Podcast
Questions and Answers
What is the primary objective of the Gathering Data step in the machine learning life cycle?
What is the primary objective of the Gathering Data step in the machine learning life cycle?
What is the result of performing the Gathering Data step?
What is the result of performing the Gathering Data step?
What is the purpose of Data Preparation?
What is the purpose of Data Preparation?
What is the primary goal of Data Wrangling?
What is the primary goal of Data Wrangling?
Signup and view all the answers
What is a common issue with collected data in real-world applications?
What is a common issue with collected data in real-world applications?
Signup and view all the answers
What is the importance of the quantity of collected data?
What is the importance of the quantity of collected data?
Signup and view all the answers
What is the primary objective of training a model in machine learning?
What is the primary objective of training a model in machine learning?
Signup and view all the answers
Which of the following steps involves evaluating the model's performance?
Which of the following steps involves evaluating the model's performance?
Signup and view all the answers
What is the final step in the machine learning life cycle?
What is the final step in the machine learning life cycle?
Signup and view all the answers
What type of algorithms are used in training a model?
What type of algorithms are used in training a model?
Signup and view all the answers
What is the purpose of data analysis in machine learning?
What is the purpose of data analysis in machine learning?
Signup and view all the answers
Study Notes
Machine Learning Life Cycle
- The machine learning life cycle consists of 7 steps: Gathering Data, Data Preparation, Data Wrangling, Data Analysis, Train Model, Test Model, and Deployment.
Gathering Data
- The first step of the machine learning life cycle, aiming to identify and obtain all data-related problems.
- Data can be collected from various sources such as files, databases, the internet, or mobile devices.
- The quantity and quality of the collected data will determine the efficiency of the output.
Data Preparation
- Exploring, organizing, and preparing the data for use in machine learning training.
- Understanding the nature of data, including characteristics, format, and quality.
- Identifying correlations, general trends, and outliers.
Data Wrangling (Cleaning)
- The process of cleaning and converting raw data into a usable format.
- Common issues with collected data include missing values, duplicates, invalid data, and noise.
- Various filtering techniques are used to clean the data.
Data Analysis
- Using cleaned and prepared data to select a machine learning technique (model) such as Classification, Regression, Cluster analysis, or Association.
- Building and evaluating the model using the prepared data.
- Reviewing the results of the model.
Train Model
- Training the model to improve its performance using various machine learning algorithms.
- The model is trained on datasets to understand patterns, rules, and features.
Test Model
- Testing the model to check for accuracy by providing a test dataset.
- Determining the percentage accuracy of the model as per the project or problem requirement.
Deployment
- The final step of the machine learning life cycle, where the model is deployed in a real-world system.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
Learn about the first step of the machine learning lifecycle, where the goal is to identify and obtain all data-related problems. Understand the importance of data sources and quality for efficient output.
