Basics of Data Analytics, Artificial Intelligence, and LLM Models

What is descriptive analytics?

• Descriptive analytics is the process of collecting data from various sources.

• Descriptive analytics is predicting future outcomes based on historical data.

• Descriptive analytics is the analysis of real-time data to make immediate decisions.

• Descriptive analytics is the analysis of historical data to understand past trends and insights.

Explain the concept of machine learning.

• Machine learning is the development of algorithms that allow computers to learn from and make predictions based on data.

• Machine learning is the process of teaching computers to think like humans.

• Machine learning is the study of mechanical systems and robotics.

• Machine learning is the practice of memorizing data for future use.

What are neural networks and how are they used in AI?

• Neural networks are used in AI to predict the weather and control traffic lights.

• Neural networks are used in AI to play video games and solve puzzles.

• Neural networks are used in AI to learn complex patterns and relationships in data, enabling tasks like image recognition and natural language processing.

• Neural networks are used in AI to bake cookies and write poetry.

Define predictive modeling and provide an example.

• An example of predictive modeling is using astrology to predict stock market trends.

• Predictive modeling involves using only qualitative data to make predictions.

• Predictive modeling is the process of guessing outcomes without data.

• Predictive modeling is the process of using data and statistical algorithms to predict outcomes. An example is using customer purchase history to predict future purchases.

How is natural language processing (NLP) applied in data analytics?

• NLP is used to analyze audio data in data analytics

• NLP is used to predict stock market trends in data analytics

• NLP is used to process and analyze text data, enabling tasks such as sentiment analysis, language translation, and information extraction in data analytics.

• NLP is used to create visualizations in data analytics

What is deep learning and how does it differ from traditional machine learning?

• Deep learning uses decision trees for classification, while traditional machine learning uses neural networks.

• Deep learning uses neural networks with multiple layers to automatically learn representations from data, while traditional machine learning relies on handcrafted features.

• Deep learning does not involve feature extraction, unlike traditional machine learning.

• Deep learning relies on unsupervised learning, while traditional machine learning relies on supervised learning.

What is the main goal of descriptive analytics?

• To create interactive data visualizations

• To predict future outcomes based on historical data

• To analyze real-time data streams

• To summarize and describe historical data

What are the different types of machine learning algorithms?

• Supervised learning, Unsupervised learning, Reinforcement learning

• Feature learning

• Semi-supervised learning

• Deep learning

How do neural networks mimic the human brain?

• Using a single node with no connections.

• Using a linear regression model.

• Using a decision tree algorithm.

• Using interconnected nodes (neurons) with weighted connections.

Give an example of a predictive modeling technique.

• Linear Regression

• K-Means Clustering

• Decision Tree

• Logistic Regression