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Show your ID card.
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What did you learn from this project/course?
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3
What is Machine Learning?
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Difference between Machine Learning and Data Science.
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Difference between Machine Learning and Deep Learning.
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What are the types of Machine Learning?
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Explain Supervised Learning.
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Explain Unsupervised Learning.
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Explain Semi-Supervised Learning.
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Explain Reinforcement Learning.
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Have you implemented Reinforcement Learning anywhere?
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Name three Unsupervised Learning algorithms.
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Explain your problem statement.
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Explain your notebook/project.
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How did you approach the problem?
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What preprocessing steps did you perform?
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How did you handle missing values?
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Did you detect outliers?
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How did you handle outliers?
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What other outlier detection methods do you know besides IQR?
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Explain your feature engineering.
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Explain your Pipeline and ColumnTransformer.
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Explain the charts you created.
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What insights would you give to the business from your analysis?
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What is Correlation?
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Which correlation method did you use?
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Why did you choose Pearson Correlation?
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Why did you select only a few features for the Correlation Matrix?
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Explain LightGBM.
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What does "Light" mean in LightGBM?
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Difference between XGBoost and LightGBM.
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Explain Random Forest.
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Why did you use Random Forest?
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What does "Forest" mean in Random Forest?
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Difference between Random Forest and XGBoost.
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What is n_estimators?
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How does the learning rate affect training?
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What happens if the learning rate changes from 0.1 to 0.01?
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Explain SVM.
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How does SVM work?
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Explain KNN.
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Explain K-Means.
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How does K-Means work?
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Difference between K-Means and K-Means++.
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What are the steps of the K-Means algorithm?
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What is PCA?
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How does PCA work?
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Is PCA supervised or unsupervised?
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How does PCA reduce features?
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How does PCA affect model performance?
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What is the optimal number of principal components?
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What are tree-based algorithms?
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How is the root node selected in a Decision Tree?
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What is Entropy?
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What is Information Gain?
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Relationship between Entropy and Information Gain.
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What is Gini Impurity?
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What is a pure node?
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What is an impure node?
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What is the height of a Decision Tree?
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What is the depth of a Decision Tree?
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Difference between a Tree and a Graph.
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What is Overfitting?
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How do you prevent Overfitting?
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What is Regularization?
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How does Regularization work?
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Explain L1 and L2 Regularization.
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What is Gradient Descent?
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Difference between Batch Gradient Descent and Stochastic Gradient Descent.
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What is the Loss Function?
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Loss function of Logistic Regression.
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Loss function of Random Forest.
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Loss function of Ridge Regression.
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Why is Mean Squared Error (MSE) used?
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What is R² Score?
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What is Precision?
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What is Recall?
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What is F1-Score?
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What is F1 Macro?
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Write the formula for F1-Score.
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What is a Confusion Matrix?
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What is TF-IDF?
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Write the TF-IDF formula.
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What are Activation Functions?
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Name different Activation Functions.
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What is ReLU?
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What is the input and output range of ReLU?
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How many hidden layers can a Neural Network have?
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What happens if the number of hidden layers increases?
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What are optimization functions in Neural Networks?
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What are different types of Encoders?
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Why is StandardScaler used?
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What is n_jobs?
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Coding: Create a dataset using random numbers.
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Coding: Create a random dataset with specified dimensions.
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Coding: Load the Digits dataset.
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Coding: Print the size/dimensions of the Digits dataset.
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Coding: Print the output classes of the Digits dataset.
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Coding: Apply K-Means and find the optimal number of clusters.
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Coding: Plot the Elbow Curve.
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Coding: Apply SVM on the Digits dataset.
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Coding: Apply a Decision Tree on the Iris dataset.
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Coding: Plot the Decision Tree.
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Coding: Load the Wine dataset.
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105
Coding: Split the dataset into training and testing sets.
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Coding: Train a Random Forest model.
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Coding: Find the model accuracy.
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108
Coding: Apply PCA before training.
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Coding: Compare accuracy and training time before and after PCA.
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110
Coding: Plot Accuracy vs. Number of Principal Components.
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111
Coding: Explain the PCA performance graph.
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112
Coding: Load a news article and generate the TF-IDF matrix.
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113
Coding: Stack a dataset using hstack.
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114
Coding: Use mutual_info_regression to find the top 5 features.