A Practical Introduction to Convnets for Image Classification
Practical guide to developing CNN-based image classification pipelines, covering dataset handling, architecture design, training, and evaluation. Explores real-world applications in robotics and biomedicine.
Machine Learning Without the Hype
Explore AI, machine learning, and deep learning concepts, comparing rule-based and ML approaches for time-series data. Learn supervised vs unsupervised learning with practical examples and best practices.
Adapting to Change - How Machine Intelligences Adapt to a Changing World
Explore how human and machine intelligences adapt to change, focusing on state-of-the-art techniques in machine learning and MLOps for effective adaptation in AI systems.
One Stop Anomaly Shop
Comprehensive machine learning framework for anomaly detection in datasets. Enables custom preprocessing, enhances accuracy on small datasets, and targets security threats efficiently.
Do You Live or Die - Explaining Machine Learning with Azure and the Titanic Dataset
Explore machine learning using Azure and the Titanic dataset, covering data preparation, model building, and evaluation for predictive analysis and security applications.
Data Science for Business - Repeatable and Objective Data Science Process
Explore the Team Data Science Process (TDSP) for efficient predictive analytics and AI integration in business processes, focusing on agile methodology and practical implementation strategies.
Self-Supervised Learning - Towards Autonomously Learning Machines
Explore cutting-edge AI techniques for autonomous learning machines, focusing on self-supervised learning methods that reduce reliance on labeled data and enhance general understanding.
Brave New World of Artificial Intelligence
Explore the future of AI, from its foundations to modern applications. Learn about machine learning, knowledge engineering, and real-world AI scenarios across various industries.
Deep Learning with DeepLearning4J and Spring Boot
Explore deep learning with DeepLearning4J in a Spring Boot application, covering neural networks, supervised learning, and custom actuator endpoints for advanced monitoring.
Machine Learning Exposed - The Fundamentals
Gentle introduction to machine learning fundamentals, covering supervised, unsupervised, and reinforcement learning, with practical examples and intuitive explanations of underlying mathematics.
Machine Learning for a Rescue
Explore machine learning applications in software development, focusing on data-driven approaches to classify developers and predict project outcomes.
Analysis of Artificial Intelligence in the Telco Sector
Explore AI's impact on telecom: from network performance monitoring to satellite communication challenges, with insights on machine learning applications and industry advancements.
Scikit-learn to "Learn Them All"
Overview of Scikit-learn for machine learning in Python, covering supervised and unsupervised techniques, with code examples and insights on implementation. Includes comparison to other ML libraries and scaling to big data.
Teaching Python
Effective strategies for teaching Python, including rapid feedback and supervised learning. Insights from OpenTechSchool's successful workshops for beginners and experienced programmers.
Using Scikit-Learn's Interface for Turning Spaghetti Data Science into Maintainable Software
Learn to structure data science code using Scikit-Learn's estimator and meta-estimator interfaces. Discover how to transform spaghetti code into maintainable, testable, and reusable machine learning models.
Is That Spam in My Ham?
Learn to build a basic spam classifier using Python and Naive Bayes. Explore key machine learning concepts, implement a model to detect spam emails, and understand how to measure and improve classification performance.
Recipe for Text Analysis in Social Media
Explore text analysis in social media using Naïve Bayes classifiers, sentiment analysis, and linguistic information. Learn about opinion lexicons and supervised vs unsupervised learning approaches.
Sound Event Detection with Machine Learning
Aprenda a implementar detección de eventos sonoros con aprendizaje automático, desde la recopilación de datos hasta el entrenamiento de redes neuronales y la evaluación del sistema, utilizando Python y bibliotecas populares.
Azure Machine Learning 101
Explore Azure ML's no-code approach to machine learning, from data preparation to model deployment, suitable for both beginners and experienced data scientists.
Kick-Start Your Understanding of Machine Learning with Python
Practical guide to building ML applications in Python, focusing on text classification using unsupervised and supervised learning techniques with visualization and deep learning examples.
Reinforcement Learning and Minecraft - ML Con Spring 2018
Explore reinforcement learning through Minecraft's AI platform, Project Malmo. Learn to solve problems using deep learning techniques in a unique experimental environment.