دوره مقدماتی کتابخانه Scikit Learn
Chapter 1: Introduction to Scikit-Learn
Lesson 1: What is Scikit-Learn?
Lesson 2: History and Importance of Scikit-Learn
Lesson 3: Installing Scikit-Learn (Windows, macOS, Linux)
Lesson 4: Importing and Using Scikit-Learn
Lesson 5: Overview of Machine Learning Concepts
Chapter 2: Understanding Data in Scikit-Learn
Lesson 1: Types of Datasets (Structured vs. Unstructured)
Lesson 2: Loading Datasets in Scikit-Learn
Lesson 3: Understanding Data Formats (NumPy, Pandas, CSV, etc.)
Lesson 4: Exploring and Visualizing Data
Lesson 5: Handling Missing Values
Chapter 3: Data Preprocessing Techniques
Lesson 1: Feature Scaling (Normalization & Standardization)
Lesson 2: Handling Categorical Data (One-Hot Encoding, Label Encoding)
Lesson 3: Dealing with Missing Data (Imputation)
Lesson 4: Feature Engineering Basics
Lesson 5: Splitting Data into Training and Testing Sets
Chapter 4: Supervised Learning | Regression Models
Lesson 1: Introduction to Regression Models
Lesson 2: Simple Linear Regression
Lesson 3: Multiple Linear Regression
Lesson 4: Polynomial Regression
Lesson 5: Evaluating Regression Models
Chapter 5: Supervised Learning | Classification Models
Lesson 1: Introduction to Classification Models
Lesson 2: Logistic Regression
Lesson 3: Decision Trees for Classification
Lesson 4: Support Vector Machines (SVM)
Lesson 5: Evaluating Classification Models
Chapter 6: Model Evaluation and Validation
Lesson 1: Understanding Overfitting and Underfitting
Lesson 2: Cross-Validation Techniques
Lesson 3: Performance Metrics for Regression Models
Lesson 4: Performance Metrics for Classification Models
Lesson 5: Confusion Matrix and ROC Curve
Chapter 7: Unsupervised Learning | Clustering Techniques
Lesson 1: Introduction to Clustering
Lesson 2: K-Means Clustering
Lesson 3: Hierarchical Clustering
Lesson 4: DBSCAN Algorithm
Lesson 5: Evaluating Clustering Models
Chapter 8: Unsupervised Learning | Dimensionality Reduction
Lesson 1: Introduction to Dimensionality Reduction
Lesson 2: Principal Component Analysis (PCA)
Lesson 3: Linear Discriminant Analysis (LDA)
Lesson 4: t-SNE for Data Visualization
Lesson 5: Feature Selection Techniques
Chapter 9: Working with Pipelines in Scikit-Learn
Lesson 1: What are Pipelines?
Lesson 2: Building and Using Pipelines
Lesson 3: Custom Transformers in Pipelines
Lesson 4: Grid Search with Pipelines
Lesson 5: Real-World Applications of Pipelines
Chapter 10: Feature Selection and Engineering
Lesson 1: Importance of Feature Selection
Lesson 2: Univariate Selection Methods
Lesson 3: Recursive Feature Elimination (RFE)
Lesson 4: Feature Importance with Decision Trees
Lesson 5: Handling High-Dimensional Data
Chapter 11: Working with Real-World Datasets
Lesson 1: Handling Large Datasets in Scikit-Learn
Lesson 2: Exploratory Data Analysis (EDA)
Lesson 3: Data Cleaning Strategies
Lesson 4: Creating Custom Data Transformers
Lesson 5: Case Study: Predicting House Prices
Chapter 12: Introduction to Ensemble Learning
Lesson 1: What is Ensemble Learning?
Lesson 2: Bagging Techniques (Random Forest)
Lesson 3: Boosting Techniques (AdaBoost, Gradient Boosting)
Lesson 4: Stacking and Voting Classifiers
Lesson 5: Comparing Ensemble Methods
دوره پیشرفته کتابخانه Scikit Learn
Chapter 1: Advanced Feature Engineering Techniques
Lesson 1: Handling Imbalanced Data
Lesson 2: Advanced Feature Transformation Methods
Lesson 3: Using Polynomial Features
Lesson 4: Interaction Features and Feature Generation
Lesson 5: Feature Selection in High-Dimensional Datasets
Chapter 2: Hyperparameter Tuning and Optimization
Lesson 1: Introduction to Hyperparameter Tuning
Lesson 2: Grid Search vs. Randomized Search
Lesson 3: Bayesian Optimization for Hyperparameter Tuning
Lesson 4: Using Optuna for Model Tuning
Lesson 5: Hyperparameter Tuning Best Practices
Chapter 3: Advanced Regression Techniques
Lesson 1: Ridge and Lasso Regression
Lesson 2: Elastic Net Regression
Lesson 3: Decision Tree Regression
Lesson 4: Random Forest Regression
Lesson 5: Gradient Boosting Regression
Chapter 4: Advanced Classification Models
Lesson 1: K-Nearest Neighbors (KNN)
Lesson 2: Naive Bayes Classifier
Lesson 3: Extreme Gradient Boosting (XGBoost)
Lesson 4: LightGBM and CatBoost Classifiers
Lesson 5: Multi-Label Classification
Chapter 5: Advanced Clustering Techniques
Lesson 1: Spectral Clustering
Lesson 2: Gaussian Mixture Models (GMM)
Lesson 3: Affinity Propagation Clustering
Lesson 4: Mean-Shift Clustering
Lesson 5: Evaluating Clustering Performance
Chapter 6: Handling Time Series Data in Scikit-Learn
Lesson 1: Time Series Data Preparation
Lesson 2: Feature Engineering for Time Series
Lesson 3: Autoregressive Models in Scikit-Learn
Lesson 4: Time Series Forecasting with Machine Learning
Lesson 5: Case Study: Stock Price Prediction
Chapter 7: Anomaly Detection Techniques
Lesson 1: Introduction to Anomaly Detection
Lesson 2: Isolation Forests
Lesson 3: One-Class SVM
Lesson 4: Local Outlier Factor (LOF)
Lesson 5: Real-World Applications of Anomaly Detection
Chapter 8: Model Deployment and Integration
Lesson 1: Saving and Loading Models
Lesson 2: Deploying Models Using Flask
Lesson 3: Deploying Models Using FastAPI
Lesson 4: Integrating with Cloud Platforms (AWS, GCP)
Lesson 5: Automating Model Deployment
Chapter 9: Explainability and Interpretability in ML Models
Lesson 1: Understanding Model Interpretability
Lesson 2: Feature Importance and SHAP Values
Lesson 3: LIME for Model Explanations
Lesson 4: Fairness in Machine Learning Models
Lesson 5: Ethical Considerations in ML
Chapter 10: Scikit-Learn with Deep Learning
Lesson 1: Combining Scikit-Learn with TensorFlow
Lesson 2: Feature Extraction for Neural Networks
Lesson 3: Hybrid Models (ML + DL)
Lesson 4: Using Scikit-Learn in PyTorch Pipelines
Lesson 5: Case Study: Deep Learning with Scikit-Learn
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