Practical Data Science using Python - Machine Learning Terminology

The video tutorial explains the differences between traditional programming and machine learning, highlighting how machine learning models learn from data rather than predefined rules. It covers related terms like AI, deep learning, and data science, and discusses various machine learning algorithms and use cases. The tutorial also delves into neural networks and deep learning, explaining their architecture and applications. It distinguishes between data science and machine learning, and concludes with an overview of big data and its relevance to AI.

What is the key difference between traditional programming and machine learning?

How do machine learning models learn from data?

What role do algorithms play in machine learning?

Explain the concept of deep learning and how it relates to neural networks.

What are some common types of machine learning algorithms?

How does data science differ from machine learning?

What is big data and how is it related to machine learning?

Describe the structure of a neural network.

What are activation functions in neural networks?

How do exploratory data analysis techniques contribute to data science?