AI & Machine Learning Foundations
From raw data to intelligent models: data literacy, linear regression, decision trees, K-Means clustering, NLP, computer vision, and AI ethics.
Course Overview
Build a rock-solid foundation across all primary domains of Artificial Intelligence and Data Science. Understand the data lifecycle from collection and cleaning to exploratory data analysis, implement core supervised algorithms (linear regression, decision trees) and unsupervised clustering (K-Means), explore NLP and Computer Vision pipelines, and master the ethical principles of algorithmic fairness.
What You'll Learn & Build
Prerequisites
- Basic high school mathematics (averages, slopes, coordinate geometry)
- Fundamental Python syntax (lists, loops, basic math)
Who This Is For
- Aspiring data scientists, software engineers entering AI, and students seeking comprehensive ML literacy
Course Curriculum & Lessons
4 Modules • 12 Total Lessons • Lessons 1-4 are Free to Solve-
1. Demystifying AI: Rule Engines vs Machine Learning
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2. Data Hygiene: Handling Missing Values & Mean Imputation
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3. Feature Scaling: Z-Score Standardization
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4. Linear Regression: Best-Fit Slope & Predictions
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5. Binary Classification & The Sigmoid Function
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6. Decision Trees: Shannon Entropy & Information Gain
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7. K-Means Clustering & Centroid Recalculation
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8. Optimal Clusters: The Elbow Method & WCSS
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9. Dimensionality Reduction & Covariance Matrices
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10. NLP: Tokenization & Stopword Filtering
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11. Computer Vision: Pixel Matrices & Edge Detection
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12. AI Ethics: Demographic Parity & Fairness Auditing
Your Instructor
Data Science & Machine Learning Academy
Senior software architect with extensive production engineering experience. Builds clean, modern curriculum and interactive web playgrounds for developers.
