Machine Learning

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Course Content

Session -1

Artificial Intelligence (AI), Machine Learning (ML), and Traditional Programming

Real-World Applications of Machine Learning

How Netflix Uses ML for Recommendations

How ML Helps in Spam Filtering

How ML Is Used in Self-Driving Cars and Medical Diagnosis

Types of Machine Learning

Main Steps in the ML Workflow

What Is Hugging Face Model Hub?

How Does Machine Learning Improve Over Time?

Session -2

1. What are the different types of data used in Machine Learning?

2. What are some common data preprocessing techniques?

3. How do you handle missing values?

4. How do you remove duplicates and outliers?

5. What is Feature Engineering?

6. What are encoding techniques for categorical variables?

What is feature scaling, and why is it necessary?

8. How do you select important features?

9. How does Hugging Face Datasets Library help in Machine Learning?

Session-3

1. Why is it important to split data into training, testing, and validation sets?

2. What is overfitting and underfitting in ML?

3. How does hyperparameter tuning improve ML models?

4. What is Tokenization in NLP, and how do Hugging Face Tokenizers work?

5. How can I avoid common mistakes when splitting my dataset?

Session 4: Supervised Learning – Regression & Classification

1. What is Supervised Learning, and how does it work?

2. What is the difference between Regression and Classification?

3. How does Linear Regression work?

4. What are some real-world applications of Linear Regression?

5. What is Logistic Regression, and how is it used in spam detection?

6. How can you train a Linear Regression & Logistic Regression model using Scikit-Learn?

7. How does Hugging Face perform Sentiment Analysis?

Session 5: Unsupervised Learning – Clustering & Pattern Recognition

1. What is Unsupervised Learning, and how does it differ from Supervised Learning?

2. What is Clustering in ML? Provide an example.

3. How does K-Means Clustering work?

4. What are some real-life applications of K-Means Clustering?

5. How does Hugging Face’s Named Entity Recognition (NER) Work?

Session 6: How to Evaluate ML Models?

1. What is Model Evaluation, and why is it important?

2. What is the difference between Overfitting and Underfitting?

3. What is train-test split, and how does cross-validation help?

4. What are the key evaluation metrics used in ML?

5. How can you evaluate a trained model’s accuracy?

6. How does Hugging Face’s Trainer API help in fine-tuning Text Classification Models?

Session 7: Model Improvement & Advanced ML Techniques

1. What are some techniques to improve model performance?

2. What is GridSearchCV, and how does it help in Hyperparameter Tuning?

3. What is the difference between Bagging and Boosting?

4. How does Random Forest work?

5. What is XGBoost, and why is it popular?

6. How does Hugging Face’s Question-Answering Model work?

Session 8: Deploying Machine Learning Models

1. What is Model Deployment, and why is it necessary?

2. What are different ML deployment methods?

3. How can you deploy an ML model using Streamlit?

4. How does Hugging Face Spaces help in deploying Transformer-based NLP models?

Advanced Techniques for Hyperparameter Tuning

1. Bayesian Optimization

2. Tree-structured Parzen Estimators (TPE)

4. Population-Based Training (PBT)

5. BOHB (Bayesian Optimization and Hyperband)

9. SageMaker Automatic Model Tuning

Summary of Advanced Techniques

Important References

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