Shopping Cart

No products in the cart.

Shopping Cart

No products in the cart.

AI Soft Skills & Productivity ,

Google AI Essentials

This beginner-friendly course will introduce you to the fundamentals of AI and show you how Google’s tools, platforms, and APIs are making AI accessible to everyone — from developers to marketers, from students to CEOs.
View Course details
creatik · May 31, 2025

📜 Course Description:

Unlock the power of artificial intelligence — the Google way.
This beginner-friendly course will introduce you to the fundamentals of AI and show you how Google’s tools, platforms, and APIs are making AI accessible to everyone — from developers to marketers, from students to CEOs. By the end of this course, learners will understand how AI works and how to begin using it to solve real-world problems using Google’s powerful suite of AI products.

No prior coding experience required. Just curiosity and a little ambition.

🎯 Learning Outcomes:

By the end of this course, learners will:

  • Understand core AI and machine learning concepts
  • Explore how AI is used in Google products
  • Learn to use Google tools like Vertex AI, AutoML, and Teachable Machine
  • Gain awareness of AI ethics and responsible use
  • Build and deploy a simple AI-powered solution using Google tools

Course Content

Expand All
AI in Everyday Life (The Google Way) 5 Topics
Expand
Lesson Content
0% Complete 0/5 Steps
Lesson Objective: To introduce learners to the foundational idea of artificial intelligence — what it is, what it isn’t, and how it fits into our world today.
Definition of Artificial Intelligence In simple terms: Artificial Intelligence (AI) refers to the ability of machines or software to perform tasks that typically require human intelligence. These include problem-solving, recognizing speech, understanding language, learning from data, and even creativity. Formal Definition: AI is a branch of computer science concerned with building smart machines capable of performing tasks that usually require human intelligence. Think of AI as “teaching a computer to think, learn, and decide like a human — but faster, and with data as its fuel.”
Key Abilities of AI Learning: The ability to improve from experience (via data) Reasoning: Solving problems using logic Perception: Seeing or hearing like a human (e.g., facial recognition) Language Understanding: Comprehending and generating natural language Decision-Making: Choosing actions based on goals and data
Types of AI 🟢 Narrow AI (Weak AI): Designed to do one specific task Examples: Google Translate, Siri, spam filters 🟡 General AI (Strong AI): A theoretical AI that can perform any intellectual task a human can do We’re not there yet! 🔴 Super AI (Artificial Superintelligence): Hypothetical AI that surpasses human intelligence across all domains The stuff of sci-fi… for now.
AI vs. Automation — Not the Same Thing! Automation AI Follows rules Learns patterns Pre-programmed Self-improving Repetitive tasks Creative solutions A washing machine is automation. A smart home system that learns when to wash based on your habits? That’s AI.
What is Artificial Intelligence? 5 Topics
Expand
Lesson Content
0% Complete 0/5 Steps
Objective: By the end of this lesson, learners will understand what Artificial Intelligence (AI) is, how it differs from other technologies, and why it’s relevant in the modern business and digital world.
What Is Artificial Intelligence? (The Core Definition) Artificial Intelligence, often abbreviated as AI, refers to the simulation of human intelligence in machines. These machines are designed to think, learn, and make decisions — often mimicking human capabilities. “AI is not magic. It’s math, data, and algorithms—trained to think like us, but at machine speed.” ✳️ Key Characteristics of AI: Learning: Acquiring knowledge from data (Machine Learning) Reasoning: Drawing logical conclusions Perception: Interpreting audio, images, and text Natural Language Processing (NLP): Understanding and generating human language Decision-Making: Taking actions based on data and predictions
Types of Artificial Intelligence AI isn’t one-size-fits-all. There are three broad categories: 🔹 Narrow AI (Weak AI) AI built for a specific task. Examples: Google Maps predicting traffic YouTube recommending videos Chatbots answering FAQs 🔸 General AI (Strong AI) A still-theoretical AI that can understand and perform any intellectual task a human can. Example: AI that could write poetry, solve physics, manage a company — all in one. 🔺 Super AI An AI that surpasses human intelligence in all domains. This is science fiction… for now.
How AI Works: The Basics AI works by learning from data. Here’s a simplified process: Input Data (Images, text, numbers) Training (Machine Learning algorithms find patterns) Output (Predictions, classifications, recommendations) Imagine teaching a child to recognize cats. Show them thousands of cat photos. Over time, they get better. AI learns the same way — only faster.
AI vs. Automation – Not the Same! Feature Automation AI Task Type Repetitive, rule-based Adaptive, pattern-based Flexibility Rigid Learns and improves Example Assembly line robot arm Self-driving car adjusting to traffic Automation is a scripted recipe. AI is a chef who learns new recipes from data.
Machine Learning vs Traditional Programming 5 Topics
Expand
Lesson Content
0% Complete 0/5 Steps
Lesson Objective: To help learners understand the key differences between traditional programming and machine learning, with practical examples, simple analogies, and real-world implications.
What is Traditional Programming? Definition: Traditional programming is rule-based. A human programmer writes explicit instructions (rules) that the machine follows to achieve a task. Example: If you wanted to sort a list of names alphabetically, you would write the exact steps (an algorithm) to do it — line by line.
What is Machine Learning? Definition: In machine learning (ML), you don’t program the rules. Instead, you feed data into an algorithm, and the machine learns the patterns or rules on its own. Example: Instead of writing rules to grade essays, you feed the system hundreds of past essays with scores, and the algorithm figures out how scoring is typically done — on its own. Machine learning is like training a dog with treats. You don’t explain the rules of “sit” — you just reward the behavior until it sticks.
How They Differ (Side-by-Side) Feature Traditional Programming Machine Learning Approach Rule-based Data-driven Input Data + Rules Data + Outcomes (examples) Output Outcome Model (rules learned) Learning None Learns from data Adaptability Static Dynamic & adaptive
Visual Analogy: The Recipe vs. The Chef Traditional Programming = Following a Recipe A recipe tells you exactly what to do. No improvisation. Every time, same dish. Machine Learning = Being a Chef A chef tastes, experiments, learns from feedback, and improves over time. ML models “cook” better as they’re exposed to more “ingredients” (data).

About Instructor

creatik

8 Courses

Preview this Course
Not Enrolled
Login to Enroll
Free

Course Includes

  • 9 Lessons
  • 15 Topics

Ratings and Reviews

0.0
Avg. Rating
0 Ratings
5
0
4
0
3
0
2
0
1
0
What's your experience? We'd love to know!
No Reviews Found!
Show more reviews
What's your experience? We'd love to know!

Report

Harassment or bullying behavior
Contains mature or sensitive content
Contains misleading or false information
Contains abusive or derogatory content
Contains spam, fake content or potential malware

Block Member?

Please confirm you want to block this member.

You will no longer be able to:

  • See blocked member's posts
  • Mention this member in posts
  • Invite this member to groups
  • Message this member
  • Add this member as a connection

Please note: This action will also remove this member from your connections and send a report to the site admin. Please allow a few minutes for this process to complete.

Report

You have already reported this .