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Adwith Malpe
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
Read more
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
Read more
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
Register for this course and see more details by visiting:
OpenCourser.com/course/q3loh6/introduction
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What's inside
Syllabus
Course Introduction
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
Read more
Syllabus
Course Introduction
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models.
You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!
Read more
Module 1: Introduction to Machine Learning
In this module you will learn about machine learning and how each branch of machine learning works in Python.
Module 2: More Supervised Learning Algorithms
In this module, you will learn about two other supervised machine learning models: k-nearest neighbors (kNN) and support vector machines (SVM). You will learn under which conditions you’d use these two models. You will also learn about unsupervised machine learning models and how they work.
Module 3: Advanced Machine Learning Topics
In this module, you will gain an overview of advanced machine learning topics, including deep learning, image processing, and generative adversarial networks (GANs).
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores supervised learning, unsupervised learning, deep learning, image processing, and GANs, which are foundational in machine learning today
Taught by Adwith Malpe, a seasoned instructor in machine learning
Develops machine learning models using Python, a highly relevant language in industry
Examines a range of machine learning algorithms, which helps learners tackle a variety of real-world problems
Requires no prior programming or computer science background, making it accessible to beginners
May require additional software or tools that learners may need to purchase, which could pose a financial barrier for some
Save this course
Save Introduction to Machine Learning with Python to your list so you can find it easily later:
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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 Introduction to Machine Learning with Python with these
activities:
Compile a collection of machine learning resources
Show steps
Organizing and maintaining a repository of valuable resources will provide ongoing support and facilitate continued learning.
Browse courses on
Online Courses
Show steps
-
Identify and gather relevant articles, tutorials, and documentation.
-
Organize the resources into a structured collection.
-
Share the collection with other learners or store it for personal use.
Review statistics basics
Show steps
Recalling the fundamentals of statistics will strengthen your foundational understanding for this course.
Show steps
-
Review concepts of probability and distributions.
-
Practice solving probability problems.
-
Review hypothesis testing methods.
Join a machine learning study group
Show steps
Engaging with peers in a study group will provide opportunities for discussion, knowledge exchange, and support.
Browse courses on
Collaboration
Show steps
-
Find or create a study group focused on machine learning.
-
Set regular meeting times and establish a study plan.
-
Collaborate on projects, discuss concepts, and share resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete Python coding exercises
Show steps
Reinforcing your Python skills through practice will enhance your ability to implement machine learning algorithms effectively.
Browse courses on
Python Programming
Show steps
-
Find online coding challenges or platforms.
-
Select exercises that cover Python concepts relevant to machine learning.
-
Solve the exercises and debug your code.
Build a simple machine learning model
Show steps
Working through the process of creating a machine learning model will build your confidence in applying theories to real datasets.
Show steps
-
Choose a simple dataset.
-
Select a suitable learning algorithm.
-
Train and evaluate the model using Python.
Create a visual representation of machine learning concepts
Show steps
Creating visual representations of machine learning concepts will enhance your understanding and ability to communicate them to others.
Browse courses on
Data Visualization
Show steps
-
Choose a specific machine learning concept.
-
Design a visual representation (e.g., diagram, flowchart, infographic).
-
Create the visual representation using suitable tools.
Explore advanced machine learning topics
Show steps
Extending your understanding of machine learning through tutorials will expand your knowledge and prepare you for more advanced concepts.
Show steps
-
Identify a specific advanced topic of interest.
-
Find reputable tutorials or online courses on the topic.
-
Follow the tutorials and complete the exercises.
Mentor junior learners in machine learning
Show steps
Mentoring others will reinforce your knowledge, develop your communication skills, and contribute to the community.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior learners.
-
Provide guidance, support, and feedback on their learning journey.
-
Create or participate in mentoring programs.
Compile a collection of machine learning resources
Show steps
Organizing and maintaining a repository of valuable resources will provide ongoing support and facilitate continued learning.
Browse courses on
Online Courses
Show steps
Show steps
Show steps
- Identify and gather relevant articles, tutorials, and documentation.
- Organize the resources into a structured collection.
- Share the collection with other learners or store it for personal use.
Review statistics basics
Show steps
Recalling the fundamentals of statistics will strengthen your foundational understanding for this course.
Show steps
Show steps
Show steps
- Review concepts of probability and distributions.
- Practice solving probability problems.
- Review hypothesis testing methods.
Join a machine learning study group
Show steps
Engaging with peers in a study group will provide opportunities for discussion, knowledge exchange, and support.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find or create a study group focused on machine learning.
- Set regular meeting times and establish a study plan.
- Collaborate on projects, discuss concepts, and share resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete Python coding exercises
Show steps
Reinforcing your Python skills through practice will enhance your ability to implement machine learning algorithms effectively.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Find online coding challenges or platforms.
- Select exercises that cover Python concepts relevant to machine learning.
- Solve the exercises and debug your code.
Build a simple machine learning model
Show steps
Working through the process of creating a machine learning model will build your confidence in applying theories to real datasets.
Show steps
Show steps
Show steps
- Choose a simple dataset.
- Select a suitable learning algorithm.
- Train and evaluate the model using Python.
Create a visual representation of machine learning concepts
Show steps
Creating visual representations of machine learning concepts will enhance your understanding and ability to communicate them to others.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a specific machine learning concept.
- Design a visual representation (e.g., diagram, flowchart, infographic).
- Create the visual representation using suitable tools.
Explore advanced machine learning topics
Show steps
Extending your understanding of machine learning through tutorials will expand your knowledge and prepare you for more advanced concepts.
Show steps
Show steps
Show steps
- Identify a specific advanced topic of interest.
- Find reputable tutorials or online courses on the topic.
- Follow the tutorials and complete the exercises.
Mentor junior learners in machine learning
Show steps
Mentoring others will reinforce your knowledge, develop your communication skills, and contribute to the community.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners.
- Provide guidance, support, and feedback on their learning journey.
- Create or participate in mentoring programs.
Career center
Learners who complete Introduction to Machine Learning with Python 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 machine learning models. They work with data scientists to identify the right machine learning algorithms for a given problem and then implement and deploy those models. A background in programming languages like Python is essential for these roles. This course provides a comprehensive introduction to machine learning in Python, which includes supervised learning, unsupervised learning, and deep learning. Additionally, the course covers advanced topics like image processing and generative adversarial networks (GANs), which are important areas in machine learning today.
Data Scientist
Data Scientist
Data Scientists use machine learning and statistical techniques to extract insights from data. They work with businesses to help them make better decisions by identifying patterns and trends in data. Machine learning is a large part of data science, and Python is one of the most common programming languages used in the field. This course provides a solid foundation in machine learning with Python, which can help you on your journey to becoming a successful Data Scientist.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning and statistical techniques to analyze financial data and make investment decisions. Python is a popular programming language in quantitative finance, and machine learning is increasingly being used to develop trading strategies and risk management models. This course can help you build a solid foundation in machine learning with Python, which is a valuable skill for Quantitative Analysts.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. Machine learning is increasingly being used in data analysis to automate tasks and improve the accuracy of analysis. This course provides a good introduction to machine learning with Python, which is a popular programming language in data analysis. By learning the concepts of machine learning, you can make yourself a more effective and efficient Data Analyst.
Statistician
Statistician
Statisticians use data to analyze and interpret data. Machine learning is increasingly being used in statistics to automate tasks and improve the accuracy of analysis. This course provides a good foundation in machine learning with Python, which is a valuable skill for Statisticians.
Actuary
Actuary
Actuaries use data to assess and manage risks. Machine learning is increasingly being used in actuarial science to automate tasks and improve the accuracy of analysis. While this course does not directly focus on actuarial science, it provides a good foundation in machine learning with Python, which is a valuable skill for Actuaries.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. Machine learning is increasingly being used in business analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on business analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Business Analysts.
Data Architect
Data Architect
Data Architects design and build data systems. Machine learning is increasingly being used in data architecture to automate tasks and improve the performance of data systems. While this course does not directly focus on data architecture, it provides a good foundation in machine learning with Python, which is a valuable skill for Data Architects.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment decisions. Machine learning is increasingly being used in financial analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on financial analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Financial Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. Machine learning is increasingly being used in risk analysis to automate tasks and improve the accuracy of analysis. While this course does not directly focus on risk analysis, it provides a good foundation in machine learning with Python, which is a valuable skill for Risk Analysts.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. Machine learning is increasingly being used in data engineering to automate tasks and improve the efficiency of data pipelines. While this course does not directly focus on data engineering, it provides a good foundation in machine learning with Python, which is a valuable skill for Data Engineers.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. Machine learning is increasingly being used to automate tasks and improve the efficiency of operations. While this course does not focus on operations management, it provides a good foundation in machine learning with Python, which is a valuable skill for Operations Managers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. Machine learning is increasingly being used to automate marketing tasks and improve the effectiveness of marketing campaigns. While this course does not focus on marketing, it provides a good foundation in machine learning with Python, which can be a valuable skill for Marketing Managers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Machine learning is increasingly being used in software development to improve the performance and efficiency of software applications. This course provides a good foundation in machine learning with Python, which is a widely-used programming language in software development. By learning the concepts of machine learning, you can make yourself a more valuable and in-demand Software Engineer.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. Machine learning is increasingly being used to develop new products and improve existing products. While this course does not focus on product management, it provides a good foundation in machine learning with Python, which is a valuable skill for Product Managers.
For more career information including salaries, visit:
OpenCourser.com/course/q3loh6/introduction
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
Introduction to Machine Learning with Python.
Provides a comprehensive introduction to machine learning with Python, covering various supervised and unsupervised learning algorithms. It also covers essential topics such as data preprocessing, model evaluation, and hyperparameter tuning, making it a great companion to this course.
Offers a practical approach to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides hands-on examples and covers a wide range of machine learning concepts, making it a valuable resource for practicing and solidifying the techniques taught in this course.
As a textbook commonly used in academic institutions, this book provides a thorough introduction to machine learning concepts with a focus on Python implementation. It covers supervised and unsupervised learning, model selection, and evaluation, complementing the content of this course effectively.
Comprehensive guide to deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. While it may go beyond the scope of this course, it provides a solid foundation for those interested in exploring deep learning further.
While this book has a broader scope than this course, it provides a comprehensive introduction to statistical learning methods, including regression, classification, and clustering. Its focus on mathematical foundations and statistical concepts is particularly valuable for those seeking a deeper understanding of the underlying principles.
Offers a rigorous and comprehensive treatment of pattern recognition and machine learning. Its focus on theoretical foundations and mathematical derivations provides a solid grounding for those interested in a deeper understanding of the subject matter.
While it goes beyond the scope of this course, this book provides a unique perspective on machine learning from a Bayesian and optimization standpoint. Its focus on probabilistic models and optimization techniques is valuable for those interested in exploring alternative approaches to machine learning.
Serves as a comprehensive reference for statistical learning methods and algorithms. While it may not be as beginner-friendly as other resources, it provides a thorough treatment of advanced topics and is commonly used as a textbook in graduate-level courses.
Offers a practical and hands-on approach to machine learning, focusing on real-world applications. Its emphasis on code examples and case studies makes it a valuable resource for those interested in implementing and deploying machine learning models.
Offers a beginner-friendly introduction to machine learning concepts, focusing on making the subject accessible to those with little to no prior knowledge.
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Takes an algorithmic approach to machine learning, providing detailed explanations of various algorithms and their applications. Its focus on mathematical foundations is particularly valuable for those interested in understanding the theoretical underpinnings of machine learning.
Offers a comprehensive and rigorous treatment of machine learning theory and algorithms. Its focus on mathematical proofs and derivations provides a solid foundation for those interested in the theoretical aspects of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q3loh6/introduction
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Level
Introductory
Via
Coursera
Institution
Arizona State University
Instructor
Adwith Malpe
Language
English
This course belongs to a
collection
called
Python: A Guided Journey from Introduction to Application. It's comprised of three courses:
- Data Analysis and Visualization with Python
- Introduction to Machine Learning with Python (viewing)
- Object-Oriented Programming and GUI with Python
Good to know
Explores supervised learning, unsupervised learning, deep learning, image processing, and GANs, which are foundational in machine learning today
Taught by Adwith Malpe, a seasoned instructor in machine learning
Develops machine learning models using Python, a highly relevant language in industry
Examines a range of machine learning algorithms, which helps learners tackle a variety of real-world problems
Requires no prior programming or computer science background, making it accessible to beginners
May require additional software or tools that learners may need to purchase, which could pose a financial barrier for some
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