Worksheetsday 1 AIML
Total questions: 8
Worksheet time: 4mins
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.
