A Hands-On Introduction to AI

What’s All the Hype About? What Is Machine Learning?

What Are Neural Networks That Mimic the Human Brain?

How Perceptrons Work

Taking It Further: What Is Deep Learning?

Machine Learning vs. Deep Learning: Key Differences

Multiple Choice Quiz

Supervised Learning: Learning with the Right Answers

Unsupervised Learning: Finding Patterns Without Answers

Reinforcement Learning: Learning Through Rewards

Fill-in-the-Blank Quiz

TensorFlow: A Library for Machine Learning and Deep Learning

Tensor: The Core Unit of TensorFlow

Tensor Dimensions

Multiple Choice Quiz

Understanding Tensor Operations

Top 5 Most Used TensorFlow APIs

Building a Simple Linear Regression Model with TensorFlow

Fill-in-the-Blank Quiz

Keras: A Simple and Intuitive Neural Network Library

Training and Evaluating Models with Keras

Multiple Choice Quiz

The Essential Ingredient for Training AI: Datasets

Data File Formats Used in AI Training

Preprocessing: Preparing Data for AI

Handling Missing Data with Python

Multiple Choice Quiz

Normalization: Adjusting the Scale of Data

Standardization: Matching Data Scales

Normalization vs. Standardization: When to Use Which?

Encoding Categorical Data

Label Encoding vs. One-Hot Encoding

Fill-in-the-Blank Quiz

What Are Features in Machine Learning?

Feature Selection and Dimensionality Reduction

Labels: The Ground Truth of Data

Bias: Adjusting the Output Baseline

Multiple Choice Quiz

Loss Functions: Comparing Predictions to Reality

Cost Functions: Average Error Across All Data

The Goal of Training: Optimization and Gradient Descent

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How AI Models Learn

Training Dataset: Learning Patterns

Validation Dataset: Tuning the Model

Test Dataset: Final Performance Check

Multiple Choice Quiz

Hyperparameters: Key to Model Performance

Learning Rate: Controlling Training Speed

Batch Size: How Much Data to Learn at Once

Epochs: Number of Times to Train

Overfitting: A Closer Look

Underfitting: A Closer Look

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What Is the Purpose of ML Models?

Classification Models: Grouping Data

Accuracy: Measuring Prediction Quality

Precision: Measuring Correctness of Positive Predictions

Recall: Measuring Coverage of Positive Predictions

F1-Score: Balancing Precision and Recall

Multiple Choice Quiz

Regression Models: Predicting Continuous Values

Mean Squared Error (MSE)

Mean Absolute Error (MAE)

R-Squared (R²)

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Why Algorithms Matter in Machine Learning

Linear Regression: Predicting with a Line

Logistic Regression: Classification with Probabilities

Decision Tree: Making Predictions Through Questions

Random Forest: Using Multiple Trees for Better Predictions

Multiple Choice Quiz

K-Nearest Neighbors: Classifying by Proximity

Support Vector Machines: Finding the Best Separation

K-Means Clustering: Grouping Similar Data

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What is a Neural Network?

What is a Neuron?

Key Components of Neural Networks

Input Layer

Hidden Layer

Output Layer

Relationship Between Number of Layers and Model Performance

Multiple Choice Quiz

What is a Perceptron?

Limitations of Single-Layer Perceptrons

Role of Activation Functions

Sigmoid Function: Converting Values to Probabilities

Multiple Choice Quiz

ReLU Function: Activating Only Positive Values

Softmax Function: Handling Multiple Probabilities

Comparison of Activation Functions: Sigmoid, ReLU, Softmax

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Fully Connected Layers

Feature Extraction in Input Layers

Key Methods for Feature Extraction

Impact of Hidden Layer Depth

Forward Propagation for Prediction

Backpropagation: How Neural Networks Learn

Multiple Choice Quiz

Gradient Descent: The Core Learning Principle

Stochastic Gradient Descent: Fast and Efficient Learning

Batch Gradient Descent for Stable Training

Momentum Optimization for Speed and Stability

Adam Optimizer: Fast and Efficient Optimization

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Challenges of Deepening Neural Networks

Dropout to Prevent Overfitting

Batch Normalization for Stable Learning

Multiple Choice Quiz

Weight Initialization Techniques

L1 & L2 Regularization to Prevent Overfitting

Transfer Learning for Reusing Learned Models

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Convolutional Neural Networks (CNN) for Image Recognition

Key Components of CNNs

Convolution Operations for Image Analysis

Filters for Pattern Detection in Images

Multiple Choice Quiz

Strides in Convolutional Operations

Padding to Control Image Size

Pooling to Reduce Image Size and Retain Features

Image Classification Example Using CNN

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RNN: Neural Networks That Remember Sequences

Structure and Principles of RNNs

What Is the Long-Term Dependency Problem?

Multiple Choice Quiz

LSTM: Remembering Long-Term Information

GRU: A Simpler Recurrent Neural Network

Transformer: Processing Sentences All at Once

Building an LSTM Model to Predict the Next Character

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What Is GPT?

Transformers: Understanding Sentences All at Once

Self-Attention: Finding Key Relationships Within Sentences

Multi-Head Attention: Seeing Sentences from Different Angles

Multiple Choice Quiz

How GPT Differs from Traditional Machine Learning Models

How GPT Is Different from Classic Neural Networks (RNNs)

Understanding Tokenization in GPT

Comparing GPT-3.5, GPT-4 mini, and GPT-4o

Comparing GPT, Claude, and DeepSeek Models

Fill-in-the-Blank Quiz

Multiple Choice Quiz

Writing Prompts That Get the Right Answer the First Time

Zero-Shot Prompting: Asking Without Examples

Few-Shot Prompting: Asking with Examples

Chain of Thought: Step-by-Step Reasoning in Prompts

Generated Knowledge: Adding Depth to Answers

Role Prompting: Assigning Roles for Smarter AI

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How Fine-Tuning Differs from Standard Training

Multiple Choice Quiz

Understanding the JSON Data Format

What Is JSONL, the Fuel for Fine-Tuning?

The JSONL Format Used in Fine-Tuning

Hyperparameters: Controlling How AI Learns

Getting Started with Fine-Tuning

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AGI and the Future of Humanity

Smart Strategies to Stay Relevant in the Age of AI

In Closing