Basic concepts

Basic concepts

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- Outline of machine learning
- Machine learning problem framing
- Teachable Machine

- Overfitting
- Machine Learning Clustering
- Scikit-Learn

- Gradient Descent
- Machine Learning
- PyTorch

- Machine Learning Yearning
- An Introduction to Statistical Learning
- Kaggle
- Papers With Code

Basic concepts

Basic concepts

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Data Science

Create machine learning models, image recognition, motion detection, voice recognition

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Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

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Community of data scientists, machine learning competitions, datasets

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Collection of interactive machine learning examples on classification, unsupervised, recurrent nets, generative, basic ml, images & video, sounds & music, text & language

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Play around with different models in the browser, pros and cons of each, k-nearest Neighbor, perceptron, support vector machine, artificial neural network, decision tree

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Research insights

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Discussion of advanced topics and concepts

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

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

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Open-source, numerical computation

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

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

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Mathematical discussion, best practices in machine learning

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

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Methods of machine learning, fields of application and tools

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

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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.

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Data size and quality, data collection and transformation

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Linear algebra, multivariate calculus, principal component analysis

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

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Human-centered AI and autonomous vehicles

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

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

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

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

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

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

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

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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

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Overview of and topical guide to machine learning

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

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

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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.

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

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

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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

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

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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

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Research insights

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Discussion of advanced topics and concepts

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

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Mathematical discussion, best practices in machine learning

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

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*
*

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

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*
*

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*
*
*

Methods of machine learning, fields of application and tools

*Upvote*
*
*

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

*Upvote*
*
*

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

*Upvote*
*
*

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 size and quality, data collection and transformation

*Upvote*
*
*

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

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*
*

Spam filtering, malware detection and biometric recognition

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*

Machine learning, recommendation system

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*

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

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*
*

Linear algebra, multivariate calculus, principal component analysis

*Upvote*
*
*

*Upvote*
*
*

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

*Upvote*
*
*

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

*Upvote*
*
*

Create machine learning models, image recognition, motion detection, voice recognition

*Upvote*
*
*

Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

*Upvote*
*
*

Open-source, numerical computation

*Upvote*
*
*

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

*Upvote*
*
*

Collection of interactive machine learning examples on classification, unsupervised, recurrent nets, generative, basic ml, images & video, sounds & music, text & language

*Upvote*
*
*

Play around with different models in the browser, pros and cons of each, k-nearest Neighbor, perceptron, support vector machine, artificial neural network, decision tree

*Upvote*
*
*

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

*Upvote*
*
*

Human-centered AI and autonomous vehicles

*Upvote*
*
*

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

*Upvote*
*
*

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