Logistic Regression Practical Case Study
Breast Cancer detection using Logistic Regression
Rating ⭐️: 4.7 out 5
Students 👨🎓 : 35,819
Duration ⏰ : 1hr 4min of on-demand video
Created by 👨🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
🔗 Course Link
#Logistic #Regression
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Breast Cancer detection using Logistic Regression
Rating ⭐️: 4.7 out 5
Students 👨🎓 : 35,819
Duration ⏰ : 1hr 4min of on-demand video
Created by 👨🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
🔗 Course Link
#Logistic #Regression
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Udemy
Free Data Science Tutorial - Logistic Regression Practical Case Study
Breast Cancer detection using Logistic Regression - Free Course
Top 5 Reasons Why Machine Learning Projects Fail
The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project.
1. Insufficient data
2. ML Models unsynchronized with the legacy systems
3. Lack of enough data scientists
4. Difficulty in updating
5. Lack of leaders’ support
The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way.
🔗 Read more
The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project.
1. Insufficient data
2. ML Models unsynchronized with the legacy systems
3. Lack of enough data scientists
4. Difficulty in updating
5. Lack of leaders’ support
The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way.
🔗 Read more
DSA_Book.pdf
14.2 MB
Data Science: Theories, Models, Algorithms, and Analytics
by SANJIV RANJAN DAS
by SANJIV RANJAN DAS
R, ggplot, and Simple Linear Regression
Begin to use R and ggplot while learning the basics of linear regression
Rating ⭐️: 4.1 out 5
Students 👨🎓 : 42,633
Duration ⏰ : 2hr 14min of on-demand video
Created by 👨🏫: Charles Redmond
🔗 Course Link
#R #linear #Regression
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Begin to use R and ggplot while learning the basics of linear regression
Rating ⭐️: 4.1 out 5
Students 👨🎓 : 42,633
Duration ⏰ : 2hr 14min of on-demand video
Created by 👨🏫: Charles Redmond
🔗 Course Link
#R #linear #Regression
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @datascience_bds for more👈
Udemy
Free R (programming language) Tutorial - R, ggplot, and Simple Linear Regression
Begin to use R and ggplot while learning the basics of linear regression - Free Course
Introduction-to-Data-Science-a-beginners-guide-min.jpg
635.5 KB
Introduction to Data Science
One question to make your data project 10x more valuable
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.
The magic question
Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes:
"What decision are you trying to make?"
Subtext: What action will you take once you have the answers?
If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action.
And the answer can be VERY telling! That's why it's so powerful.
A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data.
🔗 Read more
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.
The magic question
Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes:
"What decision are you trying to make?"
Subtext: What action will you take once you have the answers?
If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action.
And the answer can be VERY telling! That's why it's so powerful.
A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data.
🔗 Read more
Why Statistics Matter in Data Science even in 2023.pdf
1.8 MB
Why Statistics Matter in Data Science even in 2023
Forwarded from Data science research papers
Going Denser with Open-Vocabulary Part Segmentation
Publication date: 18 May 2023
Topic: Object detection
Paper: https://arxiv.org/pdf/2305.11173v1.pdf
GitHub: https://github.com/facebookresearch/vlpart
Description:
Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this work, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs:
🔹 We train the detector on the joint of part-level, object-level and image-level data.
🔹 We parse the novel object into its parts by its dense semantic correspondence with the base object.
Publication date: 18 May 2023
Topic: Object detection
Paper: https://arxiv.org/pdf/2305.11173v1.pdf
GitHub: https://github.com/facebookresearch/vlpart
Description:
Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this work, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs:
🔹 We train the detector on the joint of part-level, object-level and image-level data.
🔹 We parse the novel object into its parts by its dense semantic correspondence with the base object.
1700202599352.pdf
10.1 MB
WHICH CHART WHEN?
The data Analyst's guide to choosing the right charts
The data Analyst's guide to choosing the right charts
Create your own roadmap to succeed as a Data Engineer. 😉
▶️In the ever-evolving field of data engineering, staying up-to-date with the latest technologies and best practices is crucial with industries relying heavily on data-driven decision-making.
👉As we approach 2024, the field of data engineering continues to evolve, with new challenges and opportunities with the following key pointers:
📌Programming languages: Python, Scala and Java are few most popular programming languages for data engineers.
📌Databases: SQL or NoSQL databases such as Server, MySQL, and PostgreSQL, MongoDB, Cassandra are few popular databases.
📌Data modeling: The process of creating a blueprint for a database, it helps to ensure that the database is designed to meet the needs of the business.
📌Cloud computing: AWS, Azure, and GCP are the three major cloud computing platforms that can be used to build and deploy data engineering solutions.
📌Big data technologies: Apache Spark, Kafka, Beam and Hadoop are some of the most popular big data technologies to process and analyze large datasets.
📌Data warehousing: Snowflake, Databricks, BigQuery and Redshift are popular data warehousing platforms used to store and analyze large datasets for business intelligence purposes.
📌Data streaming: Apache Kafka and Spark are popular data streaming platform used to process and analyze data in real time.
📌Data lakes and data meshes: The two emerging data management architectures, Data lakes are centralized repositories for all types of data, while data meshes are decentralized architectures that distribute data across multiple locations.
📌Orchestraction: Pipelines are orchestrated using tools like Airflow, Dagster, Mage or similar other tools to schedule and monitor workflows.
📌Data quality, data observability, and data governance: Ensuring reliability and trustworthiness of data quality helps to keep data accurate, complete, and consistent. Data observability helps to monitor and understand data systems. Data governance is the process of establishing policies and procedures for managing data.
📌Data visualization: Tableau, Power BI, and Looker are three popular data visualization tools to create charts and graphs that can be used to communicate data insights to stakeholders.
📌DevOps and data ops: Two set of practices used to automate and streamline the development and deployment of data engineering solutions.
🔰Develop good communication and collaboration skills is equally important to understand the business aspects of data engineering, such as project management and stakeholder engagement.
♐️Stay updated and relevant with emerging trends like AI/ML, and IOT used to develop intelligent data pipelines and data warehouses.
➠Data engineers who want to be successful in 2023-2024 and beyond should focus on developing their skills and experience in the areas listed above.
▶️In the ever-evolving field of data engineering, staying up-to-date with the latest technologies and best practices is crucial with industries relying heavily on data-driven decision-making.
👉As we approach 2024, the field of data engineering continues to evolve, with new challenges and opportunities with the following key pointers:
📌Programming languages: Python, Scala and Java are few most popular programming languages for data engineers.
📌Databases: SQL or NoSQL databases such as Server, MySQL, and PostgreSQL, MongoDB, Cassandra are few popular databases.
📌Data modeling: The process of creating a blueprint for a database, it helps to ensure that the database is designed to meet the needs of the business.
📌Cloud computing: AWS, Azure, and GCP are the three major cloud computing platforms that can be used to build and deploy data engineering solutions.
📌Big data technologies: Apache Spark, Kafka, Beam and Hadoop are some of the most popular big data technologies to process and analyze large datasets.
📌Data warehousing: Snowflake, Databricks, BigQuery and Redshift are popular data warehousing platforms used to store and analyze large datasets for business intelligence purposes.
📌Data streaming: Apache Kafka and Spark are popular data streaming platform used to process and analyze data in real time.
📌Data lakes and data meshes: The two emerging data management architectures, Data lakes are centralized repositories for all types of data, while data meshes are decentralized architectures that distribute data across multiple locations.
📌Orchestraction: Pipelines are orchestrated using tools like Airflow, Dagster, Mage or similar other tools to schedule and monitor workflows.
📌Data quality, data observability, and data governance: Ensuring reliability and trustworthiness of data quality helps to keep data accurate, complete, and consistent. Data observability helps to monitor and understand data systems. Data governance is the process of establishing policies and procedures for managing data.
📌Data visualization: Tableau, Power BI, and Looker are three popular data visualization tools to create charts and graphs that can be used to communicate data insights to stakeholders.
📌DevOps and data ops: Two set of practices used to automate and streamline the development and deployment of data engineering solutions.
🔰Develop good communication and collaboration skills is equally important to understand the business aspects of data engineering, such as project management and stakeholder engagement.
♐️Stay updated and relevant with emerging trends like AI/ML, and IOT used to develop intelligent data pipelines and data warehouses.
➠Data engineers who want to be successful in 2023-2024 and beyond should focus on developing their skills and experience in the areas listed above.