Exploring Machine Learning

What are the three main types of machine learning?

• Transfer learning

• Active learning

• Semi-supervised learning

• Supervised learning, Unsupervised learning, Reinforcement learning

Can you name a common application of machine learning in daily life?

• Weather forecasting

• Recommendation systems

• Data storage

• Image editing

What is the key difference between supervised and unsupervised learning?

• Unsupervised learning uses more complex algorithms than supervised learning.

• Supervised learning can only be applied to classification tasks.

• The key difference is that supervised learning requires labeled data, whereas unsupervised learning does not.

• Supervised learning is faster than unsupervised learning.

Why is data preprocessing important in machine learning?

• Data preprocessing is unnecessary for model training.

• Data preprocessing is important because it improves model accuracy and performance by cleaning and transforming raw data.

• Data preprocessing is only relevant for supervised learning.

• Data preprocessing only increases the size of the dataset.

What ethical concerns are associated with machine learning?

• Increased computational power

• Bias, transparency, privacy, accountability, and job displacement.

• Efficiency in data processing

• Enhanced user experience

How can machine learning improve healthcare services?

• Machine learning has no impact on patient outcomes.

• Machine learning can replace all healthcare professionals.

• Machine learning only focuses on administrative tasks in healthcare.

• Machine learning improves healthcare services by enabling predictive analytics, personalized medicine, and operational efficiency.

What is an example of unsupervised learning?

• Decision tree

• Support vector machine

• Linear regression

• K-means clustering

What role does data quality play in machine learning?

• Data quality ensures accurate, reliable, and relevant information for training machine learning models.

• Data quality is irrelevant to machine learning performance.

• High data quality guarantees perfect model predictions.

• Data quality only affects the speed of model training.

How might machine learning change in the next decade?

• Machine learning will focus solely on increasing profits for companies.

• Machine learning will become more efficient, interpretable, and widely applicable, with a focus on ethical standards.

• Machine learning will become less efficient and more complex.

• Machine learning will only be used in gaming and entertainment.

What is a potential risk of using machine learning in decision-making?

• Enhanced data visualization capabilities.

• Increased accuracy in predictions.

• Faster decision-making processes.

• Bias in algorithms leading to unfair outcomes.