🔧 Feature Engineering

Technical guides to the art and science of transforming raw data into meaningful inputs for machine learning models—the craft that often separates good predictions from great ones.

Data Science for Business

by Foster Provost • 2013

Why overfitting sinks most data projects, and the conceptual tools that stop it.

4.13

3k+

📊 Business Analytics ⛏️ Data Mining 📏 Model Evaluation 🧹 Data Preparation 🔮 Predictive Modeling

Data Science for Business

Why overfitting sinks most data projects, and the conceptual tools that stop it.

by Foster Provost 2013

4.13

3k+ ratings

📊 Business Analytics ⛏️ Data Mining 📏 Model Evaluation 🧹 Data Preparation 🔮 Predictive Modeling

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 🧹 Data Preprocessing ✅ Model Validation

Python Data Science Handbook

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 🧹 Data Preprocessing ✅ Model Validation

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 📈 Statistical Learning 🏫 Supervised Learning

The Hundred-Page Machine Learning Book

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 📈 Statistical Learning 🏫 Supervised Learning

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

📈 Time Series Analysis 🧹 Data Preparation ⚙️ Machine Learning Pipelines 📊 Statistical Learning 🧮 Practical Statistics

Practical Time Series Analysis

When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.

by Aileen Nielsen 2019

3.77

64 ratings

📈 Time Series Analysis 🧹 Data Preparation ⚙️ Machine Learning Pipelines 📊 Statistical Learning 🧮 Practical Statistics

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

🤖 Applied Machine Learning 📊 Data Science 🏷️ Supervised Learning 🐍 Scikit-Learn 🌲 Ensemble Learning

Introduction to Machine Learning with Python

Build models that generalize, not just memorize: a Python data scientist's guide.

by Andreas C. Müller 2015

4.33

600 ratings

🤖 Applied Machine Learning 📊 Data Science 🏷️ Supervised Learning 🐍 Scikit-Learn 🌲 Ensemble Learning

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

📊 Predictive Analytics 💼 Business Analytics 🤖 Supervised Learning 📈 Model Evaluation 🧹 Data Preparation

Fundamentals of Machine Learning for Predictive Data Analytics

Pick the right machine learning approach for any business problem, guided by real case studies.

by John D. Kelleher 2015

4.35

105 ratings

📊 Predictive Analytics 💼 Business Analytics 🤖 Supervised Learning 📈 Model Evaluation 🧹 Data Preparation

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

📊 Data Science 📈 Statistical Thinking 🧹 Data Wrangling 🔗 Causal Inference 🗄️ Big Data

Doing Data Science

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

📊 Data Science 📈 Statistical Thinking 🧹 Data Wrangling 🔗 Causal Inference 🗄️ Big Data

Low-Code AI

by Gwendolyn Stripling • 2023

Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.

4.33

🛠️ Practical Ml 📊 Data Exploration 🔍 Model Interpretability 🧩 Low-Code Platforms 📈 Regression Analysis

Low-Code AI

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 🔍 Model Interpretability 🧩 Low-Code Platforms 📈 Regression Analysis

Java Deep Learning Essentials

by Yusuke Sugomori • 2016

Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.

3.20

🧠 Deep Learning ☕ Java Machine Learning 🔧 Practical Deep Learning 👁️ Computer Vision 🗣️ Natural Language Processing

Java Deep Learning Essentials

Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.

by Yusuke Sugomori 2016

3.20

5 ratings

🧠 Deep Learning ☕ Java Machine Learning 🔧 Practical Deep Learning 👁️ Computer Vision 🗣️ Natural Language Processing

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

📊 Data Science 🤖 Machine Learning 🔍 Exploratory Data Analysis 📐 Model Evaluation 📈 Time Series Forecasting

Python Machine Learning Case Studies

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 📐 Model Evaluation 📈 Time Series Forecasting

Machine Learning

by Dr Ruchi Doshi • 2021

From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.

5.00

🧠 Machine Learning 📊 Supervised Learning 🧬 Deep Learning 🕹️ Reinforcement Learning 🎯 Recommendation Systems

Machine Learning

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 🕹️ Reinforcement Learning 🎯 Recommendation Systems

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