Update mlpaths.md

Author: clizarraga-UAD7

docs/mlpaths.md

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 #### 1. Introduction to Data Science and Machine Learning
 
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+??? note "Topic description"
 
     **Learning Objective**: Understand the fundamental concepts of data science and machine learning, and their real-world applications.
 
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 ####  2. Python for Data Science
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+
+??? note "Topic description"
 
     **Learning Objective**: Develop proficiency in using Python for data manipulation, analysis, and visualization.
 
     **Related Skills**:
- 	- Mastering Python syntax and data structures
- 	- Utilizing NumPy for efficient numerical operations
- 	- Applying Pandas for data ingestion, cleaning, and transformation
+
+    - Mastering Python syntax and data structures
+    - Utilizing NumPy for efficient numerical operations
+    - Applying Pandas for data ingestion, cleaning, and transformation
 
     **Subtopics**:
- 	1. Python programming basics (variables, data types, control structures, functions)
- 	2. NumPy arrays and universal functions
- 	3. Pandas DataFrames and Series for data manipulation
- 	4. Data visualization with Matplotlib and Seaborn
- 	5. Integrating Python with data science libraries (scikit-learn, TensorFlow, PyTorch)
+
+    - Python programming basics (variables, data types, control structures, functions)
+    - NumPy arrays and universal functions
+    - Pandas DataFrames and Series for data manipulation
+    - Data visualization with Matplotlib and Seaborn
+    - Integrating Python with data science libraries (scikit-learn, TensorFlow, PyTorch)
 
     **References and Resources**:
- 	- "Python for Data Analysis" by Wes McKinney
- 	- "Python Data Science Handbook" by Jake VanderPlas
- 	- Datacamp's Python for Data Science Track
+
+    - "Python for Data Analysis" by Wes McKinney
+    - "Python Data Science Handbook" by Jake VanderPlas
+    - Datacamp's Python for Data Science Track
+
 
 
+#### 3. Ethical Considerations in Data Science
+
+??? note "Topic decription"
+
+    **Learning Objective**: Develop an understanding of the ethical implications and responsible practices in data science.
+
+    **Related Skills**:
+
+    - Identifying and mitigating bias in data and models
+    - Ensuring fair and equitable decision-making
+    - Protecting privacy and data security
+
+   **Subtopics**:
+   
+   - Bias and fairness in machine learning
+   - Interpretability and explainability of models
+   - Privacy-preserving techniques (differential privacy, federated learning)
+   - Data provenance and provenance tracking
+   - Responsible AI principles and guidelines
+
+   **References and Resources**:
+   - "Ethical Algorithms" by Michael Kearns and Aaron Roth
+   - "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
+   - Coursera course "AI Ethics" by DeepLearning.AI
 
-#### Ethical Considerations in Data Science
 
 ### B: Statistics
-   - Statistical Learning and Regression Models
+
+#### 4. Statistical Learning and Regression Models
+
+??? note "Topic description"
+
+    **Learning Objective**: Understand and apply statistical learning techniques, with a focus on regression models.
+    
+    **Related Skills**:
+
+    - Fitting and evaluating linear regression models
+    - Applying logistic regression for classification tasks
+    - Interpreting model coefficients and making predictions
+
+    **Subtopics**:
+    
+    - Simple and multiple linear regression
+    - Assumptions and diagnostics of linear regression
+    - Logistic regression for binary classification
+    - Evaluating model performance (R-squared, accuracy, precision, recall, F1-score)
+    - Regularization techniques (Ridge, Lasso, Elastic Net)
+
+    **References and Resources**:
+
+    - "An Introduction to Statistical Learning" by Gareth James et al.
+    - "Pattern Recognition and Machine Learning" by Christopher Bishop
+    - Coursera course "Machine Learning" by Andrew Ng
+
 
 ### C: Classical Machine Learning
-   - Classification Algorithms
-   - Ensemble Methods
-   - Unsupervised Learning
+
+#### 5. Classification Algorithms
+
+??? note "Topic description"
+
+    **Learning Objective**: Acquire knowledge of various classification algorithms and their application in real-world problems.
+
+    **Related Skills**:
+    
+    - Implementing and evaluating decision tree classifiers
+    - Applying k-nearest neighbors for classification
+    - Understanding the principles of support vector machines
+
+    **Subtopics**:
+
+    - Decision tree classification
+    - K-nearest neighbors (KNN) algorithm
+    - Support vector machines (SVMs)
+    - Evaluating classification models (accuracy, precision, recall, F1-score, ROC-AUC)
+    - Handling class imbalance (oversampling, undersampling, SMOTE)
+
+    **References and Resources**:
+
+    - "Pattern Recognition and Machine Learning" by Christopher Bishop
+    - "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron
+    - Udacity course "Intro to Machine Learning"
+
+
+#### 6. Ensemble Methods
+
+??? note "Topic description"
+
+    **Learning Objective**: Explore ensemble techniques for improving the performance of machine learning models.
+    
+    **Related Skills**:
+    
+    - Implementing random forest algorithms
+    - Understanding the principles of gradient boosting
+    - Applying bagging and boosting techniques to enhance model accuracy
+
+    **Subtopics**:
+    
+    - Random forest classification and regression
+    - Gradient boosting with XGBoost and LightGBM
+    - Bagging and boosting (AdaBoost, Gradient Boosting)
+    - Hyperparameter tuning for ensemble methods
+    - Feature importance and interpretation in ensemble models
+
+    **References and Resources**:
+
+    - "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron
+    - "Introduction to Statistical Learning" by Gareth James et al.
+    - Kaggle micro-course on "Advanced Ensembling"
+
+
+#### 7. Unsupervised Learning
+
+??? note "Topic description"
+
+    **Learning Objective**: Gain proficiency in unsupervised learning techniques for data exploration and pattern discovery.
+
+    **Related Skills**:
+    
+    - Implementing K-means clustering algorithms
+    - Applying principal component analysis (PCA) for dimensionality reduction
+    - Identifying anomalies and outliers in data
+
+    **Subtopics**:
+
+    - K-means clustering
+    - Hierarchical clustering
+    - Principal component analysis (PCA)
+    - Anomaly detection techniques (Isolation Forest, One-Class SVM)
+    - Dimensionality reduction methods (t-SNE, UMAP)
+
+    **References and Resources**:
+
+    - "Pattern Recognition and Machine Learning" by Christopher Bishop
+    - "Hands-On Unsupervised Learning Using Python" by Ankur Patel
+    - Coursera course "Cluster Analysis in Data Mining" by University of Illinois
+
 
 ### D: Deep Learning
-   - Introduction to Deep Learning
-   - Recurrent Neural Networks and Sequence Models
-   - Generative Models
-   - Transfer Learning and Fine-tuning
+
+#### 8. Introduction to Deep Learning
+
+
+#### 9. Recurrent Neural Networks and Sequence Models
+
+
+#### 10. Generative Models
+
+
+#### 11. Transfer Learning and Fine-tuning
+
 
 ### E: Continuous Development / Continuous Integration 
-   - Model Deployment and Productionization
+
+#### 12. Model Deployment and Productionization