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Learn Regularization, earn certificates with paid and free online courses from Harvard, DeepLearning.AI, Alexander Amini, UC San Diego and other top universities around the world. Read reviews to decide if a class is right for you.
Explore advanced statistical concepts including cross-validation, regularization, and robust regression to enhance model fitting and selection techniques in data analysis.
Explore a novel algorithmic framework for discrepancy minimization, reinterpreting key breakthroughs and proving conjectures for pseudorandom instances through regularization techniques.
Learn to improve machine learning model performance on new data using regularization techniques like ridge and lasso, minimizing overfitting and understanding the bias-variance tradeoff.
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
Evaluate and compare predictive models using diagnostic techniques and performance measures. Learn to implement training/validation/test pipelines for robust model generalization.
Enhance deep learning skills: master hyperparameter tuning, regularization, optimization, and TensorFlow implementation for improved neural network performance and systematic results generation.
Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems using the Python toolbox scikit-learn.
Develop machine learning skills using Python, covering regression and classification techniques with hands-on practice in NumPy and scikit-learn for real-world AI applications.
Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
Complete course covering fundamentals of Machine learning , Deep learning with Python code
Image Recognition with Convolutional Neural Networks. Advanced techniques for Deep Learning and Representation learning
Learn what machine learning is all about in this beginner-level entry course
Learn to build regression, classification, and clustering models using various algorithms. Gain hands-on experience in model selection, evaluation, and tuning for supervised and unsupervised learning tasks.
This course will introduce students to the field by covering stateÂ-of-Âthe-art modeling, analysis and visualization techniques.
Comprehensive exploration of machine learning fundamentals, covering linear models, neural networks, SVMs, and key principles for effective algorithm implementation and evaluation.
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