Advanced Certificate in ML Optimization Techniques: Performance Improvement
-- ViewingNowThe Advanced Certificate in ML Optimization Techniques: Performance Improvement is a comprehensive course designed to enhance your expertise in machine learning optimization. This certificate program focuses on imparting critical skills necessary to improve machine learning model performance, addressing the rising industry demand for proficient professionals in this area.
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⢠Advanced Optimization Algorithms: An in-depth study of advanced optimization techniques such as Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing.
⢠Hyperparameter Tuning in Machine Learning: Learn the art of selecting the optimal hyperparameters in ML models using techniques like Grid Search, Random Search, and Bayesian Optimization.
⢠Memory & Computation Efficiency: Techniques to reduce the memory footprint and computational requirements of ML models without compromising their performance.
⢠Automated Machine Learning (AutoML): Understand the tools and techniques used in AutoML for automating the end-to-end ML pipeline, including data pre-processing, feature engineering, model selection, and hyperparameter tuning.
⢠Model Compression: Learn about various model compression techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and knowledge distillation for deploying ML models on resource-constrained devices.
⢠Distributed Machine Learning: Techniques for scaling ML models to large datasets using distributed computing frameworks such as Apache Spark, Dask, and Horovod.
⢠Quantization & Binarization: Learn about quantization and binarization techniques for reducing the precision of weights and activations in deep neural networks, thereby reducing their memory requirements and computational complexity.
⢠Hardware Acceleration for ML: Understand how specialized hardware such as GPUs, TPUs, and FPGAs can be used to accelerate ML workloads, and learn about the software frameworks and libraries used for programming these devices.
⢠ML Optimization Benchmarking: Techniques for benchmarking and comparing the performance of different ML models, including metrics such as accuracy, F1 score, ROC-AUC, and precision-recall curves.
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