Complete Introduction to Artificial Intelligence course

Discover the workflow of an AI project, from identifying steps to selecting promising ideas, and learn to organize data and teams for solo or group AI ventures.

Discover the workflow of a machine learning project, from data collection to model training and deployment, with iterative feedback to improve performance, illustrated by speech recognition and self-driving car examples.

Learn the data science project workflow: collect data, analyze data, derive hypotheses and actions, deploy changes, and reanalyze to optimize sales funnels and manufacturing lines.

Explore how data from sales, manufacturing, agriculture, and marketing drives artificial intelligence, data science, and machine learning applications—from lead prioritization and automated inspections to personalized recommendations and precision farming.

Learn a framework to brainstorm AI projects that are feasible and valuable for your business, using cross functional teams to identify tasks ripe for AI automation, even with small data.

Assess AI projects with technical, business, and ethical due diligence to verify feasibility, data needs, and timelines; decide build versus buy and in-house versus outsourced for value.

Collaborate with ai teams to define acceptance criteria, provide training and test data, and use development and validation test sets to measure accuracy.

discover open source machine learning frameworks and widely shared ai resources on github and arxiv, and compare cloud, on-prem, and edge deployments to accelerate and optimize ai development.

Learn how AI fits any organization by exploring case studies of complex AI products, key team roles, and a practical transformation playbook, with a concrete first-step roadmap to start today.

Explore how smart speakers process voice commands via a four-step AI pipeline: wake word detection, speech recognition, intent recognition, and execution, with case studies from jokes to timers.

Explore how self-driving cars fuse camera, radar, and laser data to detect cars, pedestrians, and obstacles, plan motion, and control steering and speed using machine learning driven pipelines.

Explore roles and responsibilities on an artificial intelligence team, from software engineers and machine learning engineers to data scientists and product managers, understanding data volumes from megabytes to petabytes.

Learn the AI transformation playbook: launch pilot projects to gain momentum, build a centralized in-house AI team, and provide broad AI training to shape strategy.

Explore the final two steps of the AI transformation playbook: build an AI strategy, align a data strategy with a unified data warehouse, and master communications for defensible advantage.

Avoid AI pitfalls by pairing machine learning engineers with business talent, staying realistic about AI limits, and planning feasible projects with clear milestones and metrics.

Take the first step into AI and pursue concrete steps toward AI transformation. Collaborate with friends to learn, brainstorm small projects, and discuss with leadership the value of AI adoption.

Explore a realistic view of artificial intelligence, addressing its limitations, bias, and adversarial attacks, and examine AI's ethical challenges and the global job landscape.

Maintain a realistic view of AI by balancing optimism and caution. Explainability, bias, and adversarial attacks remain central challenges, even as AI transforms industries.

Explore how ai learns from internet text to form biased associations, and examine techniques like zeroing bias dimensions, inclusive data, transparency, auditing, and diverse teams to reduce discrimination.

Examine how tiny, almost imperceptible perturbations fool AI systems into misclassifying images, from panda to stop signs, and explore defenses and ongoing research.

Explore the adverse uses of AI, including deep fakes, oppressive surveillance, and fake comments, and examine how detection and anti-spam measures protect democracy and privacy.

Explore how artificial intelligence reshapes developing economies by automating low-end jobs, enabling leapfrog progress, and expanding education, with a focus on vertical industries and public private partnerships.

Explore how automation and AI reshape the labor market, estimating job displacement and creation across sectors, and learn strategies like conditional basic income and lifelong learning to navigate the impact.

Explore AI basics: artificial intelligence, machine learning, and data science, and the end-to-end workflow from data collection to deployment, including feasibility and business value.