🧩 Part 1/12

✔ What is Machine Learning and Why Do We Need Data?
✔ What is Active Learning and How Does It Work?
✔ The Active Learning Cycle
✔ Types of Active Learning Queries
✔ Benefits and Challenges of Active Learning
✔ Applications and Examples of Active Learning
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🧩 Part 2/12

✔ Active Learning Scenarios
✔ Pool-based Active Learning
✔ Stream-based Active Learning
✔ Membership Query Synthesis
✔ Query Strategies
✔ Uncertainty Sampling
✔ Query by Committee
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🧩 Part 3/12

✔ What is Active Learning and Why Use It?
✔ Active Learning Workflow with Python
✔ Load and Preprocess the Data
✔ Define the Model and the Query Strategy
✔ Initialize the Learner and the Labeler
✔ Run the Active Learning Loop
✔ Evaluate the Performance and Visualize the Results
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🧩 Part 4/12

✔ Active Learning for Text Classification
✔ What is Active Learning?
✔ Why Use Active Learning for Text Classification?
✔ How to Implement Active Learning for Text Classification?
✔ A Case Study: Sentiment Analysis of Movie Reviews
✔ Data Collection and Preprocessing
✔ Feature Extraction using TF-IDF
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🧩 Part 5/12

✔ Active Learning: What, Why, and How
✔ What is Active Learning?
✔ Why Use Active Learning for Image Classification?
✔ How to Implement Active Learning?
✔ Image Classification: A Computer Vision Task
✔ What is Image Classification?
✔ How to Preprocess Images for Classification?
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🧩 Part 6/12

✔ Active Learning for Regression
✔ What is Active Learning?
✔ Why Active Learning for Regression?
✔ How to Implement Active Learning for Regression?
✔ Case Study: Predicting House Prices
✔ Data Description and Preprocessing
✔ Linear Regression Model
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🧩 Part 7/12

✔ Active Learning for Clustering
✔ What is Active Learning?
✔ Why Use Active Learning for Clustering?
✔ How to Implement Active Learning for Clustering?
✔ Case Study: Clustering Customer Segments
✔ Data Description and Visualization
✔ K-means Clustering
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🧩 Part 8/12

✔ What is Anomaly Detection and Why is it Important?
✔ What is Active Learning and How Does it Work?
✔ A Case Study: Outlier Detection on a Credit Card Fraud Dataset
✔ Data Exploration and Preprocessing
✔ Applying Outlier Detection Methods: Isolation Forest and Local Outlier Factor
✔ Evaluating the Results and Selecting the Most Informative Samples
✔ Iterating the Process and Improving the Model Performance
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🧩 Part 9/12

✔ Background
✔ Reinforcement Learning
✔ Active Learning
✔ Sequential Decision Making
✔ Methodology
✔ Problem Formulation
✔ Active Learning for Reinforcement Learning
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🧩 Part 10/12

✔ Background and Related Work
✔ Active Learning
✔ Meta-Learning
✔ Learning to Learn
✔ Methodology
✔ Problem Formulation
✔ Active Learning for Meta-Learning Algorithm
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🧩 Part 11/12

✔ What is Active Learning and Why is it Useful?
✔ Active Learning Challenges
✔ Labeling Cost
✔ Labeling Quality
✔ Cold Start
✔ Diversity
✔ Active Learning Limitations
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🧩 Part 12/12

✔ Active Learning: Definition, Benefits, and Challenges
✔ Active Learning Research Trends and Open Problems
✔ Query Strategies and Sampling Methods
✔ Active Learning for Complex and Structured Data
✔ Active Learning with Human-in-the-Loop and Explainability
✔ Active Learning Resources: Datasets, Tools, and Papers
✔ Datasets for Active Learning Experiments and Benchmarks
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