Robust Machine Learning Mastery
Total number of tutorials: 10
Learn how to build robust machine learning models that can handle uncertainty, noise, and outliers in 10 steps.
Quantum Machine Learning Fundamentals
Total number of tutorials: 10
Learn the basics of quantum machine learning, from quantum computing to quantum algorithms and applications.
Probabilistic Deep Learning Fundamentals
Total number of tutorials: 8
Learn the basics of probabilistic deep learning, from Bayesian inference to variational autoencoders, in this 8-part tutorial series.
Matlab for Machine Learning Essentials
Total number of tutorials: 8
Learn how to use Matlab for machine learning tasks such as data preprocessing, feature extraction, model training, evaluation and deployment.
Machine Learning with Golang
Total number of tutorials: 8
Learn how to use Golang for machine learning tasks, from data preprocessing to model deployment.
Machine Learning Pruning Techniques
Total number of tutorials: 8
Learn how to reduce the complexity and improve the performance of machine learning models using pruning techniques.
Machine Learning for Fraud Detection
Total number of tutorials: 10
Learn how to apply machine learning techniques to detect and prevent fraud in various domains such as banking, e-commerce, and healthcare.
Keras and TensorFlow Mastery
Total number of tutorials: 12
Master Keras and TensorFlow step by step: installation, building neural networks, image processing, NLP, audio, time series, GANs, RL, deployment, testing, and best practices.
F1 Machine Learning Essentials
Total number of tutorials: 8
Learn how to use F1 score as a metric for machine learning models, and how to optimize it using various techniques.
Embedded Machine Learning Essentials
Total number of tutorials: 10
Learn how to design, train and deploy machine learning models on embedded devices with this 10-step tutorial series.
Active Learning in Machine Learning
Total number of tutorials: 12
Learn how to use active learning to improve your machine learning models with less data and human effort.
PyTorch for Natural Language Processing
Total number of tutorials: 12
Learn how to use PyTorch for various NLP tasks such as text classification, sentiment analysis, machine translation, and more.
Predictive Maintenance with Machine Learning
Total number of tutorials: 12
Learn how to use machine learning to predict and prevent failures in complex systems with this tutorial series.
Machine Learning Evaluation Mastery
Total number of tutorials: 12
Dive into Machine Learning Evaluation Mastery: metrics, confusion matrix, ROC-AUC, regression metrics, clustering indices, cross-validation, and statistical tests.
Elasticsearch for Machine Learning Applications
Total number of tutorials: 10
Learn how to use Elasticsearch for ML applications, from data ingestion to analysis and visualization.
AWS AutoML: A Practical Guide
Total number of tutorials: 8
Learn how to use AWS AutoML to create, train and deploy machine learning models in the cloud with this practical guide.
Advances in Financial Machine Learning
Total number of tutorials: 12
Learn how to apply machine learning techniques to financial data analysis and trading strategies in 12 tutorials.
Deep Learning from Scratch Series
Total number of tutorials: 15
Learn the basics of deep learning from scratch with Python and TensorFlow. Build your own neural networks and apply them to real-world problems.
Machine Learning Uncertainty
Total number of tutorials: 12
Learn how to quantify and handle uncertainty in machine learning models with this 12-step tutorial series.