Machine Learning & Deep Learning Courses

A model is only as good as the data it's trained on. You learn the process from raw data through cleaning and feature selection to training and evaluation with Pandas, NumPy and scikit-learn: how to split data into training and test sets, choose the right model type for the task (classification, regression, clustering) and measure whether the model actually generalizes to new data. Along the way, you learn to recognize the classic pitfalls, with overfitting as the most important: the model that memorizes the training data instead of learning the pattern.

Neural networks are made up of layers of artificial neurons that together can learn patterns that classical models miss. You build and train your own networks with TensorFlow/Keras or PyTorch and get a solid grasp of the core concepts: layers, activation functions, backpropagation and optimization. You also gain insight into the most important architectures, from convolutional networks for image analysis to the transformer architecture that has made modern language models possible.

You learn to combine multimodal generative AI models, computer vision, and tools such as Azure AI Content Understanding to extract structured information from images, scanned documents, and video. These are the kinds of tasks where pretrained services and your own models meet: you learn to assess when an off-the-shelf service is enough and when the task requires a model trained on your own data.

Large language models are deep learning at scale, and the principles of training, data and evaluation are the same whether the model classifies customer inquiries or generates text. With a solid ML foundation, you can see through what generative models actually do, fine-tune them on your own data and evaluate them systematically instead of going by gut feeling. If you want to build on top of ready-made models rather than train them, that is the subject of LLM Integration & RAG.

A model that works in a notebook is not the same as a model in production. You'll learn how machine learning models and generative AI solutions are deployed, monitored and optimized with Azure Machine Learning and Microsoft Foundry: versioning of data and models, automated pipelines with GitHub Actions, infrastructure-as-code with Bicep, and monitoring of models whose accuracy degrades as the real world drifts away from the training data.