Explore the shift from traditional software to AI powered systems. See how machine learning learns patterns from thousands of spam and non-spam examples to predict outcomes and adapt.

Explore the ai technologies toolbox, from fuzzy logic and search algorithms to neural networks and clustering, and learn how diverse techniques combine to improve decision making and reliability.

Explore leading ai frameworks like TensorFlow, PyTorch, Keras, MXNet, and scikit-learn, and learn when to use each for data preparation, training, and deployment across cpus, gpus, and cloud.

Explore ai as a service (aiaas), delivering ready-to-use machine learning models over the internet to simplify data preparation, model training, and deployment. See ai services with pay-as-you-go pricing.

Explore reinforcement learning, where an agent learns by interacting with its environment through trial and error, driven by rewards and penalties, as seen in games, autonomous vehicles, and chatbots.

Train, evaluate, and tune machine learning models to improve accuracy on unseen data. Use training and validation sets, epochs, and hyperparameters to optimize performance and select the best model.

Test the model on unseen data to verify generalization, deploy it to real-world platforms like mobile apps, websites, or cloud servers, and monitor drift with A/B testing and retraining.

Master overfitting and underfitting by balancing model complexity to generalize beyond training data, with examples of memorization and simple models and techniques like cross validation and regularization.

Data preparation drives model performance, representing about 43% of ML effort; perform data acquisition, cleaning, structuring, and formatting, label supervised data, and handle diverse formats for accuracy and reliability.

Master feature engineering by feature selection, feature extraction, and exploratory data analysis (EDA) to boost model accuracy and efficiency before training.

Improve machine learning quality by ensuring properly labeled data, avoiding mislabels, biases, and security risks, and applying labeling methods like internal, outsourced, crowdsourced, AI-assisted, and hybrid, including Kaggle data.

Evaluate machine learning models using clustering metrics for unsupervised learning, including intra-cluster, inter-cluster, and silhouette scores, to measure cluster quality and guide metric selection based on the use case.

Explore neural network coverage measures as alternatives to traditional white box criteria, focusing on neuron activation, threshold coverage, and sign change coverage to improve testing and generalization.

Navigate the challenges of testing probabilistic and non-deterministic AI systems, covering variability, transparency, explainability, and test oracles with strategies like metamorphic testing and statistical methods.

Define and implement test objectives and acceptance criteria for AI systems, addressing adaptability, autonomy, transparency, bias, ethics, and safety to mitigate unique AI risks and ensure responsible performance.

Explore techniques for testing AI based systems, including back-to-back testing, A/B testing, metamorphic testing, adversarial testing, pairwise testing, and experience-based testing, to ensure robustness, fairness, and reliable neural network performance.