Machine Learning Fundamentals
About the Author: LunarTech
LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.
The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.
Watch Online 21 lessons
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 06:55 | |
| 2 | 1. Machine Learning Basics | 15:27 | |
| 3 | 2. Bias-Variance Trade-off | 07:11 | |
| 4 | 3. Overfitting Regularization | 15:31 | |
| 5 | 4.1 Linear Regression - Causal Analysis (Part 1) | 14:51 | |
| 6 | 4.2 Linear Regression - (Part 2) | 24:25 | |
| 7 | 5. Logistic Regression & Maximum Likelihood Estimation (MLE) | 15:09 | |
| 8 | 6. Linear Discriminant Analysis (LDA) | 11:15 | |
| 9 | 7. K-Nearest Neighbors (KNN) | 11:52 | |
| 10 | 8. Decision Trees | 16:45 | |
| 11 | 9. Bagging | 07:54 | |
| 12 | 10. Random Forest | 08:11 | |
| 13 | 11. (Boosting Part 1) Introduction | 08:01 | |
| 14 | 12. Boosting (Part 2) - AdaBoost | 10:00 | |
| 15 | 13. Boosting (Part 3) - Gradient Boosting Model (GBM) | 10:53 | |
| 16 | 14. Boosting (Part 4) - XGBoost | 08:08 | |
| 17 | 15. Clustering (Part 1) - K-Means & Elbow Method | 17:43 | |
| 18 | 16. Clustering (Part 2) - Hierarchical Clustering | 10:49 | |
| 19 | 17. Clustering (Part 3) - DBScan | 07:45 | |
| 20 | 18. Dimensionality Reduction (Part 1) - Feature Selection | 07:32 | |
| 21 | 19. Dimensionality Reduction (Part 2) - Principal Component Analysis | 08:52 |
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21 lessons · 4h 5m 9sShow all 21 lessons
- 1 Introduction 06:55
- 2 1. Machine Learning Basics 15:27
- 3 2. Bias-Variance Trade-off 07:11
- 4 3. Overfitting Regularization 15:31
- 5 4.1 Linear Regression - Causal Analysis (Part 1) 14:51
- 6 4.2 Linear Regression - (Part 2) 24:25
- 7 5. Logistic Regression & Maximum Likelihood Estimation (MLE) 15:09
- 8 6. Linear Discriminant Analysis (LDA) 11:15
- 9 7. K-Nearest Neighbors (KNN) 11:52
- 10 8. Decision Trees 16:45
- 11 9. Bagging 07:54
- 12 10. Random Forest 08:11
- 13 11. (Boosting Part 1) Introduction 08:01
- 14 12. Boosting (Part 2) - AdaBoost 10:00
- 15 13. Boosting (Part 3) - Gradient Boosting Model (GBM) 10:53
- 16 14. Boosting (Part 4) - XGBoost 08:08
- 17 15. Clustering (Part 1) - K-Means & Elbow Method 17:43
- 18 16. Clustering (Part 2) - Hierarchical Clustering 10:49
- 19 17. Clustering (Part 3) - DBScan 07:45
- 20 18. Dimensionality Reduction (Part 1) - Feature Selection 07:32
- 21 19. Dimensionality Reduction (Part 2) - Principal Component Analysis 08:52
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