Practical Data Science using Python - Challenges in Machine Learning

The video discusses the challenges in setting up successful machine learning systems, focusing on data and algorithmic issues. It covers data availability, quality, non-representative data, imbalanced datasets, unnecessary features, and dimensionality reduction. The video also addresses algorithmic challenges like overfitting and underfitting, and how regularization can help mitigate these issues.

What are the two main components of machine learning?

• Features and Labels

• Data and Algorithms

• Models and Predictions

• Training and Testing

Why is data availability a challenge in machine learning?

• Data is always representative of the problem

• Data is too easy to obtain

• Organizations often lack the discipline to collect necessary data

• Data is always perfect and complete

What is a common issue with data quality in machine learning?

• Data is always accurate

• Data often contains missing values and outliers

• Data is always representative

• Data is always complete

What is the consequence of using non-representative data in training?

• The model will generalize perfectly

• The model will require no further training

• The model will be flawed or inaccurate

• The model will be highly accurate

What is a classic case of imbalanced data?

• Data is perfectly balanced

• Equal distribution of classes

• One class is significantly underrepresented

• All classes are overrepresented

Why is it important to remove unnecessary features?

• To make the learning process lighter and more efficient

• To add more noise to the data

• To ensure all data is used

• To increase the complexity of the model

What is the purpose of dimensionality reduction?

• To eliminate all features

• To add more features to the dataset

• To reduce the number of features while retaining important information

• To increase the number of features

What is overfitting in machine learning?

• When a model is too simple

• When a model memorizes the training data too closely

• When a model ignores the training data

• When a model performs well on unseen data

How can overfitting be addressed?

• By ignoring the training data

• By using more complex algorithms

• By applying regularization techniques

• By reducing the amount of data

What is underfitting in machine learning?

• When a model is too complex

• When a model cannot learn from the training data

• When a model performs perfectly on training data

• When a model overgeneralizes