Radix
×

Machine Learning

curated by Camillo

Machine Learning

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." - Tom Mitchel

It comes from

↑ Artificial Intelligence

includes the learning paths

- Python

- Calculus

and can be specialized into

↓ Artificial Neural Networks

↓ Supervised Learning

↓ Unsupervised Learning

↓ Reinforcement Learning

and can be followed into

↓ Data Science

Related Learning Paths

  • Data Mining
  • Basic concepts

    Basic concepts

    Play around with

    Apply machine learning with

    Python

    Get more information

    Learn more basics

    Apply learned concepts

    Get more in-depth infos on

    Learn new concepts

    Apply concepts with

    Have a basic knowledge of

    Calculus

    Get expert information

    Learn new concepts

    Shared information

    Apply your skills

    Some fun



    Follow-up paths

    Data Science

  • Dataiku Web Application

    Collaborative data science platform, explore, prototype, build data products

    Upvote

  • PyTorch Framework

    Open source deep learning platform that provides a seamless path from research prototyping to production deployment

    Upvote

  • TensorFlow Framework

    Open-source, numerical computation

    Upvote

  • Machine Learning Recipes with Josh Gordon Playlist

    Explanations and guidance on basic ML programming, SciKit, Weka, TensorFlow

    Upvote

  • Machine Learning with Python Playlist

    High-level intuition, understanding of algorithms, regression, classification, neural networks,scikit-learn, tensorflow

    Upvote

  • Machine Learning Video Lecture

    Mathematical discussion, best practices in machine learning

    Upvote

  • Machine learning problem framing Lessons

    Common ML terms, application of ML, problem solving with ML, programming methods, hypothesis testing

    Upvote

  • Maschinelles Lernen Video Lecture

    Methods of machine learning, fields of application and tools

    Upvote

  • Machine Learning Clustering

    Applications of clustering, data preparation, similarity measures, k-means algorithm, quality of clustering

    Upvote

  • Testing and Debugging in Machine Learning Lessons

    Validate raw feature data and engineered feature data. Debug a ML model to make the model work. Implement tests that simplify debugging. Optimize a working ML model. Monitor model metrics during development, launch, and production.

    Upvote

  • Data Preparation and Feature Engineering in ML Lessons

    Data size and quality, data collection and transformation

    Upvote

  • Mathematics for Machine Learning Specialization

    Linear algebra, multivariate calculus, principal component analysis

    Upvote

  • Chris Albon

    Code snippets on data manipulation, preprocessing, machine Learning, deep learning, statistics

    Upvote

  • Lex Fridman Research Scientist

    Human-centered AI and autonomous vehicles

    Upvote

  • Bernhard Sch?lkopf Professor

    Study of kernel methods for extracting regularities from possibly high-dimensional data

    Upvote

  • Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

    Concepts, algorithm and applications with Scikit-Learn, Keras, and TensorFlow

    Upvote

  • Machine Learning Yearning E-Book

    Technical strategy for technical engineers, machine Learning in Production and in a team

    Upvote

  • An Introduction to Statistical Learning Book

    Modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering

    Upvote

  • Python Data Science Handbook E-Book

    Introduction to Numpy, Data Manipulation with Pandas, Visualization with Matplotlib, Machine Learning with Scikit-Learn

    Upvote

  • Python Machine Learning Non-fictional

    Upvote

  • Scikit-Learn Software libray

    Classification, regression, clustering, dimensionality reduction, model selection, preprocessing, built on NumPy, SciPy, and matplotlib

    Upvote

  • Gradient Descent Method

    Iterative algorithm to find a min / max of a function, optimization strategy, iteratively moving in the direction of steepest descent

    Upvote

  • Loss Function Method

    A function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function

    Upvote

  • Outline of machine learning Outline

    Overview of and topical guide to machine learning

    Upvote

  • Machine Learning Yearning E-Book

    Technical strategy for technical engineers, machine Learning in Production and in a team

    Upvote

  • Overfitting

    Fitting data too closely or exactly to a particular dataset, and may therefore fail to fit unseen data

    Upvote

  • Statistical classification

    The problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known.

    Upvote

  • Regression analysis Method

    The set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables

    Upvote

  • Bias-variance tradeoff

    The conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set

    Upvote

  • Clustering

    The task of partitioning the dataset into groups, called clusters the goal is to split up the data ins such a way that points within a single cluster are very similar and points in a different cluster are different

    Upvote

  • Goodhart's law Quote

    "When a measure becomes a target, it ceases to be a good measure."

    Upvote

  • Curse of dimensionality

    Phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings

    Upvote

  • colah's blog Articles

    Research insights

    Upvote

  • Sebastian Ruder Articles

    Discussion of advanced topics and concepts

    Upvote

  • when trees fall Columns

    Upvote

  • Feature extraction

    Facilitates subsequent learning and generalization steps, and in some cases leading to better human interpretations

    Upvote

  • Machine Learning Video Lecture

    Mathematical discussion, best practices in machine learning

    Upvote

  • Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

    Concepts, algorithm and applications with Scikit-Learn, Keras, and TensorFlow

    Upvote

  • Machine learning problem framing Lessons

    Common ML terms, application of ML, problem solving with ML, programming methods, hypothesis testing

    Upvote

  • An Introduction to Statistical Learning Book

    Modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering

    Upvote

  • Maschinelles Lernen Video Lecture

    Methods of machine learning, fields of application and tools

    Upvote

  • Python Data Science Handbook E-Book

    Introduction to Numpy, Data Manipulation with Pandas, Visualization with Matplotlib, Machine Learning with Scikit-Learn

    Upvote

  • Machine Learning Clustering

    Applications of clustering, data preparation, similarity measures, k-means algorithm, quality of clustering

    Upvote

  • Testing and Debugging in Machine Learning Lessons

    Validate raw feature data and engineered feature data. Debug a ML model to make the model work. Implement tests that simplify debugging. Optimize a working ML model. Monitor model metrics during development, launch, and production.

    Upvote

  • Data Preparation and Feature Engineering in ML Lessons

    Data size and quality, data collection and transformation

    Upvote

  • Machine Learning Recipes with Josh Gordon Playlist

    Explanations and guidance on basic ML programming, SciKit, Weka, TensorFlow

    Upvote

  • Adversarial machine learning

    Spam filtering, malware detection and biometric recognition

    Upvote

  • Collaborative Filtering Technology

    Machine learning, recommendation system

    Upvote

  • Machine Learning with Python Playlist

    High-level intuition, understanding of algorithms, regression, classification, neural networks,scikit-learn, tensorflow

    Upvote

  • Mathematics for Machine Learning Specialization

    Linear algebra, multivariate calculus, principal component analysis

    Upvote

  • Python Machine Learning Non-fictional

    Upvote

  • Lex Fridman

    Human-centered AI and autonomous vehicles

    Upvote

  • Bernhard Sch?lkopf

    Study of kernel methods for extracting regularities from possibly high-dimensional data

    Upvote

  • 0% Module 1
    0% Module 2
    0% Module 3
    0% Module 4

    Information

    Education

    Exchange

    Application

    Contact PersonCamillo
    is curator for
    Machine Learning