Explore the risks associated with artificial intelligence, including lack of transparency, bias and discrimination, privacy concerns, user spoofing, security risks, dependence, and loss of human connection.

Learn Salesforce trusted AI principles including responsible, accountable, transparent, empowering, and inclusive, and how they safeguard data, protect human rights, enable transparent reasoning, empower humans, and promote inclusion.

Learn how Einstein Prediction Builder enables non-code, custom predictions for business outcomes, such as predicting patient no-shows in medical appointments. Build a medical appointment object with age and diabetes fields.

Learn how to set up Einstein Prediction Builder in Salesforce, including signing up, logging in, and configuring a custom object to build your first prediction.

Create a custom object named medical appointment in Salesforce, add fields and upload data to enable a prediction of patient show up probability.

Add custom fields to the medical appointment object in Salesforce, including age (number), alcoholism (checkbox), appointment day (date), diabetes (checkbox), gender (picklist), handicap (checkbox), and hypertension (checkbox).

Add custom fields to the medical appointments object in Salesforce, including neighborhood, no show, patient id, scheduled day, and SMS received, to prepare for predictions.

Import records into the medical appointment object using the data import wizard, map Excel fields to Salesforce, and explore predicting no-show with Einstein Prediction Builder.

Create and enrich a medical appointment custom object with fields and historical data, then use Einstein Prediction Builder to predict no-shows and generate a probability score for future records.

Use Einstein Prediction Builder to predict no show probability for medical appointments. Enable the prediction, select the medical appointment object and key fields, and review the no show probability field.

Enable the prediction by viewing the scorecard and disabling other predictions to activate it, then wait up to 24 hours for the no show probability score to populate.

Learn to apply Einstein Prediction Builder to a pizza reservation object, predicting whether upcoming reservations will be completed or no shows using fields like contact, status, party size, and time.

Use Einstein Prediction Builder to forecast reservation completion for upcoming bookings by training on past records and selecting influential fields, then create a complete probability score to predict outcomes.

Enable the Einstein prediction for the reservation object by enabling the completed probability score on the scorecard, then wait for results to estimate no show probability.

Discover how Einstein for developers, a beta AI tool, writes Salesforce code from natural language, generating Apex classes and triggers, and learn how to enable it in Visual Studio Code.

Install Visual Studio Code with the Salesforce Extension Pack and Salesforce CLI, configure a theme, add Einstein for developers, and prepare to authorize the org in the next tutorial.

Learn how to set up an Einstein for developers project in VS Code, authorize a Salesforce org, and generate Apex code with Einstein to fetch ten accounts and their contacts.

Explore Einstein for developers and how the Einstein generate code extension helps you write methods, such as get cases by account, and validate fields in Apex.

Explore how Einstein activity capture syncs emails and events between Salesforce and Gmail or Outlook, reflecting activities in related accounts, contacts, and the activity timeline.

Assign the Einstein activity capture license and the standard Einstein activity capture permission set to the user, enable it, and connect Google to Salesforce to sync contacts and emails in both directions.

Learn how Einstein activity capture syncs emails and calendar invites between Salesforce and Gmail, enabling seamless two-way updates of meetings, contacts, and communications.

Explore how Einstein bots handle basic queries and transfer to live agents, freeing agents for complex tasks, and learn to set up a new bot with a fresh developer account.

Set up a new Salesforce developer account, verify your email and change your password, then learn to explore Einstein bots for the Salesforce AI associate exam prep.

Enable chat in setup, then turn on Einstein bot to offer two options—create a new case and check case status—and set up omni channel in Salesforce for live agent handoff.

Set up an Einstein bot from scratch, add questions and variables, configure welcome messages and top menu actions, and enable custom chatbot privileges for seamless case creation.

Add static questions to the Einstein bot to collect customer details and case description, invoke a flow to create a case, and return the case number and ID.

Learn to invoke a flow from an Einstein bot to create a new case, capturing case id and case number, and display confirmation messages during a guided chat.

Learn to invoke an apex class from an Einstein bot to fetch case statuses by entering a case number, display results, and handle not found cases within a menu workflow.

Configure an Einstein bot to create a case, present a menu with end chat or transfer to an agent, enable omnichannel, and handle agent availability.

Disable the pre chat page in embedded service deployments by editing chat settings, then monitor event logs and performance logs to debug sessions.

Explore text preview for the Einstein bot, showing how to start a chat, select options, capture name, company, issue, contact details, and a created case with transfer to an agent.

Explore how Einstein next best action uses flows, strategies, and recommendations to present actionable steps on lightning pages, experience cloud sites, and external sites, with a gift basket example.

Build a screen flow for the Einstein next best action tool to capture recipient details and trigger a templated email to shipping when a gift basket recommendation is accepted.

Build a recommendation strategy flow for Einstein next best action in Salesforce, returning a collection of recommendations for the account record and deploying it on a lightning record page.

Explore AI capabilities in CRM using Einstein tools to discover customer insights, predict outcomes, recommend next best actions, automate tasks, and generate tailored content.

Explore Einstein Discovery, visualize and predict business data insights, including regressions for numeric outcomes, binary classification for yes or no results, and multi-class classification for multiple options.

Explore Salesforce's five guidelines for trusted generative AI: accuracy, safety, honesty, empowerment, and sustainability, ensuring verifiable results, bias mitigation, user consent, enhanced human productivity, and smaller carbon footprints.

Learn how data quality drives AI success by identifying factors like missing, duplicate, incomplete, stale records, and lack of standards, and discover how good data boosts accuracy, insights, and revenue.

The course explains six data quality dimensions: age, completeness, accuracy, consistency, duplicates, and usage, and emphasizes updating data, filling fields, and standardizing formats for reliable AI outcomes.

Set standards for creating, processing, and maintaining data to prevent duplicates. Ensure consistent currency and date formatting, govern the data lifecycle from creation to archiving, and monitor quality and privacy.

Remove duplicate or irrelevant observations and fix structural errors to prepare clean, AI-ready data; then handle missing values, remove outliers, and validate data formats.

Develop a data management strategy and share it with your team to align data collection, review, and updates with business goals, and import data into Salesforce to ensure deduplicated records.