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Take our Machine Learning and Artificial Intelligence Basics course and learn foundational ML and AI concepts including how they work and how to use them to solve problems.
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What's inside
Syllabus
Discover Artificial Intelligence and Machine Learning, learn about how they work, and find out how they are used to solve problems in new ways that were never possible in the past.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for understanding the role of Artificial Intelligence and Machine Learning in solving problems
Provides a foundational overview of Artificial Intelligence and Machine Learning for beginners
Explores the practical applications of Artificial Intelligence and Machine Learning across various industries
Taught by industry experts, offering real-world insights and practical knowledge
Facilitates networking and collaboration with fellow learners through online discussions and forums
Provides hands-on exercises and projects to reinforce learning and encourage practical application
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 Discovering Artificial Intelligence and Machine Learning with these
activities:
Review Linear Algebra and Statistics
Show steps
Strengthen your foundational math skills for better comprehension of ML concepts.
Browse courses on
Linear Algebra
Show steps
-
Review key concepts in linear algebra, such as vectors, matrices, and transformations.
-
Brush up on statistical concepts, including probability, distributions, and hypothesis testing.
Organize Course Resources
Show steps
Gather and organize all relevant course materials, including notes, assignments, quizzes, and exams, into a central location. This will enhance your ability to locate and review important information throughout the course.
Browse courses on
Learning Materials
Show steps
-
Create a dedicated folder or notebook for course materials
-
Organize materials by topic or module
-
Review and update your materials regularly
Join a Study Group
Show steps
Enhance your understanding by discussing concepts with fellow learners.
Browse courses on
Collaborative Learning
Show steps
-
Find or create a study group with peers in the course.
-
Meet regularly to discuss course material, share insights, and work through problems.
-
Provide support and encouragement to each other during the learning process.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Coursera Specialization: Machine Learning
Show steps
Supplement your understanding of Machine Learning concepts by following guided tutorials from a reputable source. This will provide you with a structured approach to learning and reinforce your knowledge.
Show steps
-
Enroll in the Coursera Machine Learning Specialization
-
Complete the video lectures and readings
-
Participate in the discussion forums
Read 'Machine Learning for Dummies' by John Paul Mueller and Luca Massaron
Show steps
Deepen your understanding of the foundational concepts of machine learning, including supervised and unsupervised learning.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Read Chapter 1-3 to understand the basics of Machine Learning
-
Read the case studies in Chapter 2 to see how ML is used in the real world
-
Solve the practice problems at the end of each chapter
Participate in online discussion forums
Show steps
Engage with other learners and experts to exchange ideas, ask questions, and enhance your understanding.
Browse courses on
Machine Learning
Show steps
-
Join online forums related to machine learning
-
Participate in discussions and ask thoughtful questions
-
Share your knowledge and help others
Explore Hands-on Machine Learning Projects
Show steps
Develop practical skills by completing guided ML projects.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a project that aligns with your interests or career goals.
-
Follow step-by-step tutorials to implement the project.
-
Troubleshoot any issues you encounter along the way.
-
Present your completed project to demonstrate your understanding.
Follow the 'Machine Learning Crash Course' series by Google Developers
Show steps
Gain hands-on experience with machine learning techniques using TensorFlow, a popular open-source machine learning library.
Browse courses on
Machine Learning
Show steps
-
Watch the videos in the series to learn the basics of machine learning
-
Follow along with the code examples to build your own machine learning models
-
Complete the quizzes at the end of each video to test your understanding
Practice Machine Learning Algorithms
Show steps
Gain proficiency in applying ML algorithms to solve real-world problems.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a dataset relevant to a problem you want to solve.
-
Select and implement appropriate ML algorithms.
-
Evaluate the performance of your models and make adjustments as needed.
Kaggle Competition: Titanic Survival Prediction
Show steps
Gain practical experience in applying Machine Learning techniques to a real-world dataset. This will enhance your understanding of supervised learning and regression analysis.
Browse courses on
Supervised Learning
Show steps
-
Review the Titanic dataset
-
Choose and implement appropriate Machine Learning algorithms
-
Evaluate and refine your models
Solve practice problems on LeetCode or HackerRank
Show steps
Enhance your problem-solving skills and reinforce your understanding of machine learning algorithms.
Browse courses on
Algorithms
Show steps
-
Choose a set of problems related to machine learning
-
Solve the problems using your preferred programming language
-
Review your solutions and identify areas for improvement
Participate in Kaggle competitions related to machine learning
Show steps
Challenge yourself, test your skills, and learn from others by participating in real-world machine learning competitions.
Browse courses on
Machine Learning
Show steps
-
Identify Kaggle competitions related to your interests
-
Download the data and familiarize yourself with the problem
-
Build and train your machine learning models
-
Submit your predictions and track your progress on the leaderboard
Build a Machine Learning-Powered Application
Show steps
Deepen your understanding by applying ML to create a functional application.
Browse courses on
Machine Learning Applications
Show steps
-
Define the problem you want to address with your application.
-
Design and implement the application using ML techniques.
-
Test and refine your application to ensure it meets user needs.
-
Deploy your application and monitor its performance.
Notebook of use cases
Show steps
Synthesize your knowledge of the different Machine Learning algorithms and their use cases into a notebook. This will solidify your understanding of the real-world applicability of these algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify different Machine Learning algorithms
-
Research use cases and applications for each algorithm
-
Document your findings in a well-organized notebook
Volunteer at a local AI or machine learning organization
Show steps
Gain practical experience, network with professionals, and contribute to the advancement of machine learning.
Browse courses on
Machine Learning
Show steps
-
Identify local organizations involved in AI or machine learning
-
Contact the organizations and inquire about volunteer opportunities
-
Attend volunteer events and contribute your skills
Contribute to open-source machine learning projects on GitHub
Show steps
Gain experience in collaborative development, learn from others, and contribute to the growth of the machine learning community.
Browse courses on
Machine Learning
Show steps
-
Identify open-source machine learning projects on GitHub
-
Review the project documentation and identify areas where you can contribute
-
Create a pull request with your proposed changes
-
Respond to feedback and iterate on your contributions
Review Linear Algebra and Statistics
Show steps
Strengthen your foundational math skills for better comprehension of ML concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review key concepts in linear algebra, such as vectors, matrices, and transformations.
- Brush up on statistical concepts, including probability, distributions, and hypothesis testing.
Organize Course Resources
Show steps
Gather and organize all relevant course materials, including notes, assignments, quizzes, and exams, into a central location. This will enhance your ability to locate and review important information throughout the course.
Browse courses on
Learning Materials
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials
- Organize materials by topic or module
- Review and update your materials regularly
Join a Study Group
Show steps
Enhance your understanding by discussing concepts with fellow learners.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Find or create a study group with peers in the course.
- Meet regularly to discuss course material, share insights, and work through problems.
- Provide support and encouragement to each other during the learning process.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Coursera Specialization: Machine Learning
Show steps
Supplement your understanding of Machine Learning concepts by following guided tutorials from a reputable source. This will provide you with a structured approach to learning and reinforce your knowledge.
Show steps
Show steps
Show steps
- Enroll in the Coursera Machine Learning Specialization
- Complete the video lectures and readings
- Participate in the discussion forums
Read 'Machine Learning for Dummies' by John Paul Mueller and Luca Massaron
Show steps
Deepen your understanding of the foundational concepts of machine learning, including supervised and unsupervised learning.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1-3 to understand the basics of Machine Learning
- Read the case studies in Chapter 2 to see how ML is used in the real world
- Solve the practice problems at the end of each chapter
Participate in online discussion forums
Show steps
Engage with other learners and experts to exchange ideas, ask questions, and enhance your understanding.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join online forums related to machine learning
- Participate in discussions and ask thoughtful questions
- Share your knowledge and help others
Explore Hands-on Machine Learning Projects
Show steps
Develop practical skills by completing guided ML projects.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a project that aligns with your interests or career goals.
- Follow step-by-step tutorials to implement the project.
- Troubleshoot any issues you encounter along the way.
- Present your completed project to demonstrate your understanding.
Follow the 'Machine Learning Crash Course' series by Google Developers
Show steps
Gain hands-on experience with machine learning techniques using TensorFlow, a popular open-source machine learning library.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Watch the videos in the series to learn the basics of machine learning
- Follow along with the code examples to build your own machine learning models
- Complete the quizzes at the end of each video to test your understanding
Practice Machine Learning Algorithms
Show steps
Gain proficiency in applying ML algorithms to solve real-world problems.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a dataset relevant to a problem you want to solve.
- Select and implement appropriate ML algorithms.
- Evaluate the performance of your models and make adjustments as needed.
Kaggle Competition: Titanic Survival Prediction
Show steps
Gain practical experience in applying Machine Learning techniques to a real-world dataset. This will enhance your understanding of supervised learning and regression analysis.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Review the Titanic dataset
- Choose and implement appropriate Machine Learning algorithms
- Evaluate and refine your models
Solve practice problems on LeetCode or HackerRank
Show steps
Enhance your problem-solving skills and reinforce your understanding of machine learning algorithms.
Browse courses on
Algorithms
Show steps
Show steps
Show steps
- Choose a set of problems related to machine learning
- Solve the problems using your preferred programming language
- Review your solutions and identify areas for improvement
Participate in Kaggle competitions related to machine learning
Show steps
Challenge yourself, test your skills, and learn from others by participating in real-world machine learning competitions.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify Kaggle competitions related to your interests
- Download the data and familiarize yourself with the problem
- Build and train your machine learning models
- Submit your predictions and track your progress on the leaderboard
Build a Machine Learning-Powered Application
Show steps
Deepen your understanding by applying ML to create a functional application.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Define the problem you want to address with your application.
- Design and implement the application using ML techniques.
- Test and refine your application to ensure it meets user needs.
- Deploy your application and monitor its performance.
Notebook of use cases
Show steps
Synthesize your knowledge of the different Machine Learning algorithms and their use cases into a notebook. This will solidify your understanding of the real-world applicability of these algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify different Machine Learning algorithms
- Research use cases and applications for each algorithm
- Document your findings in a well-organized notebook
Volunteer at a local AI or machine learning organization
Show steps
Gain practical experience, network with professionals, and contribute to the advancement of machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify local organizations involved in AI or machine learning
- Contact the organizations and inquire about volunteer opportunities
- Attend volunteer events and contribute your skills
Contribute to open-source machine learning projects on GitHub
Show steps
Gain experience in collaborative development, learn from others, and contribute to the growth of the machine learning community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source machine learning projects on GitHub
- Review the project documentation and identify areas where you can contribute
- Create a pull request with your proposed changes
- Respond to feedback and iterate on your contributions
Career center
Learners who complete Discovering Artificial Intelligence and Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of AI, machine learning, and statistics to identify data patterns, solve business problems, and forecast future trends. By taking this course, you can refine your skills with both AI and machine learning. These skills are foundational to the job.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the machine learning models that power AI systems. The concepts you will study in this course, such as AI and machine learning basics, are essential for Machine Learning Engineers.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
AI Engineers are responsible for the design and development of AI systems. This course gives you a solid introduction to the concepts of AI and machine learning. This knowledge is highly relevant to an AI Engineer's day-to-day work.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They use these insights to help businesses make better decisions. This course will give you a good foundation in the basics of AI, machine learning, and how they are used in the real world. These skills are vital for a Data Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. AI and machine learning are increasingly being used in software development. This course will help you build a foundation in these technologies, which will make you a more valuable Software Engineer.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. AI and machine learning are two of the most important areas of computer science research today. This course will help you build a foundation in these technologies, which will make you a more competitive Computer Scientist.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. AI and machine learning are increasingly being used in quantitative analysis. This course will help you build a foundation in these technologies, which will make you a more valuable Quantitative Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. AI and machine learning are increasingly being used in operations research. This course will help you build a foundation in these technologies, which will make you a more effective Operations Research Analyst.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. AI and machine learning are increasingly being used in business analysis. This course will help you build a foundation in these technologies, which will make you a more valuable Business Analyst.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. AI and machine learning are increasingly being used in product development. This course will help you build a foundation in these technologies, which will make you a more effective Product Manager.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. AI and machine learning are increasingly being used in marketing. This course will help you build a foundation in these technologies, which will make you a more effective Marketing Manager.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. AI and machine learning are increasingly being used in sales. This course will help you build a foundation in these technologies, which will make you a more effective Sales Manager.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. AI and machine learning are increasingly being used in customer success. This course will help you build a foundation in these technologies, which will make you a more effective Customer Success Manager.
Project Manager
Project Manager
Project Managers are responsible for planning and executing projects. AI and machine learning are increasingly being used in project management. This course will help you build a foundation in these technologies, which will make you a more effective Project Manager.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. AI and machine learning are increasingly being used in financial analysis. This course will may help you build a foundation in these technologies, which will make you a more effective Financial Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/9f5vz9/discovering
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
Discovering Artificial Intelligence and Machine Learning.
This textbook provides a comprehensive overview of the mathematical foundations of machine learning.
Provides a comprehensive overview of reinforcement learning, with a focus on the underlying mathematical concepts.
Save
This textbook provides a comprehensive overview of deep learning, covering both the theoretical foundations and practical applications.
Provides a comprehensive overview of deep learning using Python, with a focus on practical applications.
Save
Provides a comprehensive overview of generative adversarial networks, with a focus on practical applications.
Provides a comprehensive overview of machine learning using Python, with a focus on practical applications.
Provides a comprehensive overview of machine learning using R, with a focus on practical applications.
Save
Provides a comprehensive overview of computer vision, with a focus on practical applications.
Provides a comprehensive overview of speech and language processing, with a focus on practical applications.
Provides a practical guide to building and deploying ML models using popular Python libraries.
Provides a comprehensive overview of natural language processing using Python, with a focus on practical applications.
This textbook provides a gentle introduction to machine learning using Python.
Provides a practical introduction to machine learning for non-technical readers.
This textbook provides a gentle introduction to machine learning, with a focus on the underlying mathematical concepts.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9f5vz9/discovering
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Effort
1 hour
Via
Udacity
Language
English
Good to know
Builds a strong foundation for understanding the role of Artificial Intelligence and Machine Learning in solving problems
Provides a foundational overview of Artificial Intelligence and Machine Learning for beginners
Explores the practical applications of Artificial Intelligence and Machine Learning across various industries
Taught by industry experts, offering real-world insights and practical knowledge
Facilitates networking and collaboration with fellow learners through online discussions and forums
Provides hands-on exercises and projects to reinforce learning and encourage practical application
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