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  • Mathematics For Machine Learning

Mathematics for Machine Learning Courses

Mathematics for machine learning courses can help you learn linear algebra, calculus, probability, and statistics. You can build skills in optimization techniques, data analysis, and algorithm development. Many courses introduce tools like Python libraries such as NumPy and SciPy, that support implementing mathematical concepts in AI applications. You'll also explore key topics like regression analysis, dimensionality reduction, and neural networks, all of which are crucial for developing effective machine learning models.


Popular Mathematics for Machine Learning Courses and Certifications


  • A

    Amazon Web Services

    Machine Learning Essentials for Business and Technical Decision Makers

    Skills you'll gain: Machine Learning Methods, Decision Intelligence, Applied Machine Learning, Machine Learning, Technology Roadmaps, Data-Driven Decision-Making, Artificial Intelligence and Machine Learning (AI/ML), Business Solutions, Organizational Strategy, AI Product Strategy, Organizational Change, Feasibility Studies

    4.9
    Rating, 4.9 out of 5 stars
    ·
    14 reviews

    Beginner · Course · 1 - 4 Weeks

  • N

    New York Institute of Finance

    Using Machine Learning in Trading and Finance

    Skills you'll gain: Tensorflow, Keras (Neural Network Library), Financial Trading, Data Pipelines, Applied Machine Learning, Technical Analysis, Machine Learning, Securities Trading, Statistical Machine Learning, Model Training, Market Trend, Market Data, Data Preprocessing, Deep Learning, Python Programming, Feature Engineering, Model Evaluation, Correlation Analysis, Artificial Neural Networks, Performance Metric

    3.9
    Rating, 3.9 out of 5 stars
    ·
    379 reviews

    Intermediate · Course · 1 - 3 Months

  • N

    Northeastern University

    Statistical Learning for Engineering Part 1

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Statistical Modeling, Model Optimization, Machine Learning Algorithms, PyTorch (Machine Learning Library), Statistical Methods, Statistical Machine Learning, Machine Learning, Predictive Analytics, Predictive Modeling, Machine Learning Software, Statistical Programming, Model Training, Statistical Software, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning

    Intermediate · Course · 1 - 3 Months

  • D

    Dartmouth College

    Machine Learning with Neural Networks

    Skills you'll gain: Bayesian Network, Artificial Neural Networks, Machine Learning Methods, Convolutional Neural Networks, Deep Learning, Tensorflow, Model Training, Model Optimization, Machine Learning, Applied Machine Learning, Bayesian Statistics, Machine Learning Algorithms, Model Evaluation, Network Model, Network Architecture, Algorithms, Probability Distribution

    Build toward a degree

    Intermediate · Course · 1 - 3 Months

  • M

    Microsoft

    Create Machine Learning Models in Microsoft Azure

    Skills you'll gain: Unsupervised Learning, Scikit Learn (Machine Learning Library), PyTorch (Machine Learning Library), Convolutional Neural Networks, Exploratory Data Analysis, Deep Learning, Microsoft Azure, Data Visualization, Classification Algorithms, Applied Machine Learning, Regression Analysis, Predictive Modeling, Data Analysis, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Data Science, Model Training, Transfer Learning, Databricks, Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    331 reviews

    Intermediate · Course · 1 - 4 Weeks

  • J

    Johns Hopkins University

    Applied Machine Learning: Techniques and Applications

    Skills you'll gain: Computer Vision, Model Evaluation, Supervised Learning, Image Analysis, Data Preprocessing, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning Algorithms, Data Processing, Model Training, Model Optimization, Machine Learning, Data Cleansing, Scikit Learn (Machine Learning Library), Machine Learning Software, Data Integration, Data Transformation, Classification Algorithms, Dimensionality Reduction

    3.7
    Rating, 3.7 out of 5 stars
    ·
    12 reviews

    Intermediate · Course · 1 - 4 Weeks

  • W

    Whizlabs

    Fundamentals of Machine Learning

    Skills you'll gain: Supervised Learning, Unsupervised Learning, MLOps (Machine Learning Operations), Model Optimization, Applied Machine Learning, Machine Learning Methods, Model Evaluation, Model Training, Data Preprocessing, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Cloud Computing, Machine Learning Algorithms, Model Deployment, Microsoft Azure, Amazon Web Services, Cloud Solutions, Deep Learning, Data Processing

    Intermediate · Course · 1 - 3 Months

  • S

    Sungkyunkwan University

    Machine Learning Basics

    Skills you'll gain: Machine Learning Algorithms, Machine Learning Methods, Machine Learning, Python Programming, Supervised Learning, Model Evaluation, Scikit Learn (Machine Learning Library), Logistic Regression, Analysis, Applied Machine Learning, Regression Analysis, Model Training, Unsupervised Learning, Mathematics and Mathematical Modeling, Statistical Methods, Linear Algebra

    4.4
    Rating, 4.4 out of 5 stars
    ·
    93 reviews

    Mixed · Course · 1 - 4 Weeks

  • T

    The University of Chicago

    Machine Learning: Concepts and Applications

    Skills you'll gain: Keras (Neural Network Library), Machine Learning Methods, Unsupervised Learning, Machine Learning Algorithms, Deep Learning, Machine Learning, Statistical Machine Learning, Machine Learning Software, Decision Tree Learning, Applied Machine Learning, Scikit Learn (Machine Learning Library), Classification Algorithms, Supervised Learning, Regression Analysis, Artificial Intelligence and Machine Learning (AI/ML), Random Forest Algorithm, Model Training, Dimensionality Reduction, Model Evaluation, Data Preprocessing

    3.9
    Rating, 3.9 out of 5 stars
    ·
    25 reviews

    Intermediate · Course · 1 - 3 Months

  • N

    Northeastern University

    Statistical Learning for Engineering Part 2

    Skills you'll gain: PyTorch (Machine Learning Library), Applied Machine Learning, Unsupervised Learning, Reinforcement Learning, Supervised Learning, Machine Learning Algorithms, Machine Learning Methods, Dimensionality Reduction, Statistical Machine Learning, Machine Learning, Machine Learning Software, Convolutional Neural Networks, Artificial Neural Networks, Deep Learning, Decision Tree Learning, Autoencoders, Model Optimization

    Intermediate · Course · 1 - 3 Months

  • N

    Northeastern University

    Machine Learning with Small Data Part 1

    Skills you'll gain: Transfer Learning, Small Data, Deep Learning, Applied Machine Learning, Machine Learning Methods, Model Training, Unsupervised Learning, Machine Learning, Data Synthesis, Supervised Learning

    Mixed · Course · 1 - 3 Months

  • E

    Edureka

    Applied Machine Learning with Python

    Skills you'll gain: Model Evaluation, Supervised Learning, Machine Learning Methods, AI Personalization, Classification Algorithms, Machine Learning, Model Training, Logistic Regression, Model Optimization, Data Analysis, Driving engagement, Persona (User Experience)

    Intermediate · Course · 1 - 4 Weeks

1…678…834

In summary, here are 10 of our most popular mathematics for machine learning courses

  • Machine Learning Essentials for Business and Technical Decision Makers: Amazon Web Services
  • Using Machine Learning in Trading and Finance: New York Institute of Finance
  • Statistical Learning for Engineering Part 1: Northeastern University
  • Machine Learning with Neural Networks: Dartmouth College
  • Create Machine Learning Models in Microsoft Azure: Microsoft
  • Applied Machine Learning: Techniques and Applications: Johns Hopkins University
  • Fundamentals of Machine Learning: Whizlabs
  • Machine Learning Basics: Sungkyunkwan University
  • Machine Learning: Concepts and Applications: The University of Chicago
  • Statistical Learning for Engineering Part 2: Northeastern University

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