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Learn the in-depth concepts, principles and best practices of building end-to-end machine learning pipelines. Enroll in Udacity's online training course today.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Command line interface basics
- Intermediate Python
- Basic machine learning
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/tgpwt2/building
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What's inside
Syllabus
Create a re-usable end-to-end pipeline for predicting short-term rental prices in New York City!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches professionals to build end-to-end ML pipelines for predicting short-term rental prices in New York City, which is core to the real estate domain
Builds a solid understanding of the concepts, principles, and best practices of machine learning pipelines
Taught by Udacity, a recognized provider of online training courses in the tech industry
Requires fluency in written and spoken English, which may be a barrier for non-native speakers
Assumes prior knowledge in command line interface basics, intermediate Python, and basic machine learning, which may not be suitable for complete beginners
Save this course
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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 Building a Machine Learning Pipeline with these
activities:
Review basic probability and statistics concepts
Show steps
Ensure a strong foundation in probability and statistics, essential for understanding machine learning algorithms
Browse courses on
Probability
Show steps
-
Review your notes or textbook from previous statistics courses
-
Go through online resources or tutorials on probability and statistics
-
Solve practice problems to test your understanding
Refresh Python skills
Show steps
Review Python syntax and basics to ensure you have a solid foundation for the course content.
Show steps
-
Review Python fundamentals, such as data types, variables, operators, and control flow.
-
Practice solving simple coding problems on platforms like Leetcode or Hackerrank.
Organize your study materials for easy reference
Show steps
Maintain a well-organized system for your course materials to enhance your learning
Show steps
-
Gather all your course materials, including lecture notes, assignments, and readings
-
Create a dedicated folder or notebook for each topic
-
File and label your materials systematically for easy retrieval
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review the basic Python syntax
Show steps
Sharpen your understanding of Python syntax to prepare for the course's hands-on programming
Browse courses on
Python Syntax
Show steps
-
Go through official Python tutorials
-
Review your notes from previous Python courses or books
-
Practice simple coding exercises on websites like Codewars or HackerRank
Participate in online discussion forums related to machine learning
Show steps
Engage with peers and experts to exchange ideas, ask questions, and expand your knowledge
Browse courses on
Machine Learning
Show steps
-
Identify active online forums or communities dedicated to machine learning
-
Participate in discussions, asking thoughtful questions and sharing insights
-
Read and respond to posts from other members, learning from their experiences and perspectives
Complete online tutorials on end-to-end machine learning pipelines
Show steps
Deepen your understanding of the end-to-end process of building machine learning pipelines by following guided tutorials.
Browse courses on
Machine Learning Workflow
Show steps
-
Find reputable online tutorials from platforms like Coursera, edX, or Udacity.
-
Follow along with the tutorials, completing hands-on exercises and projects.
Follow tutorials on machine learning pipelines
Show steps
Supplement your understanding of machine learning pipelines by following online tutorials
Browse courses on
Machine Learning Pipelines
Show steps
-
Identify reputable resources like Coursera, edX, or YouTube channels specializing in machine learning
-
Select tutorials that cover the end-to-end pipeline process, from data preparation to model evaluation
-
Follow the tutorials step-by-step, implementing the concepts in your own coding environment
Solve coding exercises on machine learning algorithms
Show steps
Strengthen your problem-solving skills and reinforce your understanding of machine learning algorithms
Browse courses on
Machine Learning Algorithms
Show steps
-
Utilize online platforms like LeetCode, HackerRank, or Kaggle
-
Select exercises focused on machine learning algorithms covered in the course
-
Attempt to solve the exercises independently
-
Review solutions and identify areas for improvement
Practice building end-to-end machine learning pipelines
Show steps
Gain practical experience by building and testing end-to-end machine learning pipelines on your own.
Browse courses on
Machine Learning Workflow
Show steps
-
Choose a dataset and define a machine learning problem.
-
Implement data preprocessing, feature engineering, and model training steps.
-
Evaluate the performance of your pipeline.
Create a data visualization of a real-world dataset
Show steps
Apply your knowledge of data visualization to a practical scenario, reinforcing your understanding of data analysis
Browse courses on
Data Visualization
Show steps
-
Find an interesting dataset related to your field of interest
-
Clean and prepare the data for visualization
-
Choose appropriate visualization techniques to represent the data effectively
-
Create the visualization using tools like Tableau, Power BI, or Python libraries
-
Present your visualization, explaining the insights and patterns you observed
Develop a presentation on a specific aspect of end-to-end machine learning pipelines
Show steps
Solidify your understanding by teaching others about a particular aspect of end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
-
Choose a specific topic within the course content to focus on.
-
Research and gather relevant information.
-
Create a presentation outline and slides.
-
Practice presenting your content.
Mentor a junior student or colleague learning machine learning
Show steps
Solidify your understanding by explaining concepts to others and providing guidance
Browse courses on
Machine Learning
Show steps
-
Identify a student or colleague who is new to machine learning
-
Offer your support and guidance, answering their questions and providing resources
-
Review concepts together, reinforcing your own understanding
-
Provide constructive feedback on their progress and encourage their growth
Contribute to open-source projects related to end-to-end machine learning pipelines
Show steps
Gain hands-on experience and contribute to the community by working on open-source projects related to end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
-
Identify open-source projects that align with your interests and skills.
-
Review the project documentation and code.
-
Identify areas where you can contribute, such as bug fixes or feature enhancements.
-
Submit your contributions to the project.
Attend a workshop on advanced techniques in end-to-end machine learning pipelines
Show steps
Expand your knowledge and skills by attending a workshop focused on advanced techniques in end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
-
Research and identify workshops relevant to your interests.
-
Register for and attend the workshop.
-
Actively participate in discussions and hands-on exercises.
Review basic probability and statistics concepts
Show steps
Ensure a strong foundation in probability and statistics, essential for understanding machine learning algorithms
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review your notes or textbook from previous statistics courses
- Go through online resources or tutorials on probability and statistics
- Solve practice problems to test your understanding
Refresh Python skills
Show steps
Review Python syntax and basics to ensure you have a solid foundation for the course content.
Show steps
Show steps
Show steps
- Review Python fundamentals, such as data types, variables, operators, and control flow.
- Practice solving simple coding problems on platforms like Leetcode or Hackerrank.
Organize your study materials for easy reference
Show steps
Maintain a well-organized system for your course materials to enhance your learning
Show steps
Show steps
Show steps
- Gather all your course materials, including lecture notes, assignments, and readings
- Create a dedicated folder or notebook for each topic
- File and label your materials systematically for easy retrieval
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review the basic Python syntax
Show steps
Sharpen your understanding of Python syntax to prepare for the course's hands-on programming
Browse courses on
Python Syntax
Show steps
Show steps
Show steps
- Go through official Python tutorials
- Review your notes from previous Python courses or books
- Practice simple coding exercises on websites like Codewars or HackerRank
Participate in online discussion forums related to machine learning
Show steps
Engage with peers and experts to exchange ideas, ask questions, and expand your knowledge
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify active online forums or communities dedicated to machine learning
- Participate in discussions, asking thoughtful questions and sharing insights
- Read and respond to posts from other members, learning from their experiences and perspectives
Complete online tutorials on end-to-end machine learning pipelines
Show steps
Deepen your understanding of the end-to-end process of building machine learning pipelines by following guided tutorials.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Find reputable online tutorials from platforms like Coursera, edX, or Udacity.
- Follow along with the tutorials, completing hands-on exercises and projects.
Follow tutorials on machine learning pipelines
Show steps
Supplement your understanding of machine learning pipelines by following online tutorials
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Identify reputable resources like Coursera, edX, or YouTube channels specializing in machine learning
- Select tutorials that cover the end-to-end pipeline process, from data preparation to model evaluation
- Follow the tutorials step-by-step, implementing the concepts in your own coding environment
Solve coding exercises on machine learning algorithms
Show steps
Strengthen your problem-solving skills and reinforce your understanding of machine learning algorithms
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Utilize online platforms like LeetCode, HackerRank, or Kaggle
- Select exercises focused on machine learning algorithms covered in the course
- Attempt to solve the exercises independently
- Review solutions and identify areas for improvement
Practice building end-to-end machine learning pipelines
Show steps
Gain practical experience by building and testing end-to-end machine learning pipelines on your own.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Choose a dataset and define a machine learning problem.
- Implement data preprocessing, feature engineering, and model training steps.
- Evaluate the performance of your pipeline.
Create a data visualization of a real-world dataset
Show steps
Apply your knowledge of data visualization to a practical scenario, reinforcing your understanding of data analysis
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find an interesting dataset related to your field of interest
- Clean and prepare the data for visualization
- Choose appropriate visualization techniques to represent the data effectively
- Create the visualization using tools like Tableau, Power BI, or Python libraries
- Present your visualization, explaining the insights and patterns you observed
Develop a presentation on a specific aspect of end-to-end machine learning pipelines
Show steps
Solidify your understanding by teaching others about a particular aspect of end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Choose a specific topic within the course content to focus on.
- Research and gather relevant information.
- Create a presentation outline and slides.
- Practice presenting your content.
Mentor a junior student or colleague learning machine learning
Show steps
Solidify your understanding by explaining concepts to others and providing guidance
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a student or colleague who is new to machine learning
- Offer your support and guidance, answering their questions and providing resources
- Review concepts together, reinforcing your own understanding
- Provide constructive feedback on their progress and encourage their growth
Contribute to open-source projects related to end-to-end machine learning pipelines
Show steps
Gain hands-on experience and contribute to the community by working on open-source projects related to end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests and skills.
- Review the project documentation and code.
- Identify areas where you can contribute, such as bug fixes or feature enhancements.
- Submit your contributions to the project.
Attend a workshop on advanced techniques in end-to-end machine learning pipelines
Show steps
Expand your knowledge and skills by attending a workshop focused on advanced techniques in end-to-end machine learning pipelines.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Research and identify workshops relevant to your interests.
- Register for and attend the workshop.
- Actively participate in discussions and hands-on exercises.
Career center
Learners who complete Building a Machine Learning Pipeline will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work with users to identify business needs, and then they design and develop the software that meets those needs. This course can help you build the skills you need to become a Software Engineer by teaching you the fundamentals of software engineering, as well as how to design and develop software applications. If you're interested in a career as a Software Engineer, this course is a great place to start.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work with data scientists to identify business problems that can be solved with machine learning, and then they develop and implement the models that solve those problems. This course can help you build the skills you need to become a Machine Learning Engineer by teaching you the fundamentals of machine learning, as well as how to build and deploy machine learning models. If you're interested in a career as a Machine Learning Engineer, this course is a great place to start.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They work with data to identify trends, patterns, and insights that can help businesses make better decisions. This course can help you build the skills you need to become a Data Scientist by teaching you the fundamentals of data science, as well as how to use data to solve business problems. If you're interested in a career as a Data Scientist, this course is a great place to start.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make better decisions. They work with data to identify trends, patterns, and insights that can help businesses improve their operations and make better decisions. This course can help you build the skills you need to become a Data Analyst by teaching you the fundamentals of data analysis, as well as how to collect, analyze, and interpret data. If you're interested in a career as a Data Analyst, this course is a great place to start.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They work with traders and investors to identify investment opportunities and to manage risk. This course can help you build the skills you need to become a Quantitative Analyst by teaching you the fundamentals of quantitative analysis, as well as how to use mathematical and statistical models to analyze financial data. If you're interested in a career as a Quantitative Analyst, this course is a great place to start.
Project Manager
Project Manager
A Project Manager plans and executes projects. They work with stakeholders to define project scope, schedule, and budget. They also track project progress and identify risks. This course can help you build the skills you need to become a Project Manager by teaching you the fundamentals of project management, as well as how to plan and execute projects. If you're interested in a career as a Project Manager, this course is a great place to start.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes business problems and opportunities. They work with stakeholders to define business requirements and to develop solutions that meet those requirements. This course can help you build the skills you need to become a Business Analyst by teaching you the fundamentals of business analysis, as well as how to identify and analyze business problems and opportunities. If you're interested in a career as a Business Analyst, this course is a great place to start.
Product Manager
Product Manager
A Product Manager plans and develops new products and features. They work with engineers, designers, and marketers to bring new products to market and to improve existing products. This course can help you build the skills you need to become a Product Manager by teaching you the fundamentals of product management, as well as how to plan and develop new products and features. If you're interested in a career as a Product Manager, this course is a great place to start.
Consultant
Consultant
A Consultant provides advice and expertise to clients on a variety of topics. They work with clients to identify problems and opportunities and to develop solutions that meet their needs. This course can help you build the skills you need to become a Consultant by teaching you the fundamentals of consulting, as well as how to identify and analyze problems and opportunities. If you're interested in a career as a Consultant, this course is a great place to start.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand consumer behavior. They work with businesses to identify marketing opportunities and to develop marketing strategies. This course can help you build the skills you need to become a Market Researcher by teaching you the fundamentals of market research, as well as how to collect and analyze data. If you're interested in a career as a Market Researcher, this course is a great place to start.
Machine Learning Scientist
Machine Learning Scientist
A Machine Learning Scientist researches and develops new machine learning algorithms. They work with businesses and organizations to apply machine learning to solve real-world problems. This course may be helpful for you if you are interested in a career as a Machine Learning Scientist, as it can help you build a foundation in machine learning.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines. They work with data to ensure that it is clean, consistent, and accessible. This course may be helpful for you if you are interested in a career as a Data Engineer, as it can help you build a foundation in data engineering.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. They work with businesses to identify and solve problems that can improve efficiency and profitability. This course may be helpful for you if you are interested in a career as an Operations Research Analyst, as it can help you build a foundation in mathematical and statistical modeling.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They work with businesses and organizations to identify trends and patterns in data and to make predictions. This course may be helpful for you if you are interested in a career as a Statistician, as it can help you build a foundation in statistics.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. They work with clients to identify investment opportunities and to manage risk. This course may be helpful for you if you are interested in a career as a Financial Analyst, as it can help you build a foundation in financial analysis.
For more career information including salaries, visit:
OpenCourser.com/course/tgpwt2/building
Reading list
We've selected 14 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
Building a Machine Learning Pipeline.
Save
Comprehensive reference on deep learning. It covers the latest research and techniques in the field. It's a valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of machine learning pipelines, covering the entire process from data collection to model deployment. It's a valuable reference for anyone looking to build and manage ML pipelines.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers the theory and algorithms of probabilistic machine learning.
Focuses on the practical aspects of building ML systems using Python. It covers topics such as data engineering, model training, and deployment. It's a good choice for those who want to learn how to build real-world ML systems.
Provides a hands-on introduction to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It's a great choice for those who want to learn the fundamentals of ML and get started with building models.
Save
Provides a comprehensive introduction to machine learning from an algorithmic perspective.
Provides a comprehensive introduction to reinforcement learning. It covers the basic concepts and algorithms of RL. It's a good choice for those who want to learn more about RL.
Provides a comprehensive introduction to pattern recognition and machine learning.
Provides a comprehensive introduction to probabilistic graphical models. It covers the theory and algorithms of PGMs. It's a good choice for those who want to learn more about PGMs.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers the theory and algorithms of Bayesian methods. It's a good choice for those who want to learn more about Bayesian methods.
Provides a comprehensive introduction to information theory, inference, and learning algorithms.
Provides a practical introduction to machine learning for hackers.
Provides a concise introduction to machine learning.
Provides a collection of recipes for machine learning with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tgpwt2/building
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Effort
4 weeks
Via
Udacity
Language
English
Good to know
Teaches professionals to build end-to-end ML pipelines for predicting short-term rental prices in New York City, which is core to the real estate domain
Builds a solid understanding of the concepts, principles, and best practices of machine learning pipelines
Taught by Udacity, a recognized provider of online training courses in the tech industry
Requires fluency in written and spoken English, which may be a barrier for non-native speakers
Assumes prior knowledge in command line interface basics, intermediate Python, and basic machine learning, which may not be suitable for complete beginners
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