Machine Learning Statistical Foundations Professional Certificate by Wolfram Research

Overview

Dive into the foundations of machine learning (ML) with this comprehensive curriculum. Gain a deep understanding of AI and ML principles, enhancing your ability to design and implement advanced algorithms.

Explore core mathematical concepts, the ML life cycle, and practical applications using Wolfram Language. Master the building blocks of ML, from linear algebra to statistics.

Tune in, pass the final exam, and earn your certificate. This certificate is endorsed by Wolfram Research, pioneers of computational intelligence and scientific innovation.  Explore WolframU for open, interactive courses, learning events and other educational resources for professional and technical development.Explore the machine learning life cycle and core methods.Analyze linear algebra concepts for ML algorithms.Discover calculus foundations for ML implementation.Practice ML tasks using Wolfram Language functions.

Syllabus

• Introduction to Machine Learning
Definition and scope of ML in AI
Overview of the machine learning life cycle
• Core Mathematical Concepts
Linear Algebra for ML
Vectors, matrices, and operations
Eigenvalues and eigenvectors
Singular value decomposition
Calculus Foundations
Derivatives and integrals in ML
Chain rule and optimization applications
Probability and Statistics
Probability distributions and sampling
Hypothesis testing and confidence intervals
Bayesian inference
• Machine Learning Algorithms
Supervised Learning
Linear regression and classification
Decision trees and random forests
Support vector machines
Unsupervised Learning
K-means clustering
Principal component analysis
Association rule learning
Reinforcement Learning Introduction
• ML Implementation with Wolfram Language
Wolfram Language basics for ML
Utilizing built-in ML functions
Case studies of ML applications using Wolfram Language
• Practical Applications and Exercises
Real-world ML problem solving
Hands-on coding exercises in Wolfram Language
Projects: Designing and evaluating custom ML models
• Advanced Topics
Neural Networks and Deep Learning
Ensemble methods and boosting techniques
Hyperparameter tuning and model validation
• Final Exam Preparation
Review of key concepts and methods
Sample questions and mock exams
• Certificate Completion
Exam details and requirements for certification
Next steps in professional and technical development
• Additional Resources
Access to WolframU for further learning
Workshops, webinars, and community forums

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Taught by

Kesha Williams, Terezija Semenski, MSc and Wolfram Research

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Subjects

Computer Science