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Kishore S Meda
Learners will work on an open dataset and preprocess it so it is suitable for training machine learning model. They will create a clean, processed dataset out of raw data and will have an ML model by the end of the project.
Register for this course and see more details by visiting:
OpenCourser.com/course/xg4vzo/machine
What's inside
Syllabus
Project Overview
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
Helps learners process raw data into a clean, usable dataset, teaching a fundamental skill in data analysis
Provides real-world experience by having learners work on an open dataset
Offers a practical approach to machine learning by focusing on the process of creating a usable dataset for training
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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 Machine Learning with Databricks: Process Data with these
activities:
Review Data Wrangling Skills
Show steps
Helps you refresh your data wrangling skills, which are essential for preparing data for machine learning models.
Browse courses on
Data Wrangling
Show steps
-
Go through online tutorials on data wrangling using Python or R.
-
Work on practice exercises to apply your refreshed skills.
Follow Online Tutorials on Data Preprocessing
Show steps
Provides structured guidance and step-by-step instructions for enhancing your understanding of data preprocessing concepts.
Browse courses on
Data Preprocessing
Show steps
-
Identify reputable online platforms or instructors offering tutorials on data preprocessing.
-
Follow the tutorials, complete the exercises, and review the provided examples.
Refer to Practical Machine Learning Recipes in Python
Show steps
Develop initial insights into practical data science and algorithms.
Show steps
-
Read through the book's chapters covering ML techniques.
-
Work through the exercises and examples provided in the book.
-
Experiment with the code snippets provided online.
-
Utilize the book's resources to understand ML concepts.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve ML Coding Challenges
Show steps
Strengthen programming skills and problem-solving abilities.
Show steps
-
Identify online platforms or resources that提供ML coding challenges.
-
Choose challenges that match your skill level and interests.
-
Attempt to solve the challenges on your own.
-
Review your solutions and identify areas for improvement.
Attend Data Preprocessing Workshops
Show steps
Offers a chance to interact with experts and learn from their insights on best practices in data preprocessing.
Browse courses on
Data Preprocessing
Show steps
-
Identify and register for relevant workshops organized by universities, industry professionals, or online platforms.
-
Attend the workshops, take notes, and actively participate in discussions.
Join a Study Group
Show steps
Enhance understanding through collaboration and discussion.
Show steps
-
Find a group of peers with similar interests and learning goals.
-
Meet regularly to discuss course materials, share insights, and work on projects together.
-
Provide feedback and support to fellow group members.
Join Data Preprocessing Study Groups
Show steps
Facilitates collaboration, knowledge sharing, and peer support in your journey to master data preprocessing.
Browse courses on
Data Preprocessing
Show steps
-
Connect with classmates or join online communities dedicated to data preprocessing.
-
Organize regular study sessions, discuss concepts, and work on projects together.
Build a Predictive Model for a Real-World Problem
Show steps
Develop a deep understanding of the ML process.
Show steps
-
Identify a specific problem to address.
-
Gather and preprocess the necessary data.
-
Select and implement an appropriate ML algorithm.
-
Evaluate and refine the model's performance.
-
Deploy the model and track its performance in real-world scenarios.
Explore Data Preprocessing Techniques
Show steps
Allows you to experiment with different data preprocessing techniques and understand their impact on model performance.
Browse courses on
Data Preprocessing
Show steps
-
Choose a dataset and define your project goals.
-
Apply various preprocessing techniques like data cleaning, transformation, and normalization.
-
Evaluate the effectiveness of different techniques using metrics like accuracy and F1-score.
Build a Machine Learning Project
Show steps
Gain hands-on experience in applying ML techniques.
Show steps
-
Identify a real-world problem that can be addressed using ML.
-
Collect and prepare the necessary data.
-
Choose and implement suitable ML algorithms.
-
Evaluate the performance of the developed model.
-
Deploy the model and monitor its performance over time.
Solve Data Preprocessing Challenges
Show steps
Provides an opportunity to practice solving real-world data preprocessing challenges and improve your problem-solving skills.
Browse courses on
Data Preprocessing
Show steps
-
Participate in online coding challenges or competitions focused on data preprocessing.
-
Work on Kaggle or other data science platforms to solve data preprocessing tasks.
Develop a Data Preprocessing Pipeline
Show steps
Allows you to apply your knowledge and skills to a real-world project, demonstrating your ability to preprocess data effectively.
Browse courses on
Data Preprocessing
Show steps
-
Choose a dataset and define the preprocessing tasks to be performed.
-
Implement the preprocessing pipeline using appropriate tools and libraries.
-
Document your approach, the techniques used, and the results obtained.
Review Data Wrangling Skills
Show steps
Helps you refresh your data wrangling skills, which are essential for preparing data for machine learning models.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Go through online tutorials on data wrangling using Python or R.
- Work on practice exercises to apply your refreshed skills.
Follow Online Tutorials on Data Preprocessing
Show steps
Provides structured guidance and step-by-step instructions for enhancing your understanding of data preprocessing concepts.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Identify reputable online platforms or instructors offering tutorials on data preprocessing.
- Follow the tutorials, complete the exercises, and review the provided examples.
Refer to Practical Machine Learning Recipes in Python
Show steps
Develop initial insights into practical data science and algorithms.
Show steps
Show steps
Show steps
- Read through the book's chapters covering ML techniques.
- Work through the exercises and examples provided in the book.
- Experiment with the code snippets provided online.
- Utilize the book's resources to understand ML concepts.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Solve ML Coding Challenges
Show steps
Strengthen programming skills and problem-solving abilities.
Show steps
Show steps
Show steps
- Identify online platforms or resources that提供ML coding challenges.
- Choose challenges that match your skill level and interests.
- Attempt to solve the challenges on your own.
- Review your solutions and identify areas for improvement.
Attend Data Preprocessing Workshops
Show steps
Offers a chance to interact with experts and learn from their insights on best practices in data preprocessing.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Identify and register for relevant workshops organized by universities, industry professionals, or online platforms.
- Attend the workshops, take notes, and actively participate in discussions.
Join a Study Group
Show steps
Enhance understanding through collaboration and discussion.
Show steps
Show steps
Show steps
- Find a group of peers with similar interests and learning goals.
- Meet regularly to discuss course materials, share insights, and work on projects together.
- Provide feedback and support to fellow group members.
Join Data Preprocessing Study Groups
Show steps
Facilitates collaboration, knowledge sharing, and peer support in your journey to master data preprocessing.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Connect with classmates or join online communities dedicated to data preprocessing.
- Organize regular study sessions, discuss concepts, and work on projects together.
Build a Predictive Model for a Real-World Problem
Show steps
Develop a deep understanding of the ML process.
Show steps
Show steps
Show steps
- Identify a specific problem to address.
- Gather and preprocess the necessary data.
- Select and implement an appropriate ML algorithm.
- Evaluate and refine the model's performance.
- Deploy the model and track its performance in real-world scenarios.
Explore Data Preprocessing Techniques
Show steps
Allows you to experiment with different data preprocessing techniques and understand their impact on model performance.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Choose a dataset and define your project goals.
- Apply various preprocessing techniques like data cleaning, transformation, and normalization.
- Evaluate the effectiveness of different techniques using metrics like accuracy and F1-score.
Build a Machine Learning Project
Show steps
Gain hands-on experience in applying ML techniques.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using ML.
- Collect and prepare the necessary data.
- Choose and implement suitable ML algorithms.
- Evaluate the performance of the developed model.
- Deploy the model and monitor its performance over time.
Solve Data Preprocessing Challenges
Show steps
Provides an opportunity to practice solving real-world data preprocessing challenges and improve your problem-solving skills.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Participate in online coding challenges or competitions focused on data preprocessing.
- Work on Kaggle or other data science platforms to solve data preprocessing tasks.
Develop a Data Preprocessing Pipeline
Show steps
Allows you to apply your knowledge and skills to a real-world project, demonstrating your ability to preprocess data effectively.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Choose a dataset and define the preprocessing tasks to be performed.
- Implement the preprocessing pipeline using appropriate tools and libraries.
- Document your approach, the techniques used, and the results obtained.
Career center
Learners who complete Machine Learning with Databricks: Process Data will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists create and apply statistical and machine learning models to large datasets. This course provides a foundation in data preprocessing, a crucial step in the machine learning workflow. By learning how to clean, transform, and prepare data for modeling, individuals can enhance their skills as Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course teaches essential data preprocessing techniques, enabling individuals to build robust and accurate ML models. The hands-on experience gained in preparing data for modeling aligns with the core responsibilities of Machine Learning Engineers.
Data Analyst
Data Analyst
Data Analysts gather, clean, and analyze data to extract insights and inform decision-making. This course focuses on data preprocessing, a fundamental skill for Data Analysts. By gaining proficiency in data preparation, individuals can enhance their ability to derive meaningful insights from complex datasets.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts leverage data to identify trends, patterns, and opportunities for businesses. This course provides a foundation in data preprocessing, enabling individuals to prepare and analyze data effectively. By mastering data preparation techniques, Business Intelligence Analysts can enhance their ability to generate valuable insights and support informed business decisions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course focuses on data preprocessing, a crucial aspect of statistical analysis. By gaining expertise in data preparation, individuals can improve the accuracy and reliability of their statistical models and enhance their ability to draw meaningful conclusions from data.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases, ensuring data integrity and accessibility. This course provides a foundation in data preprocessing, enabling individuals to prepare data for efficient storage and retrieval in database systems. By mastering data preparation techniques, Database Administrators can optimize database performance and ensure data quality.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a foundation in data preprocessing, a crucial step in building data-driven applications. By learning how to prepare data for modeling, Software Engineers can enhance the performance and accuracy of their software solutions.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course provides a foundation in data preprocessing, a fundamental aspect of data engineering. By gaining proficiency in data preparation, Data Engineers can enhance the efficiency and quality of their data pipelines and improve the overall data management process.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to optimize complex systems. This course provides a foundation in data preprocessing, enabling individuals to prepare data for modeling and analysis in operations research. By mastering data preparation techniques, Operations Research Analysts can enhance the accuracy and effectiveness of their models and contribute to better decision-making.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a foundation in data preprocessing, a crucial step in quantitative analysis. By gaining proficiency in data preparation, Quantitative Analysts can improve the accuracy and reliability of their models and enhance their ability to make informed investment decisions.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make recommendations and advise clients on investment decisions. This course provides a foundation in data preprocessing, enabling individuals to prepare data for financial modeling and analysis. By mastering data preparation techniques, Financial Analysts can enhance the accuracy and reliability of their recommendations and contribute to better investment outcomes.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research to understand market trends, consumer behavior, and industry dynamics. This course provides a foundation in data preprocessing, enabling individuals to prepare data for analysis and interpretation in market research. By mastering data preparation techniques, Market Research Analysts can enhance the accuracy and reliability of their insights and contribute to better decision-making.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks in various industries, including finance, insurance, and healthcare. This course provides a foundation in data preprocessing, enabling individuals to prepare data for risk modeling and analysis. By mastering data preparation techniques, Risk Analysts can improve the accuracy and reliability of their risk assessments and contribute to better risk management practices.
For more career information including salaries, visit:
OpenCourser.com/course/xg4vzo/machine
Reading list
We've selected seven 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
Machine Learning with Databricks: Process Data.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and support vector machines. It will be useful for readers who want to learn about the statistical foundations of machine learning.
Provides a hands-on guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It will be useful for readers who want to learn how to implement machine learning models in Python.
Save
Provides a comprehensive overview of data mining techniques, including data preprocessing, feature selection, and model evaluation. It will be useful for readers who want to learn about the practical aspects of data mining.
Provides a comprehensive overview of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning. It will be useful for readers who want to learn about the different types of machine learning algorithms and how they work.
Provides a comprehensive overview of machine learning techniques using R, including data preprocessing, model training, and model evaluation. It will be useful for readers who want to learn how to implement machine learning models in R.
Provides a collection of recipes for solving common machine learning problems using Python. It will be useful for readers who want to learn how to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of deep learning techniques using Python, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It will be useful for readers who want to learn about the latest advances in deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xg4vzo/machine
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Kishore S Meda
Language
English
Good to know
Helps learners process raw data into a clean, usable dataset, teaching a fundamental skill in data analysis
Provides real-world experience by having learners work on an open dataset
Offers a practical approach to machine learning by focusing on the process of creating a usable dataset for training
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