999
4.14
859 ratings
🏗️ Requirements Engineering👤 User-Centered Design✅ Software Quality
AI and Machine Learning for On-Device Development
by Laurence Moroney • 2021
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
4.17
6
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
AI and Machine Learning for On-Device Development Summary
AI and Machine Learning for On-Device Development 87%
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
by Laurence Moroney 2021
4.17
6 ratings
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
Working in Public
by Nadia Eghbal • 2020
Open source runs on solo maintainers, not communities. The scarcest resource: attention, not code.
3.92
951
💻 Open Source Software💰 Creator Economy👁️ Attention Economics
Working in Public Summary
Working in Public 87%
Open source runs on solo maintainers, not communities. The scarcest resource: attention, not code.
by Nadia Eghbal 2020
3.92
951 ratings
💻 Open Source Software💰 Creator Economy👁️ Attention Economics
Tribe of Hackers
by Marcus J. Carey • 2019
The cybersecurity insight too many ignore: secure your people, not just your network.
3.90
262
👤 Human Element🔐 Security Fundamentals🌐 Community Building
Tribe of Hackers Summary
Tribe of Hackers 87%
The cybersecurity insight too many ignore: secure your people, not just your network.
by Marcus J. Carey 2019
3.90
262 ratings
👤 Human Element🔐 Security Fundamentals🌐 Community Building
Designing Machine Learning Systems
by Chip Huyen • 2022
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
4.45
1k+
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
Designing Machine Learning Systems Summary
Designing Machine Learning Systems 87%
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
by Chip Huyen 2022
4.45
1k+ ratings
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
Program or Be Programmed
by Douglas Rushkoff • 2010
Digital tools have biases. If you can't read their code, someone else writes your behavior.
3.72
2k+
🌐 Media Ecology⚖️ Technological Bias🎮 Digital Agency
Program or Be Programmed Summary
Program or Be Programmed 87%
Digital tools have biases. If you can't read their code, someone else writes your behavior.
by Douglas Rushkoff 2010
3.72
2k+ ratings
🌐 Media Ecology⚖️ Technological Bias🎮 Digital Agency
Algorithms
by Panos Louridas • 2020
Twenty steps to find one item in a million, and the hidden logic that runs our world.
4.03
302
📐 Algorithm Design💻 Computing Primers🕸️ Graph Theory
Algorithms Summary
Algorithms 87%
Twenty steps to find one item in a million, and the hidden logic that runs our world.
by Panos Louridas 2020
4.03
302 ratings
📐 Algorithm Design💻 Computing Primers🕸️ Graph Theory
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
🤖 Machine Learning📊 Statistical Learning🏷️ Supervised Learning
Introduction To Machine Learning Summary
Introduction To Machine Learning 87%
The mathematical core of machine learning, taught without cutting corners: from Bayes to boosting.
by Ethem Alpaydin 2004
3.78
251 ratings
🤖 Machine Learning📊 Statistical Learning🏷️ Supervised Learning
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⭐
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We have a special gift for you
Open
38% OFF
DISCOUNT FOR YOU
⭐
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⭐
$79.99
$49.99/year
only $4.16 per month
Continue
2 taps to start, super easy to cancel