Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera Alternatives

Similar projects and alternatives to Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera based on common topics and language

• Machine-Learning-Specialization-Coursera

  6 2,680 5.0 Jupyter Notebook Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera VS Machine-Learning-Specialization-Coursera

  Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

• imodels

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  Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Basic-Mathematics-for-Machine-Learning

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  The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

• ML-foundations

  1 2,945 5.1 Jupyter Notebook Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera VS ML-foundations

  Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

• Andrew-NG-Notes

  1 2,228 0.0 Jupyter Notebook Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera VS Andrew-NG-Notes

  This is Andrew NG Coursera Handwritten Notes.

• coursera-deep-learning-specialization

  112 2,679 4.7 Jupyter Notebook Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera VS coursera-deep-learning-specialization

  Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models