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Muhammad Saad uddin
By the end of this project, you will learn how to perform analysis on data using different python libraries and export reports and visualization without much hassle all this with minimal coding.
Register for this course and see more details by visiting:
OpenCourser.com/course/osk9ay/automl
What's inside
Syllabus
AutoML tools for DS
Here you will describe what the project is about...give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for anyone who wants to enhance their data analysis and visualization skills
Uses Python libraries to perform analysis and create visualizations
Suitable for beginners who want to gain foundational knowledge in data analysis
Provides hands-on training with minimal coding, making it accessible to learners with varying technical skill levels
Covers automated machine learning tools for data science, which can be beneficial for streamlining analysis processes
Save this course
Save AutoML tools for data science to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in AutoML tools for data science with these
activities:
Review 'Python Machine Learning' by Sebastian Raschka
Show steps
Review the concepts covered in the course by reading a related book. This can help reinforce the material and provide additional insights.
View
Build a Large Language Model (From Scratch)
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read the chapters corresponding to the course topics.
-
Take notes and highlight important sections.
-
Complete the exercises and quizzes in the book.
Watch video tutorials on specific Python libraries
Show steps
Complement your learning by watching video tutorials that demonstrate how to use specific Python libraries relevant to the course.
Browse courses on
Python Libraries
Show steps
-
Search for video tutorials on YouTube or other platforms.
-
Select tutorials that cover the specific libraries used in the course.
-
Watch the tutorials and take notes on the key concepts and code snippets.
Practice Python code challenges on coding platforms
Show steps
Practice coding exercises on platforms like HackerRank or LeetCode to improve your programming skills and reinforce the concepts learned in the course.
Browse courses on
Python
Show steps
-
Sign up for a coding platform like HackerRank or LeetCode.
-
Select a problem to solve that aligns with the course concepts you're studying.
-
Attempt to solve the problem on your own.
-
Review solutions and explanations from the platform or online forums to understand different approaches.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum to connect with peers
Show steps
Engage with other students through a study group or online forum to discuss course topics, share knowledge, and learn from different perspectives.
Show steps
-
Join a study group organized through the course platform or social media.
-
Attend regular study sessions and participate in discussions.
-
Share your understanding and help others.
Create a Python script to visualize data
Show steps
Apply your knowledge by creating a Python script that visualizes data using libraries like Matplotlib or Seaborn. This will enhance your understanding of data analysis and presentation techniques.
Browse courses on
Data Visualization
Show steps
-
Gather a dataset.
-
Import the necessary Python libraries.
-
Clean and prepare the data for visualization.
-
Select appropriate visualization techniques and create the plot.
Build a small Python project that integrates multiple concepts
Show steps
Challenge yourself by creating a small Python project that combines concepts learned in the course. This will provide practical experience and demonstrate your understanding.
Show steps
-
Define the project scope and objectives.
-
Design the project architecture and data structures.
-
Implement the project using Python.
-
Test and debug the project.
Contribute to a Python open-source library or project
Show steps
Deepen your understanding and enhance your portfolio by contributing to open-source Python projects. This experience will expose you to real-world coding practices and collaboration.
Show steps
-
Identify open-source Python projects that align with your interests.
-
Review the project's codebase and documentation.
-
Find a suitable issue or feature to work on.
-
Create a pull request with your proposed changes.
Review 'Python Machine Learning' by Sebastian Raschka
Show steps
Review the concepts covered in the course by reading a related book. This can help reinforce the material and provide additional insights.
View
Build a Large Language Model (From Scratch)
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read the chapters corresponding to the course topics.
- Take notes and highlight important sections.
- Complete the exercises and quizzes in the book.
Watch video tutorials on specific Python libraries
Show steps
Complement your learning by watching video tutorials that demonstrate how to use specific Python libraries relevant to the course.
Browse courses on
Python Libraries
Show steps
Show steps
Show steps
- Search for video tutorials on YouTube or other platforms.
- Select tutorials that cover the specific libraries used in the course.
- Watch the tutorials and take notes on the key concepts and code snippets.
Practice Python code challenges on coding platforms
Show steps
Practice coding exercises on platforms like HackerRank or LeetCode to improve your programming skills and reinforce the concepts learned in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Sign up for a coding platform like HackerRank or LeetCode.
- Select a problem to solve that aligns with the course concepts you're studying.
- Attempt to solve the problem on your own.
- Review solutions and explanations from the platform or online forums to understand different approaches.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum to connect with peers
Show steps
Engage with other students through a study group or online forum to discuss course topics, share knowledge, and learn from different perspectives.
Show steps
Show steps
Show steps
- Join a study group organized through the course platform or social media.
- Attend regular study sessions and participate in discussions.
- Share your understanding and help others.
Create a Python script to visualize data
Show steps
Apply your knowledge by creating a Python script that visualizes data using libraries like Matplotlib or Seaborn. This will enhance your understanding of data analysis and presentation techniques.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather a dataset.
- Import the necessary Python libraries.
- Clean and prepare the data for visualization.
- Select appropriate visualization techniques and create the plot.
Build a small Python project that integrates multiple concepts
Show steps
Challenge yourself by creating a small Python project that combines concepts learned in the course. This will provide practical experience and demonstrate your understanding.
Show steps
Show steps
Show steps
- Define the project scope and objectives.
- Design the project architecture and data structures.
- Implement the project using Python.
- Test and debug the project.
Contribute to a Python open-source library or project
Show steps
Deepen your understanding and enhance your portfolio by contributing to open-source Python projects. This experience will expose you to real-world coding practices and collaboration.
Show steps
Show steps
Show steps
- Identify open-source Python projects that align with your interests.
- Review the project's codebase and documentation.
- Find a suitable issue or feature to work on.
- Create a pull request with your proposed changes.
Career center
Learners who complete AutoML tools for data science will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops, deploys, and maintains machine learning models. This course provides a foundation in AutoML tools for data science, which can help Machine Learning Engineers to develop more efficient and accurate models.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to extract insights. This course provides a foundation in AutoML tools for data science, which can help Statisticians to develop more accurate and efficient models.
Data Scientist
Data Scientist
A Data Scientist uses machine learning and statistical modeling to extract insights from data, which can be used to make predictions or inform decisions. This course provides a foundation in AutoML tools for data science, which can help Data Scientists to develop and deploy models more quickly and efficiently.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to identify trends and patterns. This course provides a foundation in AutoML tools for data science, which can help Data Analysts to explore data more quickly and easily.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to support business decision-making. This course provides a foundation in AutoML tools for data science, which can help Business Intelligence Analysts to develop more accurate and insightful reports.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. This course provides a foundation in AutoML tools for data science, which can help Business Analysts to develop data-driven solutions more quickly and efficiently.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to analyze financial performance. This course provides a foundation in AutoML tools for data science, which can help Financial Analysts to develop more accurate and sophisticated models.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to identify and manage risks. This course provides a foundation in AutoML tools for data science, which can help Risk Analysts to develop more accurate and reliable models.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical modeling to analyze financial data. This course provides a foundation in AutoML tools for data science, which can help Quantitative Analysts to develop more accurate and sophisticated models.
Actuary
Actuary
An Actuary uses data to assess and manage financial risks. This course provides a foundation in AutoML tools for data science, which can help Actuaries to develop more accurate and sophisticated models.
Data Architect
Data Architect
A Data Architect designs and builds data architectures. This course provides a foundation in AutoML tools for data science, which can help Data Architects to develop more efficient and effective architectures.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides a foundation in AutoML tools for data science, which can help Software Engineers to develop more efficient and effective software.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve business problems. This course provides a foundation in AutoML tools for data science, which can help Operations Research Analysts to develop more efficient and effective solutions.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course provides a foundation in AutoML tools for data science, which can help Database Administrators to identify and solve performance issues more quickly and efficiently.
Data Engineer
Data Engineer
A Data Engineer designs and builds systems to store, manage, and process data. This course provides a foundation in AutoML tools for data science, which can help Data Engineers to develop more efficient and scalable systems.
For more career information including salaries, visit:
OpenCourser.com/course/osk9ay/automl
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AutoML tools for data science.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on practical implementation using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers various aspects of machine learning, including data preprocessing, model selection, evaluation, and deployment.
Covers the practical aspects of machine learning using Python, with a focus on real-world applications. It provides step-by-step instructions for building and evaluating machine learning models, and includes case studies and exercises to reinforce learning.
Provides a comprehensive guide to deep learning, a powerful technique for solving complex problems in areas such as computer vision, natural language processing, and speech recognition. It covers the theoretical foundations of deep learning, as well as practical implementation using Python libraries such as TensorFlow and Keras.
Offers a gentle introduction to data science, covering fundamental concepts such as data cleaning, data analysis, and machine learning. It emphasizes the basics and provides hands-on exercises to help readers develop a practical understanding of data science techniques.
Provides a theoretical foundation for machine learning, with a focus on probabilistic models and Bayesian inference. It covers advanced topics such as graphical models, Markov chain Monte Carlo methods, and deep learning, making it suitable for readers with a strong background in mathematics and statistics.
Provides a practical introduction to programming using Python, with a focus on automating repetitive tasks. It covers basic programming concepts as well as advanced topics such as web scraping and data processing, making it a valuable resource for both beginners and experienced programmers.
Provides a comprehensive introduction to machine learning using Python, covering both theoretical concepts and practical implementation. It includes hands-on exercises and case studies to help readers develop a solid understanding of machine learning techniques.
Provides a comprehensive overview of statistical learning, covering a wide range of topics including linear regression, classification, and clustering. It emphasizes theoretical foundations and provides detailed mathematical explanations, making it suitable for readers with a strong background in statistics and machine learning.
Provides a comprehensive guide to machine learning using R, a popular programming language for data science and statistical computing. It covers various machine learning techniques, including supervised and unsupervised learning, and provides practical examples and case studies to help readers develop a solid understanding of machine learning concepts.
Provides a gentle introduction to machine learning, covering fundamental concepts and practical applications. It is written in a clear and concise style, making it suitable for readers with no prior knowledge of machine learning.
Provides a practical guide to deep learning using Fastai and PyTorch, two popular libraries for deep learning. It covers various aspects of deep learning, including image classification, natural language processing, and reinforcement learning, and provides hands-on exercises to help readers develop a solid understanding of deep learning techniques.
Provides a practical guide to data science using Python, covering various aspects of the data science workflow, including data cleaning, data analysis, and machine learning. It includes hands-on exercises and case studies to help readers develop a solid understanding of data science techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/osk9ay/automl
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Effort
2 Hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Language
English
Good to know
Appropriate for anyone who wants to enhance their data analysis and visualization skills
Uses Python libraries to perform analysis and create visualizations
Suitable for beginners who want to gain foundational knowledge in data analysis
Provides hands-on training with minimal coding, making it accessible to learners with varying technical skill levels
Covers automated machine learning tools for data science, which can be beneficial for streamlining analysis processes
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