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Xavier Morera
Many problems are solved using Machine Learning. This course will teach you how to pick the ML algorithm that can help you create the right ML model to solve the problem at hand.
Read more
Many problems are solved using Machine Learning. This course will teach you how to pick the ML algorithm that can help you create the right ML model to solve the problem at hand.
Read more
Many problems are solved using Machine Learning. This course will teach you how to pick the ML algorithm that can help you create the right ML model to solve the problem at hand.
There are many ways to solve a problem using Machine Learning. Picking the right algorithm can make the difference between success or “burning down in flames”. In this course, Machine Learning with Python - Practical Application, you’ll learn how to pick the right ML model to solve your real-world problem. First, you’ll explore the characteristics of many real-world problems that can be solved using ML. Next, you’ll discover how each one of the types of algorithms can solve a particular problem and how. Finally, you’ll learn how to pick the right algorithm for your problem. When you’re finished with this course, you’ll have the skills and knowledge of ML needed to get started working on your problem and make the world a better place.
Register for this course and see more details by visiting:
OpenCourser.com/course/0hhwol/machine
What's inside
Syllabus
Course Overview
Getting Your Hands Dirty with Machine Learning
Regression
Classification
Read more
Syllabus
Course Overview
Getting Your Hands Dirty with Machine Learning
Regression
Classification
Read more
Dimensionality Reduction
Clustering
Understanding Other Types of ML Problems
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches learners to use the appropriate algorithm for practical ML applications
Provides a framework for solving real-world ML problems
Involves hands-on practice with ML techniques
Covers various ML concepts, including regression, classification, and clustering
Appropriate for learners with basic ML knowledge seeking to apply it in practice
Course instructor has extensive expertise in ML applications
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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 Python - Practical Application with these
activities:
Organize and review your course materials
Show steps
Improve your understanding of the course material by organizing and reviewing it regularly.
Show steps
-
Create a system for organizing your notes, assignments, quizzes, and exams
-
Review your materials regularly, focusing on the key concepts and ideas
Read 'Data Science for Business'
Show steps
Gain a foundational understanding of the key concepts and techniques used in data science and machine learning.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read Chapter 1-3 of 'Data Science for Business'
-
Complete the exercises at the end of each chapter
Join a Study Group for ML
Show steps
Enhance your understanding through peer interaction and collaboration by joining a study group specifically focused on ML, allowing you to exchange ideas, discuss concepts, and learn from others.
Show steps
-
Identify or create a study group with fellow students or learners.
-
Establish regular meeting times and a study schedule.
-
Prepare for each session by reviewing materials and completing assignments.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Join a study group for the course
Show steps
Enhance your understanding of the course material by discussing it with your peers.
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss the course material, ask questions, and work on problems together
Practice solving machine learning problems
Show steps
Practicing solving machine learning problems will help you develop the skills needed to apply machine learning to real-world problems.
Browse courses on
Problem Solving
Show steps
-
Find a set of machine learning problems to solve, such as the Titanic dataset or the MNIST dataset.
-
Develop a plan for solving the problems, including the algorithms you will use and the metrics you will use to evaluate your results.
-
Implement your plan and solve the problems.
-
Evaluate your results and identify areas for improvement.
Follow Guided ML Tutorials
Show steps
Supplement your classroom learning by following structured ML tutorials, providing step-by-step guidance and practical examples to reinforce your understanding of concepts and techniques.
Browse courses on
Machine Learning Basics
Show steps
-
Identify reputable online platforms or resources offering ML tutorials.
-
Select tutorials that align with your learning objectives.
-
Follow the tutorials thoroughly, implementing the code and understanding the concepts.
Follow along with the 'Machine Learning with Python' tutorial series
Show steps
Learn the basics of machine learning and how to apply it using Python.
Browse courses on
Machine Learning
Show steps
-
Watch the first 5 videos in the 'Machine Learning with Python' tutorial series
-
Complete the practice exercises for each video
Solve practice problems on different machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Machine Learning
Show steps
-
Choose a practice problem related to machine learning
-
Implement the solution in Python
-
Submit your solution and review the feedback
Attend an ML Workshop
Show steps
Enhance your practical skills and deepen your understanding of ML algorithms by attending a workshop led by industry experts, providing hands-on experience and insights.
Browse courses on
ML Algorithms
Show steps
-
Research and identify reputable ML workshops aligned with your learning goals.
-
Register and attend the workshop.
-
Actively participate in the workshop activities and discussions.
-
Network with other participants and industry professionals.
Solve LeetCode problems on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Machine Learning
Show steps
-
Choose a LeetCode problem related to machine learning
-
Implement the solution in Python
-
Submit your solution and review the feedback
Practice Solving ML Problems
Show steps
Test your understanding of ML algorithms by solving real-world problems, solidifying your understanding and improving your problem-solving skills.
Browse courses on
ML Algorithms
Show steps
-
Identify a suitable real-world problem that can be solved with ML.
-
Gather and preprocess the necessary data.
-
Select and implement the appropriate ML algorithm to solve the problem.
-
Evaluate the performance of your ML model.
Create an ML Project
Show steps
Deepen your understanding of the practical applications of ML by developing and implementing your own ML project, allowing you to apply your knowledge and solve a real-world problem.
Browse courses on
Hands-on Experience
Show steps
-
Define the problem you want to solve using ML.
-
Gather and preprocess the necessary data.
-
Research and select an appropriate ML algorithm.
-
Implement and train the ML model.
-
Evaluate and refine your ML model.
Write a blog post about a machine learning project you worked on
Show steps
Solidify your understanding of machine learning by applying it to a real-world problem and sharing your results.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning project to work on
-
Implement the project in Python
-
Write a blog post about your project, including the problem you solved, the approach you took, and the results you achieved
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will help you learn about the software development process and how to collaborate with other developers.
Browse courses on
Open Source
Show steps
-
Find an open-source machine learning project that you are interested in contributing to.
-
Read the project's documentation and get started with the codebase.
-
Identify a bug or feature that you would like to work on.
-
Submit a pull request to the project with your changes.
Contribute to an open-source machine learning project
Show steps
Gain practical experience with machine learning by contributing to an open-source project.
Browse courses on
Machine Learning
Show steps
-
Find an open-source machine learning project to contribute to
-
Identify an issue or feature to work on
-
Implement a solution and submit a pull request
Organize and review your course materials
Show steps
Improve your understanding of the course material by organizing and reviewing it regularly.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, quizzes, and exams
- Review your materials regularly, focusing on the key concepts and ideas
Read 'Data Science for Business'
Show steps
Gain a foundational understanding of the key concepts and techniques used in data science and machine learning.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1-3 of 'Data Science for Business'
- Complete the exercises at the end of each chapter
Join a Study Group for ML
Show steps
Enhance your understanding through peer interaction and collaboration by joining a study group specifically focused on ML, allowing you to exchange ideas, discuss concepts, and learn from others.
Show steps
Show steps
Show steps
- Identify or create a study group with fellow students or learners.
- Establish regular meeting times and a study schedule.
- Prepare for each session by reviewing materials and completing assignments.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Join a study group for the course
Show steps
Enhance your understanding of the course material by discussing it with your peers.
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss the course material, ask questions, and work on problems together
Practice solving machine learning problems
Show steps
Practicing solving machine learning problems will help you develop the skills needed to apply machine learning to real-world problems.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Find a set of machine learning problems to solve, such as the Titanic dataset or the MNIST dataset.
- Develop a plan for solving the problems, including the algorithms you will use and the metrics you will use to evaluate your results.
- Implement your plan and solve the problems.
- Evaluate your results and identify areas for improvement.
Follow Guided ML Tutorials
Show steps
Supplement your classroom learning by following structured ML tutorials, providing step-by-step guidance and practical examples to reinforce your understanding of concepts and techniques.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Identify reputable online platforms or resources offering ML tutorials.
- Select tutorials that align with your learning objectives.
- Follow the tutorials thoroughly, implementing the code and understanding the concepts.
Follow along with the 'Machine Learning with Python' tutorial series
Show steps
Learn the basics of machine learning and how to apply it using Python.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Watch the first 5 videos in the 'Machine Learning with Python' tutorial series
- Complete the practice exercises for each video
Solve practice problems on different machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a practice problem related to machine learning
- Implement the solution in Python
- Submit your solution and review the feedback
Attend an ML Workshop
Show steps
Enhance your practical skills and deepen your understanding of ML algorithms by attending a workshop led by industry experts, providing hands-on experience and insights.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Research and identify reputable ML workshops aligned with your learning goals.
- Register and attend the workshop.
- Actively participate in the workshop activities and discussions.
- Network with other participants and industry professionals.
Solve LeetCode problems on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a LeetCode problem related to machine learning
- Implement the solution in Python
- Submit your solution and review the feedback
Practice Solving ML Problems
Show steps
Test your understanding of ML algorithms by solving real-world problems, solidifying your understanding and improving your problem-solving skills.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Identify a suitable real-world problem that can be solved with ML.
- Gather and preprocess the necessary data.
- Select and implement the appropriate ML algorithm to solve the problem.
- Evaluate the performance of your ML model.
Create an ML Project
Show steps
Deepen your understanding of the practical applications of ML by developing and implementing your own ML project, allowing you to apply your knowledge and solve a real-world problem.
Browse courses on
Hands-on Experience
Show steps
Show steps
Show steps
- Define the problem you want to solve using ML.
- Gather and preprocess the necessary data.
- Research and select an appropriate ML algorithm.
- Implement and train the ML model.
- Evaluate and refine your ML model.
Write a blog post about a machine learning project you worked on
Show steps
Solidify your understanding of machine learning by applying it to a real-world problem and sharing your results.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning project to work on
- Implement the project in Python
- Write a blog post about your project, including the problem you solved, the approach you took, and the results you achieved
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will help you learn about the software development process and how to collaborate with other developers.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source machine learning project that you are interested in contributing to.
- Read the project's documentation and get started with the codebase.
- Identify a bug or feature that you would like to work on.
- Submit a pull request to the project with your changes.
Contribute to an open-source machine learning project
Show steps
Gain practical experience with machine learning by contributing to an open-source project.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source machine learning project to contribute to
- Identify an issue or feature to work on
- Implement a solution and submit a pull request
Career center
Learners who complete Machine Learning with Python - Practical Application will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain ML models. This course will teach them how to choose the right ML algorithm for their specific project, ensuring they build models that are accurate and efficient.
Data Scientist
Data Scientist
Data Scientists use ML to solve real-world problems. This course will help them understand the different types of ML algorithms and how to apply them to their work. It will also teach them how to evaluate the performance of ML models, ensuring they are using the best possible model for their needs.
Software Engineer
Software Engineer
Software Engineers who work on ML projects will benefit from this course, as it will teach them how to choose the right ML algorithm and implement it in their code. This will help them develop software that is more accurate and efficient.
Product Manager
Product Manager
Product Managers who are responsible for ML products will find this course helpful, as it will teach them how to evaluate the performance of ML models and make decisions about which models to use in their products.
Business Analyst
Business Analyst
Business Analysts who work on projects that involve ML can benefit from this course, as it will teach them how to communicate with ML engineers and make informed decisions about ML projects.
Data Analyst
Data Analyst
Data Analysts use their knowledge of ML to gather, clean, and analyze data, transforming it into actionable insights. This course is especially suited for Data Analysts as it provides a deep dive into the various ML algorithms, helping them choose the right model for the problem they are trying to solve.
Project Manager
Project Manager
Project Managers who are responsible for ML projects will find this course helpful, as it will teach them how to plan and execute ML projects successfully.
Data Engineer
Data Engineer
Data Engineers who work with ML data will find this course helpful, as it will teach them how to prepare and manage data for ML models.
Statistician
Statistician
Statisticians who work with ML data will find this course helpful, as it will teach them how to apply statistical techniques to ML problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts who work on ML projects will find this course helpful, as it will teach them how to apply ML techniques to operations research problems.
Financial Analyst
Financial Analyst
Financial Analysts who work with ML data will find this course helpful, as it will teach them how to apply ML techniques to financial problems.
Marketing Analyst
Marketing Analyst
Marketing Analysts who work with ML data will find this course helpful, as it will teach them how to apply ML techniques to marketing problems.
Sales Analyst
Sales Analyst
Sales Analysts who work with ML data will find this course helpful, as it will teach them how to apply ML techniques to sales problems.
Customer Success Manager
Customer Success Manager
Customer Success Managers who work with ML products will find this course helpful, as it will teach them how to understand and communicate the value of ML to customers.
Technical Writer
Technical Writer
Technical Writers who write about ML topics will find this course helpful, as it will teach them how to understand and explain ML concepts clearly.
For more career information including salaries, visit:
OpenCourser.com/course/0hhwol/machine
Reading list
We've selected ten 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 Python - Practical Application.
Comprehensive introduction to deep learning with Python. It covers a wide range of topics, from the basics of deep learning to advanced topics such as convolutional neural networks and recurrent neural networks.
Provides practical guidance on using popular machine learning libraries in Python. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Comprehensive introduction to machine learning with Python. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning.
Comprehensive introduction to natural language processing with Python. It covers a wide range of topics, from the basics of natural language processing to advanced topics such as machine translation and information retrieval.
Comprehensive introduction to data analysis with Python. It covers a wide range of topics, from the basics of data analysis to advanced topics such as machine learning and deep learning.
Comprehensive introduction to machine learning with R. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning.
Practical guide to machine learning for hackers. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning, and provides practical examples of how to use machine learning in Python.
Comprehensive introduction to machine learning for finance. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning, and provides practical examples of how to use machine learning in Python.
Practical guide to machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation, and provides hands-on examples of how to use machine learning in Python.
Comprehensive introduction to reinforcement learning. It covers the basics of reinforcement learning, including Markov decision processes and value iteration, and provides practical examples of how to use reinforcement learning in Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0hhwol/machine
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Time
116 hours
Level
Introductory
Via
Pluralsight
Instructor
Xavier Morera
Language
English
Good to know
Teaches learners to use the appropriate algorithm for practical ML applications
Provides a framework for solving real-world ML problems
Involves hands-on practice with ML techniques
Covers various ML concepts, including regression, classification, and clustering
Appropriate for learners with basic ML knowledge seeking to apply it in practice
Course instructor has extensive expertise in ML applications
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