day 1 AIML

Which type of Machine Learning primarily deals with unlabeled data to find hidden patterns and intrinsic structure, such as grouping data points into clusters?

• Supervised Learning

• UnSupervised Learning

• Semi Supervised Learning

• Reinforcement learning

What is the key technological characteristic that distinguishes Deep Learning (DL) from traditional Machine Learning (ML)?

• DL models require significantly less data to achieve high performance compared to ML models.

• DL is primarily focused on solving regression problems, while ML is for classification.

• DL models use multi-layered Artificial Neural Networks to automatically perform feature engineering.

• DL algorithms are inherently unsupervised, while ML algorithms are always supervised.

Which of the following describes the field of Data Science (DS) relative to Data Analysis (DA) and Machine Learning (ML)?

• DS is an overarching, multidisciplinary field that utilizes DA for descriptive insights and ML for predictive modeling.

• DS is the final step in a pipeline, only performed after DA and ML have been completed.

• DS is a niche area focused only on experimental design and A/B testing.

• DS is solely concerned with building predictive models using ML algorithms.

Which of the following is an example of a classification task using a supervised learning algorithm?

• Reducing the number of variables in a dataset while retaining most of the information.

• Determining if an email is 'Spam' or 'Not Spam' based on its content.

• Predicting the housing price (a dollar value) of a house based on its square footage.

• Grouping a set of customer transactions into different types of fraud patterns.

In a Pandas DataFrame, which method is specifically recommended for indexing and selecting data based on integer position (row/column number)?

• [] (standard indexing)

• .iloc[]

• .head()

• .loc[]

You have a $DataFrame$ of sales data. You want to calculate the mean sales for each distinct 'Region'. Which combination of Pandas methods is most efficient for this task?

• .groupby('Region')['Sales'].mean()

• .merge(other_df)

• .pivot_table(index='Region', values='Sales')

• .apply(np.mean)

A Pandas $DataFrame$ contains numerous missing values (NaN). Which Pandas method is used to fill these missing values with a specified constant, or a calculated value like the mean of the column?

• df.fillna()

• 
  df.replace()

• df.dropna()

• df.drop()

What is the fundamental difference in output between Linear Regression and Logistic Regression?

• Linear Regression predicts a discrete category (like 1 or 0), while Logistic Regression predicts a continuous value.

• Linear Regression is only for $2D$ data, while Logistic Regression can handle $N$-dimensional data.

• Linear Regression predicts a continuous numerical value, while Logistic Regression predicts a probability (a value between 0 and 1) of belonging to a discrete class.

• Linear Regression uses a line, while Logistic Regression uses only curves.