LunarTech – Machine Learning Fundamentals 2024-12

Machine Learning Fundamentals, Level Up Your Machine Learning Career. Master the ML Fundamentals Employers Crave. Build a solid foundation in ML concepts and terminology, enabling you to make informed decisions about your career path Gain hands-on experience applying ML to solve practical challenges, building 6 portfolio projects that showcases your abilities. Learn essential ML algorithms, giving you the flexibility to address different data challenges and impress potential employers. Develop the knowledge to select the right ML models and techniques, positioning you as a valuable problem-solver.

What you’ll learn

• Fundamentals of Machine Learning
• Tackle Real-World Problems
• Master In-Demand Techniques
• Choose the Right Path
• Understand the Fundamentals of Machine Learning, Bias-Variance Trade-offs, and Regularization Techniques
• Master Linear and Logistic Regression, Predict Continuous Outcomes, and Understand Core Statistical Concepts

Specificatoin of Machine Learning Fundamentals

• Publisher : LunarTech
• Teacher : Tatev Aslanyan
• Language : English
• Level : All Levels
• Number of Course : 21
• Duration : 4 hours and 4 minutes

Content of Machine Learning Fundamentals

0. Introduction
1. Machine Learning Basics
2. Bias-Variance Trade-off
3. Overfitting Regularization
4.1 Linear Regression – Causal Analysis (Part 1)
4.2 Linear Regression – (Part 2)
5. Logistic Regression & Maximum Likelihood Estimation (MLE)
6. Linear Discriminant Analysis (LDA)
7. K-Nearest Neighbors (KNN)
8. Decision Trees
9. Bagging
10. Random Forest
11. (Boosting Part 1) Introduction
12. Boosting (Part 2) – AdaBoost
13. Boosting (Part 3) – Gradient Boosting Model (GBM)
14. Boosting (Part 4) – XGBoost
15. Clustering (Part 1) – K-Means & Elbow Method
16. Clustering (Part 2) – Hierarchical Clustering
17. Clustering (Part 3) – DBScan
18. Dimensionality Reduction (Part 1) – Feature Selection
19. Dimensionality Reduction (Part 2) – Principal Component Analysis

Pictures

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296 MB