Applied Machine Learning: Algorithms
1h 58mIntermediate2024-04-15
Authors
Matt Harrison
Python and Data Science Corporate Trainer, Author, Speaker, Consultant
Course details
With the growing importance of machine learning in almost every sector, professionals need a deeper understanding and practical approach to implementing ML algorithms effectively.
This course covers commonly used machine learning algorithms. Instructor Matt Harrison focuses on non-deep learning algorithms, covering PCA, clustering, linear and logistic regression, decision trees, random forests, and gradient boosting.
This course covers commonly used machine learning algorithms. Instructor Matt Harrison focuses on non-deep learning algorithms, covering PCA, clustering, linear and logistic regression, decision trees, random forests, and gradient boosting.
Skills covered
Machine LearningPythonArtificial Intelligence (AI)Open SourceDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Applied machine learning - Algorithms
- 02 - What you should know
1. Clustering
- 03 - K-means
- 04 - K evaluation
- 05 - Understanding clusters
- 06 - Other algorithms
- 07 - Challenge - Apply KNN
- 08 - Solution - Apply KNN
2. PCA
- 09 - PCA
- 10 - Structure of components
- 11 - Components
- 12 - Scatter plot
- 13 - Other algorithms
- 14 - Challenge - Utilize PCA
- 15 - Solution - Utilize PCA
3. Linear Regression
- 16 - Linear regression algorithm
- 17 - scikit-learn
- 18 - Real-world example
- 19 - Assumptions
- 20 - Challenge - Develop a linear regression model
- 21 - Solution - Develop a linear regression model
4. Logistic Regression
- 22 - Logistic regression algorithm
- 23 - Basic example
- 24 - Assumptions
- 25 - Challenge - Construct a logistic regression model
- 26 - Solution - Construct a logistic regression model
5. Decision Trees
- 27 - Decision tree algorithm
- 28 - Real-world example
- 29 - Random Forest and XGBoost
- 30 - Challenge - Design a decision tree model
- 31 - Solution - Design a decision tree model
Conclusion
- 32 - Next steps
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