Introduction to Data Science and AI Course

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Questions and Answers

What are the main types of Machine Learning?

Descriptive, Predictive, Prescriptive Learning
Bayesian, Clustering, Decision Tree
Supervised, Unsupervised, Reinforcement Learning (correct)
Linear, Logistic, Polynomial Regression

What distinguishes Classification from Regression problems in Machine Learning?

Classification predicts categories, while Regression predicts numerical values (correct)
Classification uses decision trees, while Regression uses neural networks
Classification deals with big data, while Regression deals with small datasets
Classification is unsupervised learning, while Regression is supervised learning

What is the primary focus of Descriptive Analytics in Machine Learning?

Optimizing decision-making by considering various possible actions
Summarizing historical data to understand past events (correct)
Identifying hidden patterns or structures within data
Predicting future outcomes based on historical patterns

What distinguishes Supervised Learning from Unsupervised Learning in Machine Learning?

<p>Supervised Learning uses labeled data, while Unsupervised Learning uses unlabeled data (A)</p> Signup and view all the answers

What is the key focus of Neural Network and Deep Learning in Machine Learning?

<p>Learning from complex and large-scale datasets to extract patterns and make decisions (C)</p> Signup and view all the answers

Flashcards

Supervised Learning

Machine learning approaches that use labeled data to train models for specific tasks, such as classification or regression.

Unsupervised Learning

Machine learning approaches that use unlabeled data to discover patterns and insights, such as clustering or dimensionality reduction.

Regression

Types of machine learning problems that involve predicting a numerical value, such as stock prices or house prices.

Classification

A type of machine learning problem that involves predicting a category or label, such as whether an email is spam or not.

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Deep Learning

A field of machine learning that deals with training models to learn complex patterns from large datasets, often using artificial neural networks.

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Study Notes

Data Science and Its Concepts

Data Science is a field that deals with defining data, identifying benefits, and exploring uses of Big Data.
Big Data has various facets, including Structured Data, Unstructured Data, Natural Language, Machine-generated Data, Graph-based or Network Data, Audio, Image, Video, and Streaming data.

Data Science Process

The data science process involves six steps:
- Defining research goals
- Data retrieval
- Cleansing data and correcting errors as early as possible
- Integrating data from different sources
- Transforming data
- Exploratory data analysis

Data Science Process (Continued)

The data science process also includes:
- Data modeling
- Model and variable selection
- Model execution
- Model diagnostic and model comparison
- Presentation and automation

Data Science Ecosystem

The Big Data ecosystem includes:
- Distributed file systems
- Distributed programming framework
- Data integration framework
- Machine learning framework
- No SQL Databases
- Scheduling tools
- Benchmarking tools
- System deployments

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Description

This quiz covers the concepts and processes of data science, machine learning, and artificial intelligence, including defining data science, big data, and the different types of data. It also explores the objectives of the course offered by Samatrix Consulting Pvt Ltd.