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Курс: Applied Machine Learning in Python. Чтобы вернуться, нажмите здесь.
- Course Syllabus
- Introduction
- Help us learn more about you!
- Key Concepts in Machine Learning
- Python Tools for Machine Learning
- Notice for Auditing Learners: Assignment Submission
- An Example Machine Learning Problem
- Examining the Data
- K-Nearest Neighbors Classification
- Zachary Lipton: The Foundations of Algorithmic Bias (optional)
- Introduction to Supervised Machine Learning
- Overfitting and Underfitting
- Supervised Learning: Datasets
- K-Nearest Neighbors: Classification and Regression
- Linear Regression: Least-Squares
- Linear Regression: Ridge, Lasso, and Polynomial Regression
- Logistic Regression
- Linear Classifiers: Support Vector Machines
- Multi-Class Classification
- Kernelized Support Vector Machines
- Cross-Validation
- Decision Trees
- A Few Useful Things to Know about Machine Learning
- Ed Yong: Genetic Test for Autism Refuted (optional)
- Model Evaluation & Selection
- Confusion Matrices & Basic Evaluation Metrics
- Classifier Decision Functions
- Precision-recall and ROC curves
- Multi-Class Evaluation
- Regression Evaluation
- Practical Guide to Controlled Experiments on the Web (optional)
- Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- Naive Bayes Classifiers
- Random Forests
- Gradient Boosted Decision Trees
- Neural Networks
- Neural Networks Made Easy (optional)
- Play with Neural Networks: TensorFlow Playground (optional)
- Deep Learning (Optional)
- Deep Learning in a Nutshell: Core Concepts (optional)
- Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
- Data Leakage
- The Treachery of Leakage (optional)
- Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
- Data Leakage Example: The ICML 2013 Whale Challenge (optional)
- Rules of Machine Learning: Best Practices for ML Engineering (optional)
- Introduction
- Dimensionality Reduction and Manifold Learning
- Clustering
- How to Use t-SNE Effectively
- How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
- Conclusion
- Post-course Survey