Learn Machine Learning Product Management from Cats and Dogs

Explore the basic premise of machine learning using a training data set to predict new data with various algorithms, illustrated with a dog and cat dataset.

Explore the three major types of machine learning: supervised learning, reinforcement learning, and unsupervised learning, and see how each informs product management decisions.

Explore semi-supervised learning with a dataset that mixes labeled dogs and cats with unlabeled parrots, illustrating how labeled and unlabeled data work together. Discover the basics of reinforcement learning.

Explore reinforcement learning as a robot learns from penalties and rewards to hug or avoid cats and dogs, then examine overfitting and underfitting in evaluating models.

Define your business problem, determine if machine learning is the right approach with your data scientists and engineers, choose an algorithm, and explore the data science loop.

Map the stages of the data science loop for your product and share the outline in the course discussion thread, noting intellectual property considerations and using non-sensitive examples if needed.

Explore the data science loop from data fetch to model deployment, including cleaning, feature selection, training, evaluation, and iteration to boost accuracy and monitor performance.

Explore how machine learning engineers, statisticians, and data analysts differ and overlap, highlighting their languages (Python, R, SQL), and key metrics like mean squared error and AUC.

Explore the top 10 algorithms you may encounter, including naive Bayes, k-means, logistic regression, deep learning, and more, and classify whether each is supervised, unsupervised, or reinforcement learning.

Explore linear regression with a y = x plus b model, using beta coefficients and intercept to predict dog likelihood from previous dogs, socioeconomic status, and cat clicks.

Explore classification success metrics, including accuracy, log loss, and the confusion matrix, and learn how precision, recall, and F1 shape model decisions with dog and cat examples.

Examine regression metrics like R squared, mean absolute error (MAE), and mean squared error (MSE), and link model accuracy to product goals through adoption, engagement, retention, and usability metrics.

Explore ROC curves, plotting true positive rate, true negative rate, and false positive rate, with AUC as an accuracy metric; seek curves toward the upper left and retrain as needed.

Post the machine learning project's success metrics from the course, avoiding confidential projects, and preview deep learning with convolutional neural networks to recognize images of cats and dogs.

Introduction to convolutional neural networks and deep learning. See how CNNs process images in layers to identify features, and consider computational costs and data needs, with cats and dogs.

Evaluate user experience issues in ml product design by comparing alternative ux designs for the same feature and balancing usability with accuracy.

Post user experience considerations for your ml product on the discussion board, and learn how customer feedback and a customer-centric approach inform user research.

Design mock prototypes of a ML-powered portfolio tool to test user experience and perceived value before building the algorithm, using inputs like risk tolerance, age, and starting assets.

Align user experience, research, product management, data science, and engineering from inception to build features users want and can feasibly implement, with regular cross-disciplinary meetings.

Design a three-month research roadmap for your machine learning product and post it on classroom discussion board, focusing on quarter; avoid confidential material and use an alternative idea if needed.

Evaluate whether to use machine learning by weighing simple rule alternatives, data access, and transparency for compliance. Use this stance to shape your ML product roadmap.

Explore a practical ML product management checklist that guides data collection, model accuracy, risk assessment, tradeoffs, and user experience design to drive responsible, data-driven ML projects.

Define the business problem with customer research and validate needs. Assess risks, data availability, training data, and algorithm costs to shape the ML product roadmap.