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Mohammed Osman
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Machine Learning
Supervised Learning
Regression Analysis
Data Preprocessing
AI as a Service
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more career information including salaries, visit:
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Reading list
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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.
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.
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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.
Offers a concise yet comprehensive introduction to machine learning, covering essential concepts and algorithms in just over 100 pages. It balances theory and practice, making it suitable for data professionals looking to expand their knowledge or prepare for interviews. It includes illustrations, models, and algorithms with Python examples. This book is excellent for gaining a broad understanding and serves as a valuable quick reference.
A highly practical book that guides readers through building intelligent systems using popular Python libraries. It starts with fundamental techniques like linear regression and progresses to deep neural networks. is ideal for those who prefer a hands-on approach with code examples and exercises. It is widely used as a textbook and reference for practitioners.
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Considered a foundational text in the field of deep learning, this book provides a comprehensive theoretical and conceptual understanding of neural networks and deep learning techniques. It covers essential mathematical prerequisites like linear algebra and probability. While theoretically oriented, it crucial resource for those wanting to delve deeply into the mechanics of deep learning and is often used in graduate-level courses.
Provides an accessible introduction to statistical learning methods, which form the basis of many machine learning algorithms. It focuses on concepts and applications rather than rigorous mathematical proofs, making it suitable for a broad audience with a statistics background. It is often used as a textbook for undergraduate and graduate courses and offers practical examples in R or Python.
A more advanced and theoretical counterpart to 'An Introduction to Statistical Learning,' this book provides a deep dive into the statistical underpinnings of machine learning. It valuable reference for researchers and practitioners seeking a thorough understanding of the algorithms. While mathematically rigorous, it is considered a classic in the field and is often used in graduate-level programs.
This comprehensive book covers both the theoretical and practical aspects of machine learning from a probabilistic perspective. It explores various algorithms and concepts rigorously, including Bayesian methods and neural networks. It well-regarded textbook for advanced undergraduate and graduate students and serves as a strong reference for researchers.
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Focuses on the practical aspects of building effective machine learning systems, offering guidance on making strategic decisions in ML projects. It is particularly valuable for those transitioning into or working as ML engineers or data scientists. It provides practical advice and best practices based on real-world experience.
Provides the essential mathematical background required for understanding machine learning algorithms, covering linear algebra, calculus, probability, and statistics. It is an excellent resource for students and professionals who need to solidify their mathematical foundations to better grasp the inner workings of ML models. It can be used as a prerequisite text or a companion resource.
Considered the standard textbook for reinforcement learning, this book covers foundational principles and real-world applications of RL. It is essential reading for anyone interested in this specific area of machine learning, which is crucial for developing agents that learn through interaction. It includes examples and connections to neuroscience.
Offers a practical, hands-on introduction to machine learning using the scikit-learn library in Python. It focuses on the practical aspects of applying ML algorithms and is suitable for data scientists and developers. It helps readers understand the core concepts and how to implement them effectively.
As the title suggests, this book provides a very basic and accessible introduction to machine learning for individuals with no prior background in coding, math, or statistics. It uses plain language and visuals to explain fundamental concepts and algorithms. This is an excellent starting point for complete newcomers to the field.
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.
For more information about how these books relate to this course, visit:
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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)
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