Complete AI Crash Course: Text, Video, and Code Content

Complete AI Crash Course: Text, Video, and Code Content

Module 1: Introduction to Artificial Intelligence

Lesson Text

  • What is AI?
    Artificial Intelligence studies how computers can perform tasks that typically require human intelligence, such as learning, reasoning, pattern recognition, and decision-making.

  • History & Milestones:
    From the 1950s (Turing Test, early logic programs) to modern generative models. AI has grown alongside increased computational power and massive data availability.

  • Types of AI:

    • Narrow (Weak) AI: Single-task (e.g., chatbots, recommendation engines)

    • General AI: Human-level intelligence (theoretical)

    • Super AI: Surpasses human intelligence (hypothetical)

  • Applications:

    • Virtual assistants (e.g., Siri, Alexa)

    • Recommendation systems (Netflix, YouTube)

    • Autonomous vehicles

    • Medical diagnostics

Demo Video

Assignment

  • List 5 examples of AI you interact with weekly.

Module 2: Machine Learning Fundamentals

Lesson Text

  • What is Machine Learning (ML)?
    ML enables systems to learn from data, identify patterns, and make predictions with minimal explicit programming.

  • Supervised vs. Unsupervised Learning:

    • Supervised: Labeled data (spam detection, image classification)

    • Unsupervised: Unlabeled data (clustering, association)

  • Classic Algorithms:

    • Linear Regression

    • Decision Trees

    • K-Means Clustering

Code Example

python
# Simple Linear Regression with Scikit-Learn
from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([3, 4, 2, 5, 6])

model = LinearRegression()
model.fit(X, y)
print(f"Predicted value for 6: {model.predict([[6]])}")

Demo Video

Assignment

  • Decide: Is a given scenario supervised or unsupervised learning?

Module 3: Deep Learning & Neural Networks

Lesson Text

  • What is Deep Learning?
    A subset of ML involving multi-layered neural networks capable of complex pattern recognition.

  • Neural Network Basics:

    • Input Layer → Hidden Layers → Output Layer

  • Everyday Use Cases:

    • Image and speech recognition

    • Language translation

Code Example

python
# Simple Neural Network with Keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]]) # XOR

model = Sequential([
Dense(8, input_dim=2, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X, y, epochs=500, verbose=0)
print(model.predict(X))

Demo Video

Assignment

  • Draw and label the parts of a simple neural network.

Module 4: Natural Language Processing (NLP)

Lesson Text

  • What is NLP?
    The study of AI models that process and understand human language.

  • Applications:

    • Chatbots (e.g., ChatGPT)

    • Sentiment analysis

    • Machine translation

Code Example

python
# Sentiment Analysis Using TextBlob
from textblob import TextBlob

text = "AI is transforming business and personal productivity!"
blob = TextBlob(text)
print(blob.sentiment)

Demo Video

Assignment

  • Test a free NLP demo, e.g., sentiment analysis, and describe the outcome.

Module 5: Computer Vision

Lesson Text

  • What is Computer Vision?
    Enables computers to “see” and interpret images or videos.

  • Use Cases:

    • Security cameras (face detection)

    • Social media (automatic tagging)

    • Self-driving cars

Code Example

python
# Basic Image Classification with PyTorch (MNIST Dataset)
import torch
import torchvision
from torchvision import transforms

# Download MNIST dataset
mnist = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())
print(f"Number of examples: {len(mnist)}")

Demo Video

Assignment

  • Upload an image to a free AI tool and interpret the results.

Module 6: Generative AI

Lesson Text

  • What is Generative AI?
    Models that create new content, such as text, images, music, or code.

  • Famous Models:

    • GPT (text)

    • Midjourney, DALL-E (images)

    • Suno (music)

Code Example

python
# Generate Text with OpenAI's GPT (via openai Python package)
import openai

openai.api_key = 'sk-your-api-key'
response = openai.Completion.create(
engine="text-davinci-002",
prompt="Explain generative AI in simple terms.",
max_tokens=60
)
print(response.choices[0].text.strip())

Demo Video

Assignment

  • Generate a paragraph or image using a leading platform, paste and discuss it.

Module 7: Popular AI Tools & Platforms

Lesson Text

  • Current Leading Platforms:

    • ChatGPT, Claude, Gemini, Copilot, MidJourney

  • Integration Tips:

    • Use free trials to test features.

    • Compare strengths (text, image, code, agentic AI, etc.)

Video Overview

Assignment

  • Try a tool and describe a practical use case.

Module 8: Prompt Engineering

Lesson Text

  • Effective Prompt Design:

    • Provide clarity, context, step-by-step instructions.

    • Use examples to illustrate tasks.

Prompt Exercise

text
# Before: Summarize this.
# After: Summarize the following article in three bullet points, focusing on business applications.

Assignment

  • Rewrite ineffective prompts into more effective requests.

Module 9: Using AI for Productivity

Lesson Text

  • AI can automate tasks like email sorting, scheduling, or data entry.

  • Building simple workflows with Zapier, Copilot, or scripting.

Assignment

  • Use an AI tool to automate a daily task; describe the outcome.

Module 10: AI Ethics & Safety

Lesson Text

  • Risks:

    • Bias, fairness, and discrimination

    • Misinformation (deepfakes)

  • Ethics Best Practices:

    • Transparency

    • Testing for bias

    • Using reputable data sources

Assignment

  • Research an AI ethics case and summarize the lessons learned.

Module 11: Mini AI Project

Step-by-Step Guide

  • Pick: Chatbot with no-code tools (e.g., Zapier, ManyChat) or a basic classifier in Python.

  • Building a Chatbot Example:

    • Use ChatGPT API, set up a flow, test responses.

Downloadable Templates

  • “Hello World” chatbot script (editable)

  • Classifier Python notebook

Module 12: The Future of AI

Lesson Text

  • Trends:

    • AI agents with memory/personality

    • Multimodal AI (text, image, audio)

  • Careers:

    • AI upskilling is relevant for all knowledge workers.

Assignment

  • Draft a personal upskilling plan to integrate AI into your role.

Bundled Resources

  • Quizzes:
    Self-assessment questions for each module (multiple choice, short answer)

  • Slide Decks:
    Module-aligned downloadable presentation files

  • Videos:
    Curated YouTube/Crash Course playlists for each module

  • Assignments:
    Practical, real-world tasks to reinforce learning

  • Project Notebooks:
    Downloadable Python notebooks for hands-on projects

Code Repository and Project Notebooks

  • Starter code and Python notebooks for all examples, ready to be distributed via GitHub or Google Colab.

Licensing & Usage

  • All textual content is original and free for commercial use.

  • For code, review licenses of included libraries (Scikit-learn, TensorFlow, PyTorch, OpenAI).

  • For video assets, link to open, free-to-use educational resources or record your own, using the lesson scripts above as guidance.

This curriculum includes text, high-quality public video lessons, and sample code, providing everything needed to deliver a robust AI crash course to any beginner audience.