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Stacey McBrine, Megan Smith Branch, and Sarah Haq

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems.

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This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems.

To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.

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What's inside

Syllabus

Prepare to Train a Machine Learning Model
In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.
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Develop Classification Models
The first type of machine learning task you'll build models for is classification. Classification has many applications across many different fields, so it's a good starting point. In this module, you'll train classification models, tune those models, and then evaluate them as part of a process of iterative improvement.
Develop Regression Models
The next major machine learning task you'll undertake is regression. Whereas classification is about placing things in categories, regression is about estimating numbers. As with the previous module, in this module you'll train, tune, and then evaluate models that perform regression.
Develop Clustering Models
You've built supervised learning models using both classification and regression. But now it's time to work with unsupervised learning, where labeled data is not readily available. In this module, you'll implement unsupervised learning in the form of clustering models, which can group observations that share common traits. Just like before, you'll develop these models as a process of training, tuning, and evaluation.
Apply What You've Learned
You have developed models for classification, regression and clustering, in this module you will apply what you have learned working within a practical scenario. Using a Jupyter notebook you will perform machine learning tasks. You are given the choice of three notebooks, each of which leverages a different type of algorithm.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
This course is designed for business professionals who wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems
Suitable for business professionals who have a background in computing technology
Provides hands-on experience with machine learning tasks using Jupyter notebooks

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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 Train Machine Learning Models with these activities:
Review statistical concepts
This course assumes that you have a working knowledge of basic statistical concepts. Review these concepts to ensure that you are ready to succeed in this course.
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  • Review the concepts of probability and statistics.
  • Practice solving statistical problems.
Review Python basics
This course requires a basic understanding of Python. Review the basics to ensure that you are ready to succeed in this course.
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  • Review the syntax of Python.
  • Practice writing simple Python programs.
Learn different regression algorithms
Tutorials provide step-by-step guidance on how to perform important tasks in the field of machine learning.
Browse courses on Regression
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  • Find 3 different regression algorithms that are covered in the course.
  • Find a tutorial or video that explains the algorithm's benefits and implementation.
  • Follow along with the tutorial and implement the algorithm in your own environment.
  • Write the steps of the algorithm in your own words.
Five other activities
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Attempt practice problems on regression
Practice problems solitify your understanding of topics through practical application and exposure.
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  • Review class coverage of regression.
  • Find a list of practice problems or a graded quiz online.
  • Solve at least twenty (20) practice problems.
  • Check your answers against the answer set
  • For incorrect answers, review the problem and material.
Develop a data visualization for a regression model
Developing a data visualization is a great way to demonstrate your understanding of a regression model and communicate your findings to others.
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  • Choose a regression model that you have built.
  • Identify the key insights that you want to communicate with your visualization.
  • Select a data visualization tool, such as Tableau or Power BI.
  • Create a visualization that effectively communicates your insights.
  • Share your visualization with others and get feedback.
Write a blog post about a machine learning project you have worked on
Writing a blog post about a machine learning project you have worked on is a great way to share your knowledge and insights with others.
Browse courses on Machine Learning
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  • Choose a machine learning project that you have worked on.
  • Write a blog post about your project, including the following information:
  • The problem that you were trying to solve.
  • The data that you used.
  • The algorithm that you used.
  • The results that you achieved.
Contribute to an open-source machine learning project
Contributing to an open-source machine learning project is a great way to gain experience and learn from others.
Browse courses on Machine Learning
Show steps
  • Find an open-source machine learning project that you are interested in.
  • Read the project's documentation and contribute something.
  • Communicate with the project maintainers.
Mentor a junior machine learning engineer
Mentoring a junior machine learning engineer is a great way to give back to the community and help others to succeed.
Browse courses on Machine Learning
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  • Find a junior machine learning engineer who is looking for a mentor.
  • Meet with the mentee regularly to provide guidance and support.
  • Help the mentee to develop their skills and knowledge.

Career center

Learners who complete Train Machine Learning Models will develop knowledge and skills that may be useful to these careers:
Data Scientist
As a Data Scientist, you'll use machine learning to solve complex business problems. This course can help you build a foundation in machine learning, which is essential for success in this role. You'll learn how to train, tune, and evaluate machine learning models, which will give you the skills you need to develop and implement data-driven solutions.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Data Analyst
Data Analysts use machine learning to analyze data and extract insights. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Business Analyst
Business Analysts use machine learning to solve business problems. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Software Engineer
Software Engineers use machine learning to develop software applications. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Product Manager
Product Managers use machine learning to develop and manage products. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Operations Research Analyst
Operations Research Analysts use machine learning to solve operational problems. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Financial Analyst
Financial Analysts use machine learning to analyze financial data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Marketing Analyst
Marketing Analysts use machine learning to analyze marketing data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Healthcare Analyst
Healthcare Analysts use machine learning to analyze healthcare data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Risk Analyst
Risk Analysts use machine learning to analyze risk data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Actuary
Actuaries use machine learning to analyze insurance data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Quantitative Analyst
Quantitative Analysts use machine learning to analyze financial data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Statistician
Statisticians use machine learning to analyze data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Data Engineer
Data Engineers use machine learning to build and maintain data pipelines. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.

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 Train Machine Learning Models.
Provides a practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for those who want to gain hands-on experience with these tools and build their own machine learning models.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn the basics of machine learning.
Provides a rigorous introduction to machine learning from a probabilistic perspective. It valuable resource for those who want to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of deep learning, a subfield of machine learning that has revolutionized many fields in recent years. It valuable resource for those who want to learn about the latest advances in deep learning.
Provides a practical guide to machine learning for business professionals. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to solve business problems.
Provides a practical guide to machine learning for predictive data analytics. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics, from supervised learning to unsupervised learning, and valuable resource for those who want to learn about the underlying algorithms that power machine learning.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn the basics of machine learning with Python.
Provides a non-technical overview of machine learning. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn about machine learning without getting bogged down in the technical details.
Provides a practical guide to machine learning using a variety of programming languages. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive overview of machine learning for the web. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to improve their websites and web applications.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn about the latest advances in machine learning.

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