[Technical] AI Product Manager Explorer Certificate

Master Python for AI by exploring NumPy, pandas, TensorFlow, PyTorch, and scikit-learn, and setting up Jupyter and Visual Studio Code while practicing core syntax and data types.

Learn Python control flow with conditional statements, for and while loops, and break and continue, plus hands-on exercises on prime checking and a menu-driven calculator.

Explore Python functions and modules: define with def, pass parameters, return values; understand local/global scope and lifetime; import built-in modules, create custom modules, and build a basic math operations module.

Explore lists, tuples, dictionaries, and sets in Python, learning creation, access, and mutability; perform common operations like append, insert, remove, slicing, and set unions, intersections, and differences.

Explore string manipulation in Python, covering concatenation, slicing, and formatting with f-strings; use split, join, replace, and strip, and apply regex for pattern matching and text cleaning.

Master python file handling by reading and writing text files, using open modes (r, w, a, r+), and leveraging with statements for safe closure and exception handling.

Learn to write clean pythonic code and leverage built-ins, list comprehensions, lambdas, map, filter, and reduce, then build a practical command-line task manager with file-based persistence.

Explore machine learning basics and terminology, covering supervised, unsupervised, and reinforcement learning, and learn to define features, targets, train-test splits, and visualize data relationships.

Explore supervised learning fundamentals and regression analysis, including linear regression with labeled data, cost functions, gradient descent, and practical Python implementation with evaluation and visualization.

Explore polynomial regression to model non-linear relationships and master regularization techniques like ridge and lasso to prevent overfitting in Python.

Explore classification and logistic regression, covering binary, multi-class, and multi-label tasks, with sigmoid-based probability, decision boundaries, and evaluation metrics like accuracy, precision, recall, and F1 across real-world use cases.

Explore regression and classification evaluation metrics, including MSE, MAE, RMSE, accuracy, precision, recall, and F1, plus cross-validation and confusion matrices for robust model assessment.

Master the k-nearest neighbors algorithm, an instance-based method using distance metrics for classification and regression. Apply feature scaling and hands on iris dataset experiments to compare k values and performance.

Build and evaluate an end-to-end supervised learning pipeline, using regression and classification on California housing data and telco churn data, with EDA, preprocessing, model comparison, and reporting.

Transform raw data into meaningful inputs to boost model accuracy and interpretability through feature engineering, with a Titanic dataset hands-on exercise on categorical, numerical, and ordinal features and encoding.

Explore why scaling and normalization matter in machine learning and compare min max scaling and standardization on the iris dataset using scikit learn, observing effects on knn performance and training.

Explore one hot and label encoding for categorical variables, compare applications for nominal, ordinal, and high cardinality features, and test impacts on Titanic data with logistic regression.

Learn feature selection techniques to identify relevant features, reduce overfitting, and improve efficiency using filter, wrapper, and embedded methods with examples like correlation, mutual information, and random forest.

Create and transform features from date data to boost model performance, extracting day of week, month, and year, applying polynomial transformations, and comparing results on the bike sharing dataset.

Explore regression and classification evaluation metrics, including MAE, MSE, RMSE, R-squared, accuracy, precision, recall, F1, and ROC AUC, with hands-on exercises on iris and California housing datasets.

Learn cross validation techniques—k-fold, stratified k-fold, and leave-one-out—to assess model generalization. Apply feature engineering and hyperparameter tuning with grid search and random search on Titanic data for end-to-end model evaluation.

Explore how AI agents interact with environments via sensors and actuators, perceive, decide, act, and learn—demonstrating autonomy, proactivity, and social ability beyond traditional models.

Explore how AI agents combine autonomy, reactivity, and proactivity to act independently, adapt to real-time changes, and anticipate future goals in real world systems.

Discover four core AI agent types—reactive, deliberative, hybrid, and learning—and how each architecture shapes speed, planning, adaptability, and use cases.

Explore how machine learning powers AI agents through supervised learning, reinforcement learning, and imitation learning, with real-world applications in perception, control, and decision making.

Explore natural language processing for communication agents, covering intent detection, entity recognition, dialogue management, and language generation powered by transformer-based models.

Robotics integrates ai agents that bridge sensing and acting through the sensorimotor loop, enabling perception, planning, and safe, real-time decision making in the real world.

Explore three leading open source ai agent frameworks—Lang chain, Haystack, and Open agents—and learn how they handle memory, prompt templating, tool usage, and multi-step reasoning to accelerate ai agent development.

Learn how auto GPT enables autonomous goal planning, tool use, and self-looping agents that reason, store long-term memory with embeddings, and execute multi-step tasks toward a defined objective.

Explore the IBM bee framework for enterprise AI agents, a modular, governance-driven blueprint that enables auditable, secure, production-ready agents with planner and worker roles.

Explore Landgraf, a graph-based, stateful agent framework that adds memory and persistent state machines to agents, enabling multi-step reasoning with branching, retries, and long-term task management.

Explore Crew AI, a Python framework that turns agents into collaborative teams with roles, tools, and shared memory to tackle complex, multi-domain tasks through coordinated task flows.

Explore how AI agents transform business operations by automating tasks, managing workflows, and providing intelligent insights across departments, boosting speed and efficiency while keeping humans in the loop.

Explore how ai agents in healthcare augment clinicians—diagnostic support, clinical trial matching, patient engagement, and workflow automation—under strict privacy, compliance, and human oversight.

Explore how ai agents transform financial services with robo advisors, real-time fraud detection, and regulatory compliance, delivering scalable, auditable, explainable decision making across front, middle, and back offices.

Explore how AI agents transform entertainment with dynamic NPCs, storytelling companions, and creative co-pilots that react in real time, generate endless content, and enable interactive, immersive experiences.

AI agents power smart homes by sensing context, coordinating behaviors, and personalizing comfort, security, and energy use across IoT devices and ecosystems, while preserving privacy and trust.

Explore how future ai agents achieve lifelong autonomy through environment-driven learning, self-improvement, and memory architectures that support persistent, goal-driven cognition.

Explore fairness, explainability, and alignment in AI agents, detailing auditing, diverse human oversight, and safeguards embedded in data, models, interfaces, and goals to preserve trust.

Explore data rights, liability, and international regulation shaping AI agents, including consent, data use, and accountability across the stack for transparent governance.

Explore how AI agents transform work and society, driving productivity while reshaping jobs, equity, and policy, and learn to orchestrate human plus agent collaboration for inclusive prosperity.

Explore complementary intelligence where ai agents augment human judgment, creativity, and emotion. Design human in the loop with transparency and trust to amplify outcomes and guard against drift.

Explore how AI agents accelerate scientific discovery through simulation, lab automation, and hypothesis generation, enabling autonomous, interdisciplinary research with transparent, reproducible collaboration between humans and machines.

Explore how AI agents boost public safety and defense through real-time threat detection, disaster response, and autonomous drones, while emphasizing ethical governance, oversight, and accountability.

Explore what ai agents are, their autonomy, perception, decision making, and learning, with hands-on development using autogen, ibm, landgraf, crewai, and autogpt.

Explore Autogen, a multi-agent framework for agent communication, coordination, and tool use; build hands-on workflows with the user proxy and assistant agents, including code execution and human in the loop.

Explore the IBM Bee Agent framework to build modular, model-agnostic AI agents with tool integrations, memory strategies, sandboxed code execution, and observability for scalable workflows.

Explore LangGraph, a Lang Chain–based framework for building stateful multi-agent workflows with LLMs, featuring cyclic and branching processes, human-in-the-loop control, persistence, and real-time streaming across multi-agent hierarchies.

Explore Crew AI, a platform for building and deploying multi-agent AI systems with role-based agents, sequential, hierarchical, and consensual processes, memory, and tool integrations.

Explore auto GPT, an autonomous GPT-4 based framework that chains tasks, handles memory and file management, and supports multi-agent collaboration with plugins and API integrations. Learn practical steps to install, configure, and run auto GPT with defined goals.

Create and publish GPTs to the ChatGPT store by building sentiment analysis, image description, and destination knowledge base GPTs, manage accounts, sharing options, and publication workflow.

Create and publish GPTs that describe uploaded images, generate names and icons, set a profile image, and test them in the store to refine descriptions.

Create and publish a vacation planning GPT named Travel Architect by uploading a knowledge base, configuring data sources, and publishing to the ChatGPT Store, enabling destination recommendations within budget.

Earn your AI product manager explorer certificate and translate Python basics and machine learning fundamentals into leading AI powered product initiatives, collaborating with technical teams, and building GPT powered applications.