list of machine learning questions categorized by chapter. Here’s an example of how it might look:
| Chapter | Topic | Question |
|---|---|---|
| 1. Introduction to ML | Basics of Machine Learning | What is Machine Learning? |
| What are the different types of Machine Learning? | ||
| What is the difference between supervised, unsupervised, and reinforcement learning? | ||
| What are some real-world applications of Machine Learning? | ||
| What is the role of data in Machine Learning? | ||
| 2. Data Preprocessing | Data Cleaning | Why is data preprocessing important in Machine Learning? |
| What are the common techniques for handling missing data? | ||
| How do you handle categorical data? | ||
| What is feature scaling and why is it important? | ||
| 3. Supervised Learning | Regression | What is linear regression and how does it work? |
| Explain the concept of the cost function in linear regression. | ||
| What is overfitting and how can it be prevented? | ||
| How does logistic regression differ from linear regression? | ||
| Classification | What are the main algorithms used for classification? | |
| Explain the concept of a decision tree. | ||
| What is a confusion matrix? | ||
| How do you evaluate the performance of a classification model? | ||
| 4. Unsupervised Learning | Clustering | What is clustering in Machine Learning? |
| Explain the K-means clustering algorithm. | ||
| What is hierarchical clustering? | ||
| How do you determine the optimal number of clusters? | ||
| Dimensionality Reduction | What is the purpose of dimensionality reduction? | |
| Explain Principal Component Analysis (PCA). | ||
| What is t-SNE and how is it different from PCA? | ||
| 5. Model Evaluation | Model Selection | What is cross-validation and why is it important? |
| Explain the bias-variance tradeoff. | ||
| What are some common metrics for evaluating regression models? | ||
| What are some common metrics for evaluating classification models? | ||
| 6. Advanced Topics | Ensemble Learning | What is ensemble learning? |
| Explain the concept of bagging and boosting. | ||
| What is a random forest? | ||
| How does gradient boosting work? | ||
| Neural Networks | What is a neural network and how does it work? | |
| Explain the backpropagation algorithm. | ||
| What are the different types of neural networks? | ||
| What is the role of activation functions in neural networks? | ||
| 7. Practical Applications | Real-world Applications | How is Machine Learning used in healthcare? |
| What are the applications of Machine Learning in finance? | ||
| How does Machine Learning contribute to the field of autonomous vehicles? | ||
| What are the ethical considerations in the use of Machine Learning? |
Feel free to adjust or expand on this structure based on specific chapters or topics you are interested in.