Materials
Beginner
4 Chapters · 79 Lessons
Introduction to Machine Learning
Learn the basic concepts and algorithms of machine learning and explore various ML models.
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Chapter 1
Machine Learning: Learning Patterns Hidden in Data
Lessons
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
What Does It Mean to Train AI?
The Result of AI Training: Files Made of Matrices
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
Chapter 2
Essential Knowledge for Understanding Machine Learning
Lessons
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
Weights: Determining Feature Importance
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
Fill-in-the-Blank Quiz
Chapter 3
How Machine Learning Models Work
Lessons
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
Fill-in-the-Blank Quiz
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)
Fill-in-the-Blank Quiz
Chapter 4
Machine Learning Algorithm Basics
Lessons
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
Fill-in-the-Blank Quiz