Master machine learning basics without spending a dime. High-quality free courses from Google, Kaggle, StatQuest, and top educators. Perfect for beginners starting their ML journey.
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Basic algebra and statistics helpful but not required
Any programming experience; Python preferred
Certified AI Professional (CAIP)
IntermediateAWS Academy Machine Learning Foundations
IntermediateHow to train your first machine learning model and run it inside your iOS app via CoreML
IntermediateArtificial Intelligence in Psychology Advance course
IntermediatePoint Cloud Processing Online Course
IntermediateIntroduction to Stochastic Optimization for Large-scale Machine Learning
IntermediateMachine Learning at Scale
IntermediateAdvanced Machine Learning: Optimization
IntermediateOptimization for Machine Learning
IntermediateAdvanced Optimization and Randomized Methods
AdvancedArtificial Intelligence in Real Estate
BeginnerSDSC6015 Stochastic Optimization for Machine Learning
IntermediateDuke University Introduction to Machine Learning
BeginnerPrecision Agriculture with AI and Machine Learning
IntermediateArtificial Intelligence Foundations: Machine Learning
BeginnerMachine Learning and AI Foundations: Value Estimations
IntermediateMachine Learning Engineering for Production (MLOps)
AdvancedAWS Certified Machine Learning - Specialty
AdvancedAI and Machine Learning Bootcamp
IntermediateCertified AI Professional (CAIP)
IntermediateAWS Academy Machine Learning Foundations
IntermediateHow to train your first machine learning model and run it inside your iOS app via CoreML
IntermediateArtificial Intelligence in Psychology Advance course
IntermediatePoint Cloud Processing Online Course
IntermediateIntroduction to Stochastic Optimization for Large-scale Machine Learning
IntermediateMachine Learning at Scale
IntermediateAdvanced Machine Learning: Optimization
IntermediateOptimization for Machine Learning
IntermediateAdvanced Optimization and Randomized Methods
AdvancedArtificial Intelligence in Real Estate
BeginnerSDSC6015 Stochastic Optimization for Machine Learning
IntermediateDuke University Introduction to Machine Learning
BeginnerPrecision Agriculture with AI and Machine Learning
IntermediateArtificial Intelligence Foundations: Machine Learning
BeginnerMachine Learning and AI Foundations: Value Estimations
IntermediateMachine Learning Engineering for Production (MLOps)
AdvancedAWS Certified Machine Learning - Specialty
AdvancedAI and Machine Learning Bootcamp
IntermediateFollow these courses in order to complete the learning path. Click on any course to enroll.
This certification provides a comprehensive understanding of machine learning, deep learning, and AI ethics. It is designed for professionals interested in the intersection of AI and blockchain, covering AI integration in business processes, data analytics, and automation with hands-on labs.
An introductory course to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). Students will be able to select and apply ML services to resolve business problems and will be able to label, build, train, and deploy a custom ML model.
A guide for developers on how to train a machine learning model and deploy it on-device in an iOS app using Core ML. The tutorial covers the entire process from data collection to model execution in Xcode.
An accredited course designed by mental health professionals that provides in-depth and updated insights into the integration of AI in psychology. It covers the contributions of AI to mental health care, ethics, machine learning in psychology, and neuropsychology and AI integration.
This course teaches you to create automated systems for 3D point clouds. It covers several segmentation workflows, feature engineering, and applying machine learning classifiers for point-based or object-based classification.
This video course introduces state-of-the-art first and second-order stochastic gradient methods for solving large-scale optimization problems and reviews their theoretical background on convergence rate analysis.
This course covers the principles required to develop scalable machine learning pipelines, including a section on Recommendation Systems at Scale which discusses graph-networks, link analysis, collaborative filtering, and challenges of sparsity and scalability.
A lecture focusing on the role of optimization in machine learning, covering various algorithms and their properties.
A graduate-level course covering the theory and algorithms for optimization in machine learning. It explores topics like convex and non-convex optimization, gradient-based methods, and stochastic optimization.
A graduate-level course on advanced optimization techniques and randomized methods for machine learning.
This course provides an introduction to the foundations of data, strategy, and analytical tools like AI and Machine Learning as they apply to real estate. It includes lessons on machine learning, guest lectures from Columbia University professors, and a group project to design a real-world AI application.
This course introduces the theoretical foundations and algorithmic developments in stochastic optimization for machine learning. It covers basic convex optimization theories and focuses on stochastic approximation and its accelerations in statistical and machine learning models.
Duke University Introduction to Machine Learning
Precision Agriculture with AI and Machine Learning
Artificial Intelligence Foundations: Machine Learning
Machine Learning and AI Foundations: Value Estimations
Machine Learning Engineering for Production (ML Ops)
AWS Certified Machine Learning - Specialty
Explore related content to expand your skills beyond this learning path.
Master machine learning basics without spending a dime. High-quality free courses from Google, Kaggle, StatQuest, and top educators. Perfect for beginners starting their ML journey.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Build and train machine learning models from scratch; Understand supervised, unsupervised, and reinforcement learning paradigms; Evaluate model performance using industry-standard metrics.
About 638 hours of study across 19 courses — and you can go at your own pace.
It's pitched at beginner level — a solid starting point if you're new to the topic.
19 curated courses, sequenced from foundational to advanced.
The courses in this path can be started for free.
Enroll in this path to track your progress and stay motivated.