One Pager Cheat Sheet

• By reviewing common ML questions and topics, you can prepare yourself for a successful Machine Learning job interview.
• AI is the broadest term of the three, covering everything related to making machines smart, while ML and DL are subsets of AI which can learn to make decisions and mimic the processing patterns of the human brain using artificial neural networks respectively.
• Machine learning algorithms can be classified into three distinct categories: Supervised, Unsupervised, and Reinforcement.
• Classification is used when the target variable is categorical, and Regression is used when the target variable is continuous.
• Supervised Learning models such as Logistic Regression, Linear Regression and Decision Tree as well as Random Forest are used for classification and regression, while the K-means Clustering and Apriori Algorithm are used for Unsupervised Learning tasks.
• Supervised learning algorithms such as Logistic Regression, Linear Regression, Decision Tree and Random Forest are used to model linear relationships between input and output variables.
• The important steps involved in Machine Learning are Data collection, Data preparation, Model selection, Model training, Model evaluation, Parameter tuning and Making predictions.
• Data is cleaned, visualized, split, and prepared in the data preparation step.
• A confusion matrix is a performance measurement for a classification problem that depicts the number of true positives, true negatives, false positives, and false negatives.
• A false negative is when a negative prediction is actually positive, resulting in a misclassification of the positive instance.
• The ROC Curve is used to plot the True Positive Rate (TPR) against the False Positive Rate (FPR) to determine the optimal threshold to separate the classes.
• We can evaluate our classification model's performance using popular metrics such as Precision, Recall, Accuracy, and AUC.
• We should use a validation set to check if our model is overfitting or underfitting.
• Overfitting occurs when a model learns too many details from the training data, resulting in poor generalization with unseen data.