TCS Data Scientist Interview Questions, Process, and Tips

Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.

• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more

Supervised learning algorithms are used in machine learning to predict outcomes based on labeled training data.

• Supervised learning algorithms require labeled training data to learn the relationship between input and output variables.

• Common supervised learning algorithms include linear regression, logistic regression, decision trees, support vector machines, and neural networks.

• These algorithms are used for tasks such a...read more

Unsupervised learning algorithms are used to find patterns in data without labeled outcomes.

• Unsupervised learning algorithms do not require labeled data for training.

• They are used for clustering, dimensionality reduction, and anomaly detection.

• Examples include K-means clustering, hierarchical clustering, and principal component analysis.

Cosine similarity measures the similarity between two non-zero vectors in an inner product space.

• Cosine similarity ranges from -1 to 1, with 1 indicating identical vectors and -1 indicating opposite vectors.

• It is commonly used in information retrieval, text mining, and recommendation systems.

• Formula: cos(theta) = (A . B) / (||A|| * ||B||)

• Example: Calculating similarity between two documents based on their word frequenc

Recall is the ratio of correctly predicted positive observations to the all observations in actual class, while precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is about the actual positive instances that were correctly identified by the model.

• Precision is about the predicted positive instances and how many of them were actually positive.

• Recall = Tr...read more

Stop words are common words like 'the', 'is', 'and' that are removed from text data to improve analysis.

• Stop words are commonly removed from text data to improve the accuracy of natural language processing tasks.

• They are typically removed before tokenization and can be done using libraries like NLTK or spaCy.

• Examples of stop words include 'the', 'is', 'and', 'in', 'on', etc.

Confusion matrix is a table used to evaluate the performance of a classification model.

• It is a 2x2 matrix that shows the counts of true positive, true negative, false positive, and false negative predictions.

• It is used to calculate metrics like accuracy, precision, recall, and F1 score.

• Example: TP=100, TN=50, FP=10, FN=5.

Similarity matrix algo is a method to quantify the similarity between data points in a dataset.

• It calculates the similarity between each pair of data points in a dataset and represents it in a matrix form.

• Common similarity measures used include cosine similarity, Euclidean distance, and Jaccard similarity.

• The diagonal of the matrix usually contains 1s as each data point is perfectly similar to itself.

• The values in the ...read more