Each course is a fully structured path: phases, modules, lessons, hands-on labs, and checkpoint quizzes, built end-to-end as one curriculum rather than a list of articles.
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Beginner
Machine Learning Fundamentals
A complete, self-paced path through classical machine learning: statistics and problem framing, data wrangling and feature engineering, regression, evaluation, classification with ensembles, unsupervised learning, model-quality pitfalls, and a taste of reinforcement learning — finishing with a hands-on capstone project.
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Machine Learning Fundamentals
A complete, self-paced path through classical machine learning: statistics and problem framing, data wrangling and feature engineering, regression, evaluation, classification with ensembles, unsupervised learning, model-quality pitfalls, and a taste of reinforcement learning — finishing with a hands-on capstone project.
Machine Learning Fundamentals
Phase 1: Foundations 0/5
Phase 2: Data Wrangling, Preprocessing & ML Workflow 0/9
Phase 3: Regression 0/9
Phase 4: Evaluation 0/8
Phase 5: Classification 0/15
Phase 6: Unsupervised Learning 0/4
Phase 7: Model Quality 0/8
Phase 8: Reinforcement Learning 0/3
Phase 9: Capstone 0/1
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Intermediate
Deep Learning
A hands-on, self-paced path through neural networks: start at a single neuron and backpropagation, work through optimization and regularization, build CNNs, RNNs, and attention/transformer architectures from scratch, and pick up the practical skills to train, debug, and avoid common pitfalls — finishing with a deep learning capstone project.
0 / 56 lessons complete
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Deep Learning
A hands-on, self-paced path through neural networks: start at a single neuron and backpropagation, work through optimization and regularization, build CNNs, RNNs, and attention/transformer architectures from scratch, and pick up the practical skills to train, debug, and avoid common pitfalls — finishing with a deep learning capstone project.