Machine Learning
Course Content
Session -1
Artificial Intelligence (AI), Machine Learning (ML), and Traditional Programming
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Real-World Applications of Machine Learning
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How Netflix Uses ML for Recommendations
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How ML Helps in Spam Filtering
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How ML Is Used in Self-Driving Cars and Medical Diagnosis
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Types of Machine Learning
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Main Steps in the ML Workflow
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What Is Hugging Face Model Hub?
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How Does Machine Learning Improve Over Time?
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Session -2
1. What are the different types of data used in Machine Learning?
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2. What are some common data preprocessing techniques?
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3. How do you handle missing values?
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4. How do you remove duplicates and outliers?
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5. What is Feature Engineering?
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6. What are encoding techniques for categorical variables?
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What is feature scaling, and why is it necessary?
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8. How do you select important features?
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9. How does Hugging Face Datasets Library help in Machine Learning?
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Session-3
1. Why is it important to split data into training, testing, and validation sets?
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2. What is overfitting and underfitting in ML?
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3. How does hyperparameter tuning improve ML models?
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4. What is Tokenization in NLP, and how do Hugging Face Tokenizers work?
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5. How can I avoid common mistakes when splitting my dataset?
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Session 4: Supervised Learning – Regression & Classification
1. What is Supervised Learning, and how does it work?
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2. What is the difference between Regression and Classification?
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3. How does Linear Regression work?
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4. What are some real-world applications of Linear Regression?
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5. What is Logistic Regression, and how is it used in spam detection?
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6. How can you train a Linear Regression & Logistic Regression model using Scikit-Learn?
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7. How does Hugging Face perform Sentiment Analysis?
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Session 5: Unsupervised Learning – Clustering & Pattern Recognition
1. What is Unsupervised Learning, and how does it differ from Supervised Learning?
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2. What is Clustering in ML? Provide an example.
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3. How does K-Means Clustering work?
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4. What are some real-life applications of K-Means Clustering?
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5. How does Hugging Face’s Named Entity Recognition (NER) Work?
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Session 6: How to Evaluate ML Models?
1. What is Model Evaluation, and why is it important?
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2. What is the difference between Overfitting and Underfitting?
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3. What is train-test split, and how does cross-validation help?
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4. What are the key evaluation metrics used in ML?
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5. How can you evaluate a trained model’s accuracy?
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6. How does Hugging Face’s Trainer API help in fine-tuning Text Classification Models?
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Session 7: Model Improvement & Advanced ML Techniques
1. What are some techniques to improve model performance?
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2. What is GridSearchCV, and how does it help in Hyperparameter Tuning?
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3. What is the difference between Bagging and Boosting?
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4. How does Random Forest work?
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5. What is XGBoost, and why is it popular?
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6. How does Hugging Face’s Question-Answering Model work?
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Session 8: Deploying Machine Learning Models
1. What is Model Deployment, and why is it necessary?
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2. What are different ML deployment methods?
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3. How can you deploy an ML model using Streamlit?
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4. How does Hugging Face Spaces help in deploying Transformer-based NLP models?
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Advanced Techniques for Hyperparameter Tuning
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1. Bayesian Optimization
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2. Tree-structured Parzen Estimators (TPE)
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3. Hyperband
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4. Population-Based Training (PBT)
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5. BOHB (Bayesian Optimization and Hyperband)
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6. Optuna
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Ray Tune
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8. Keras Tuner
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9. SageMaker Automatic Model Tuning
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Summary of Advanced Techniques
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Important References
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