Useful Links
1. Introduction to Machine Learning for Developers
2. Machine Learning Project Lifecycle
3. Supervised Learning Fundamentals
4. Unsupervised Learning Fundamentals
5. Python Machine Learning Ecosystem
6. Data Engineering for Machine Learning
7. Pre-trained Models and Transfer Learning
8. Model Deployment and MLOps
9. Production Monitoring and Maintenance
10. Natural Language Processing for Developers
11. Computer Vision for Developers
12. Responsible AI and Ethics
13. Advanced Topics and Specializations
  1. Computer Science
  2. Artificial Intelligence
  3. Machine Learning

Machine Learning for Developers

1. Introduction to Machine Learning for Developers
2. Machine Learning Project Lifecycle
3. Supervised Learning Fundamentals
4. Unsupervised Learning Fundamentals
5. Python Machine Learning Ecosystem
6. Data Engineering for Machine Learning
7. Pre-trained Models and Transfer Learning
8. Model Deployment and MLOps
9. Production Monitoring and Maintenance
10. Natural Language Processing for Developers
11. Computer Vision for Developers
12. Responsible AI and Ethics
13. Advanced Topics and Specializations
  1. Unsupervised Learning Fundamentals
    1. Clustering Techniques
      1. Partitioning Methods
        1. K-Means Clustering
          1. K-Medoids
            1. Fuzzy C-Means
            2. Hierarchical Methods
              1. Agglomerative Clustering
                1. Divisive Clustering
                  1. Dendrogram Interpretation
                  2. Density-Based Methods
                    1. DBSCAN
                      1. OPTICS
                        1. Mean Shift
                        2. Clustering Evaluation
                          1. Internal Validation Metrics
                            1. External Validation Metrics
                              1. Cluster Quality Assessment
                            2. Dimensionality Reduction
                              1. Linear Methods
                                1. Principal Component Analysis
                                  1. Linear Discriminant Analysis
                                    1. Independent Component Analysis
                                    2. Non-Linear Methods
                                      1. t-SNE
                                        1. UMAP
                                          1. Autoencoders
                                          2. Feature Selection
                                            1. Filter Methods
                                              1. Wrapper Methods
                                                1. Embedded Methods
                                              2. Association Rule Learning
                                                1. Market Basket Analysis
                                                  1. Apriori Algorithm
                                                    1. FP-Growth Algorithm
                                                      1. Rule Evaluation Metrics

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                                                    3. Supervised Learning Fundamentals

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                                                    5. Python Machine Learning Ecosystem

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