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Mohammed Osman
Machine learning is perceived as a difficult, challenging, and math-intensive topic. In this course, Building Your First Machine Learning solution, you will discover the magic of machine learning and understand the theory behind it. First, you will learn what machine learning is, its types, its applications, why it is getting traction, and what its phases are. Next, you will discover how vital the data is for machine learning solutions, how to source it, analyze it, and pre-process it for subsequent machine learning steps. Finally, you will explore how to train your machine learning algorithms and evaluate them. Moreover, you will...
Read more
Machine learning is perceived as a difficult, challenging, and math-intensive topic. In this course, Building Your First Machine Learning solution, you will discover the magic of machine learning and understand the theory behind it. First, you will learn what machine learning is, its types, its applications, why it is getting traction, and what its phases are. Next, you will discover how vital the data is for machine learning solutions, how to source it, analyze it, and pre-process it for subsequent machine learning steps. Finally, you will explore how to train your machine learning algorithms and evaluate them. Moreover, you will...
Read more
Machine learning is perceived as a difficult, challenging, and math-intensive topic. In this course, Building Your First Machine Learning solution, you will discover the magic of machine learning and understand the theory behind it. First, you will learn what machine learning is, its types, its applications, why it is getting traction, and what its phases are. Next, you will discover how vital the data is for machine learning solutions, how to source it, analyze it, and pre-process it for subsequent machine learning steps. Finally, you will explore how to train your machine learning algorithms and evaluate them. Moreover, you will develop knowledge around recent trends in machine learning, such as AI as a Service. When you are finished with this course, you will have a firm understanding of machine learning with the ability to build a basic regression machine learning solution.
Register for this course and see more details by visiting:
OpenCourser.com/course/3g7aw7/building
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces essential concepts and phases of machine learning
Provides a strong foundation for beginners to build a basic regression machine learning solution
Suitable for learners with diverse backgrounds and interests in machine learning
Teaches practical skills and knowledge that are relevant to industry applications
Exposes learners to recent trends in machine learning, such as AI as a Service
Assumes no prior knowledge in machine learning, making it accessible to a broad audience
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Activities
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Building Your First Machine Learning Solution.
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Career center
Learners who complete Building Your First Machine Learning Solution will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the machine learning models that power AI systems. They design and implement these models, as well as monitor their performance. They typically need a Master's or PhD in Computer Science or related field, and this course may be useful in building a foundation for this role.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. Some Statisticians have a background in machine learning, and this course may be helpful for Statisticians who wish to gain a better understanding of machine learning or who are working on machine learning projects.
Researcher
Researcher
Researchers conduct research in a variety of fields, including machine learning. This course may be helpful for Researchers who wish to gain a better understanding of machine learning or who are working on machine learning projects.
Educator
Educator
Educators teach students about a variety of subjects, including machine learning. This course may be helpful for Educators who wish to teach machine learning or who are working on developing machine learning curriculum.
AI Engineer
AI Engineer
AI Engineers design, develop, and maintain AI systems. They work on teams that develop new products or services, or within companies to help improve efficiency and revenue. While some roles may require a Master's or PhD, this course may be helpful in building a foundation for this role.
Data Analyst
Data Analyst
Data Analysts analyze data to help businesses make better decisions. They use techniques from machine learning, among others, to find insights from data. While some roles may require a Master's degree, this course may be helpful in building a foundation for this role.
Business Analyst
Business Analyst
Business Analysts help businesses understand their customers and make better decisions. They use techniques from machine learning, among others, to find insights from data. While some roles may require a Master's degree, this course may be helpful in building a foundation for this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may work on teams that develop new products or services, or within companies to help improve efficiency and revenue. This course may be helpful for Software Engineers who wish to work on machine learning projects.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products or services. Some Product Managers have a background in machine learning, and this course may be helpful for Product Managers who wish to gain a better understanding of machine learning.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to help businesses make better decisions. Some Financial Analysts have a background in machine learning, and this course may be helpful for Financial Analysts who wish to gain a better understanding of machine learning.
Data Scientist
Data Scientist
A Data Scientist uses machine learning, among other methods, to help find insights from data. They may work on teams that develop new products or services, or within companies to help improve efficiency and revenue. While some roles may require a Master's or PhD, this course may be helpful in building a foundation for this role.
Consultant
Consultant
Consultants help businesses solve problems and improve their performance. Some Consultants have a background in machine learning, and this course may be helpful for Consultants who wish to gain a better understanding of machine learning.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other products. Some Technical Writers have a background in machine learning, and this course may be helpful for Technical Writers who wish to write documentation for machine learning products or services.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. Some Entrepreneurs have a background in machine learning, and this course may be helpful for Entrepreneurs who wish to develop machine learning products or services.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and purchase software and other products. Some Sales Engineers have a background in machine learning, and this course may be helpful for Sales Engineers who wish to sell machine learning products or services.
For more career information including salaries, visit:
OpenCourser.com/course/3g7aw7/building
Reading list
We haven't picked any books for this reading list yet.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
Provides a comprehensive and practical guide to deep learning, including hands-on exercises and real-world examples.
Classic text on machine learning and statistical pattern recognition, with a focus on Bayesian approaches. The author has won the prestigious Turing Award.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
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Provides a comprehensive treatment of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian inference to deep learning.
Practical guide to machine learning for programmers, with a focus on using Python to build and deploy machine learning models.
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Practical guide to machine learning for those with no prior experience, covering a wide range of topics from data preprocessing to model evaluation.
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Comprehensive and authoritative reference on deep learning, covering a wide range of topics from neural networks to reinforcement learning.
Practical guide to machine learning for those with no prior experience, covering a wide range of topics from data preprocessing to model evaluation. It great hands-on tutorial to pick up skills in machine learning.
While not focused specifically on Machine learning, this book covers a broad range of topics in Artificial Intelligence including machine learning, and good companion to delve deeper into the theoretical and technical aspects of the field.
Provides a broad overview of supervised learning, covering the fundamental concepts, algorithms, and applications.
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Provides a comprehensive overview of supervised learning, deep learning, and related topics, such as neural networks and reinforcement learning.
Provides a practical introduction to supervised learning, using popular Python libraries such as scikit-learn, Keras, and TensorFlow.
Provides a theoretical foundation for supervised learning, covering topics such as linear regression, logistic regression, and support vector machines.
Provides a comprehensive overview of supervised learning, covering both theoretical and practical aspects.
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Provides a probabilistic perspective on supervised learning, covering topics such as Bayesian inference and graphical models.
Provides a comprehensive overview of supervised learning, covering topics such as neural networks, support vector machines, and Gaussian processes.
Provides a theoretical foundation for supervised learning, with a focus on large-margin classifiers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3g7aw7/building
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Time
2 hours
Via
Pluralsight
Instructor
Mohammed Osman
This course belongs to a
collection
called
Machine Learning Literacy. It's comprised of five courses:
- Preparing Data for Machine Learning
- Designing a Machine Learning Model
- Creating Machine Learning Models
- Deploying Machine Learning Solutions
- Building Your First Machine Learning Solution (viewing)
Good to know
Introduces essential concepts and phases of machine learning
Provides a strong foundation for beginners to build a basic regression machine learning solution
Suitable for learners with diverse backgrounds and interests in machine learning
Teaches practical skills and knowledge that are relevant to industry applications
Exposes learners to recent trends in machine learning, such as AI as a Service
Assumes no prior knowledge in machine learning, making it accessible to a broad audience
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