Data science vs machine learning vs AI?
Data Science: Data science is the extraction of relevant insights from data. It uses various techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization, pattern recognition and learning, uncertainty modeling, data warehousing, and cloud computing. Data Science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.
Machine Learning: Machine learning is the ability of a computer system to learn from the environment and improve itself from experience without the need for any explicit programming. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Machine learning can be performed using multiple approaches. The three basic models of machine learning are supervised, unsupervised and reinforcement learning.
Artificial Intelligence: Artificial intelligence refers to the simulation of a human brain function by machines. This is achieved by creating an artificial neural network that can show human intelligence. The primary human functions that an AI machine performs include logical reasoning, learning and self-correction. Artificial intelligence is a wide field with many applications but it also one of the most complicated technology to work on. Machines inherently are not smart and to make them so, we need a lot of computing power and data to empower them to simulate human thinking.
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- What features would you use to predict the Uber ETA for ride requests?
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- Error vs variance vs bias?
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- Describe your data science process start to finish?
- How would you find correlation between a categorical variable and a continuous variable?
- How do you treat null/missing values? Name 3 methodologies.
- How can outlier values be treated?
- What is data normalization? Name 2 normalization methodologies.
- What is the role/importance of data cleaning?
- What are success metrics vs tracking metrics?
- What kind of metric would you make to measure success of a program (marketing) and how do you define them?
- Let's say an app was getting a redesign. How do you know if the redesign was successful?
- We noticed a steep decline in users in a certain area of the world, how would you address/asses?
- What are the two methods used for the calibration in Supervised Learning?
- Which method is frequently used to prevent overfitting?
- What is the difference between heuristic for rule learning and heuristics for decision trees?
- What is Perceptron in Machine Learning?
- Explain the two components of Bayesian logic program?
- What are Bayesian Networks (BN) ?
- Why instance based learning algorithm sometimes referred as Lazy learning algorithm?