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AI & ML
Machine learning fundamentals, neural networks, model training, and production ML system design.
1Roadmaps
3Notes
- beginner01
Mathematics for ML
Build strong foundations in linear algebra, calculus, probability, and statistics essential for understanding ML algorithms.
Linear AlgebraCalculusProbability & StatisticsOptimization - beginner02
Python for Data Science
Master Python libraries for data manipulation, visualization, and scientific computing.
NumPyPandasMatplotlibJupyter NotebooksResources
- intermediate03
Classical Machine Learning
Learn supervised and unsupervised algorithms: regression, classification, clustering, and ensemble methods.
Linear RegressionDecision TreesSVMK-Means ClusteringResources
- advanced04
Deep Learning
Understand neural network architectures, backpropagation, CNNs, RNNs, and transformers using PyTorch or TensorFlow.
Neural NetworksCNNsRNNs & LSTMsTransformers - advanced05
MLOps & Production ML
Deploy ML models to production. Model versioning, monitoring, feature stores, and ML pipeline orchestration.
MLflowModel ServingFeature StoresA/B TestingResources