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Machine Learning
Machine Learning with Python
1. Foundations of Machine Learning and Python
2. Core Python Libraries for Data Science
3. Machine Learning Workflow with Scikit-Learn
4. Supervised Learning Algorithms
5. Unsupervised Learning Algorithms
6. Introduction to Deep Learning
7. Deep Learning with Python Frameworks
8. Advanced Topics and Applications
9. Model Deployment and MLOps
Machine Learning Workflow with Scikit-Learn
Scikit-Learn Overview
Library Architecture
Estimator Interface
Transformer Interface
Predictor Interface
API Consistency
Fit Method
Transform Method
Predict Method
Data Representation
Feature Matrices
Target Vectors
Sparse Matrices
Data Preprocessing
Handling Missing Data
Missing Data Detection
Imputation Strategies
Simple Imputation
Iterative Imputation
KNN Imputation
Categorical Data Encoding
One-Hot Encoding
Label Encoding
Ordinal Encoding
Target Encoding
Feature Scaling
Standardization
StandardScaler
RobustScaler
Normalization
MinMaxScaler
MaxAbsScaler
Unit Vector Scaling
Data Splitting
Train-Test Split
Stratified Splitting
Time Series Splitting
Cross-Validation Splits
Feature Engineering
Feature Creation
Polynomial Features
Interaction Features
Mathematical Transformations
Domain-Specific Features
Feature Selection
Filter Methods
Univariate Selection
Correlation-based Selection
Variance Threshold
Wrapper Methods
Recursive Feature Elimination
Sequential Feature Selection
Embedded Methods
L1 Regularization
Tree-based Importance
Dimensionality Reduction
Linear Methods
Principal Component Analysis
Linear Discriminant Analysis
Factor Analysis
Non-linear Methods
Kernel PCA
Manifold Learning
Model Training and Evaluation
Model Training Process
Fitting Models
Parameter Learning
Convergence Monitoring
Model Evaluation Metrics
Classification Metrics
Accuracy
Precision
Recall
F1-Score
ROC-AUC
Precision-Recall AUC
Confusion Matrix
Classification Report
Regression Metrics
Mean Absolute Error
Mean Squared Error
Root Mean Squared Error
R-squared
Adjusted R-squared
Mean Absolute Percentage Error
Model Validation
Holdout Validation
Cross-Validation
K-Fold Cross-Validation
Stratified K-Fold
Leave-One-Out
Time Series Cross-Validation
Bootstrap Validation
Model Optimization
Hyperparameter Tuning
Grid Search
Exhaustive Search
Parameter Grids
Random Search
Random Sampling
Search Distributions
Bayesian Optimization
Gaussian Processes
Acquisition Functions
Model Selection
Cross-Validation Scoring
Model Comparison
Statistical Testing
Pipeline Construction
Creating Pipelines
Pipeline Components
Nested Pipelines
Pipeline Optimization
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2. Core Python Libraries for Data Science
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4. Supervised Learning Algorithms