Join and share our telegram channel with your friends to learn data science, machine learning, big data and , deep learning
What is online machine learning
Online machine learning is a type of machine learning that involves updating a model continuously based on new data points as they become available. In contrast to batch learning, where the model is trained on a fixed dataset, online learning adapts to new data incrementally and in real-time.
Online learning is particularly useful in scenarios where data is constantly arriving and the model needs to be updated frequently to reflect the latest information. Examples include fraud detection, recommendation systems, and online advertising.
In online learning, the model is initially trained on a small subset of the data, and as new data arrives, the model updates its parameters to incorporate the new information. The update process can be done using various algorithms, such as stochastic gradient descent or online gradient descent.
Online learning has several advantages over batch learning, including the ability to adapt to changing data distributions, the ability to handle large datasets efficiently, and the ability to make real-time predictions. However, it also has some limitations, such as the need to carefully manage the learning rate to avoid overfitting, and the difficulty in handling non-stationary data streams.
Online machine learning is a type of machine learning that involves updating a model continuously based on new data points as they become available. In contrast to batch learning, where the model is trained on a fixed dataset, online learning adapts to new data incrementally and in real-time.
Online learning is particularly useful in scenarios where data is constantly arriving and the model needs to be updated frequently to reflect the latest information. Examples include fraud detection, recommendation systems, and online advertising.
In online learning, the model is initially trained on a small subset of the data, and as new data arrives, the model updates its parameters to incorporate the new information. The update process can be done using various algorithms, such as stochastic gradient descent or online gradient descent.
Online learning has several advantages over batch learning, including the ability to adapt to changing data distributions, the ability to handle large datasets efficiently, and the ability to make real-time predictions. However, it also has some limitations, such as the need to carefully manage the learning rate to avoid overfitting, and the difficulty in handling non-stationary data streams.
Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
What are the various time series algorithms available for forecasting
Source- Instagram
www.instagram.com/dataspoof
Source- Instagram
www.instagram.com/dataspoof
https://www.instagram.com/p/CqktXUrNeA_/?igshid=YmMyMTA2M2Y=
Follow us on Instagram for more data science related contents and giveways
Follow us on Instagram for more data science related contents and giveways
Data Engineering course
1. Master Python: https://lnkd.in/e5rCbvP8
2. Learn SQL: https://lnkd.in/efMKFkfX
3. Learn MySQL: https://lnkd.in/efk-Mi3c
4. Learn MongoDB: https://lnkd.in/eMKPWtqX
5. Dominate PySpark: https://lnkd.in/exwA2hKz
6. Learn Bash, Airflow & Kafka: https://lnkd.in/eyN6u2yd
7. Learn Git & GitHub: https://lnkd.in/eX_Q8s99
8. Learn CICD basics: https://lnkd.in/epKGivFY
9. Decode Data Warehousing: https://lnkd.in/eKnVbFAB
10. Learn DBT: : https://lnkd.in/eG9eaEuE
11. Learn Data Lakes: https://lnkd.in/eQ9xxAJT
12. Learn DataBricks: https://lnkd.in/ePZpCv86
13. Learn Azure Databricks: https://lnkd.in/eBij4akJ
14. Learn Snowflake: https://lnkd.in/erETmtFU
15. Learn Apache NiFi: http://bit.ly/43btwYy
16. Learn Debezium: http://bit.ly/3K6W5gL
1. Master Python: https://lnkd.in/e5rCbvP8
2. Learn SQL: https://lnkd.in/efMKFkfX
3. Learn MySQL: https://lnkd.in/efk-Mi3c
4. Learn MongoDB: https://lnkd.in/eMKPWtqX
5. Dominate PySpark: https://lnkd.in/exwA2hKz
6. Learn Bash, Airflow & Kafka: https://lnkd.in/eyN6u2yd
7. Learn Git & GitHub: https://lnkd.in/eX_Q8s99
8. Learn CICD basics: https://lnkd.in/epKGivFY
9. Decode Data Warehousing: https://lnkd.in/eKnVbFAB
10. Learn DBT: : https://lnkd.in/eG9eaEuE
11. Learn Data Lakes: https://lnkd.in/eQ9xxAJT
12. Learn DataBricks: https://lnkd.in/ePZpCv86
13. Learn Azure Databricks: https://lnkd.in/eBij4akJ
14. Learn Snowflake: https://lnkd.in/erETmtFU
15. Learn Apache NiFi: http://bit.ly/43btwYy
16. Learn Debezium: http://bit.ly/3K6W5gL
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
How to build a website using chatgpt
To learn Data Science and Python, follow @dataspoof 🧠
https://www.instagram.com/p/CqsnrcXPMYx/?igshid=YmMyMTA2M2Y=
To learn Data Science and Python, follow @dataspoof 🧠
https://www.instagram.com/p/CqsnrcXPMYx/?igshid=YmMyMTA2M2Y=