Course Content
Module 1 : What Is AI Engineering?
After this lesson, learners will be able to: Identify where AI adds real business value Apply Jobs-To-Be-Done (JTBD) for problem discovery Score AI ideas using Pain Ă— Frequency Ă— Cost Ă— Urgency Prioritize high-ROI AI opportunities
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Overview of AI vs ML vs DL
11:07
Role of AI engineers
13:48
Jobs-To-Be-Done (JTBD) Framework for AI
04:30
Quiz-1
00:10:00
Assignment -1
Module 2 :Data Foundations
Data is the backbone of any AI or Machine Learning system. High-quality, well-structured, and relevant data dramatically improves model performance, accuracy, and reliability. Understanding how data is collected, processed, and prepared is essential for building robust AI solutions.
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Types of Data & Labeling
02:39
Feature Engineering Basics
05:12
Data Pipelines
05:25
Quiz-2
Assignment-2
Module 3: ML Fundamentals for Engineers
Machine Learning (ML) enables systems to automatically learn patterns from data and make predictions or decisions. Engineers must understand the core types of ML problems, how to evaluate models, and how to manage errors and generalization to build reliable AI systems.
0/6
Core ML Problem Types
05:40
Production Data Pipelines: Airflow, Kafka, dbt
Data Contracts, Quality, and Governance
Compliance: GDPR, HIPAA, SOC 2 Standards
Quiz-3
Assignment-3
AI Engineering

  • Overview of AI vs ML vs DL

    Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are closely related fields, but each represents a different level of complexity and capability within intelligent systems. Understanding the differences helps clarify how modern technologies work and how they are applied in real-world scenarios.

    1. Artificial Intelligence (AI)

    AI is the broadest concept.
    It refers to the science of creating machines or systems that can perform tasks that normally require human intelligence.

    Key Capabilities

    • Problem-solving

    • Reasoning

    • Learning

    • Perception (vision, speech)

    • Decision-making

    Examples

    • Chatbots and virtual assistants

    • Recommendation systems

    • Smart home automation

    • Autonomous vehicles

    AI includes many subfields, including ML and DL.

    2. Machine Learning (ML)

    ML is a subset of AI.
    It focuses on enabling machines to learn patterns from data and improve performance over time without being explicitly programmed.

    How ML Works

    • Collect data

    • Train algorithms

    • Learn patterns

    • Make predictions or decisions

    Common ML Algorithms

    • Linear/Logistic Regression

    • Decision Trees

    • Random Forest

    • Support Vector Machines

    • K-means Clustering

    Examples

    • Spam email detection

    • Credit score prediction

    • Product recommendations

    • Fraud detection

    ML is the “engine” that powers many AI applications.

    3. Deep Learning (DL)

    DL is a specialized subset of ML that uses artificial neural networks with multiple layers (hence “deep”).
    It is inspired by the structure and function of the human brain.

    Why DL is Powerful

    • Automatically extracts features from raw data

    • Handles large, complex, unstructured datasets

    • Excels in vision, speech, and language tasks

    Common DL Architectures

    • Convolutional Neural Networks (CNNs)

    • Recurrent Neural Networks (RNNs)

    • Transformers

    • Generative Adversarial Networks (GANs)

    Examples

    • Image and video recognition

    • Language translation

    • Self-driving car perception

    • Voice assistants (speech-to-text)

    DL fuels modern breakthroughs in AI.

    Classroom Iink : https://meet.google.com/inz-kabs-wjj

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