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Dr. Ryan Ahmed, Ph.D., MBA,
Ligency Team,
SuperDataScience Team,
Mitchell Bouchard,
and
Stemplicity Q&A Support
Do you want to leverage the power of Machine Learning without writing any code?
Do you want to break into Machine Learning, but you feel overwhelmed and intimidated?
Do you want to leverage Machine Learning for your business, but you don’t have data science or mathematics background?
If the answer is yes to any of these questions, you came to the right place.
This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale.
Read more
Do you want to leverage the power of Machine Learning without writing any code?
Do you want to break into Machine Learning, but you feel overwhelmed and intimidated?
Do you want to leverage Machine Learning for your business, but you don’t have data science or mathematics background?
If the answer is yes to any of these questions, you came to the right place.
This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale.
Read more
Do you want to leverage the power of Machine Learning without writing any code?
Do you want to break into Machine Learning, but you feel overwhelmed and intimidated?
Do you want to leverage Machine Learning for your business, but you don’t have data science or mathematics background?
If the answer is yes to any of these questions, you came to the right place.
This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale.
Machine Learning is one of the hottest tech fields to be in right now. The field is exploding with opportunities and career prospects.
Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Machine Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation, and technology.
In this course, we will cover top 3 brand new tools to build, train and deploy cutting edge machine learning models without writing a single line of code. We will cover the new Google Vertex AI, Microsoft Azure Machine Learning Designer, and DataRobot AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/pbfnn7/no
What's inside
Learning objectives
- Master machine learning fundamentals without any intimidating mathematics and without writing a single line of code!
- Build, train and deploy machine learning models that could predict loan default using customer features such as income and loan term using microsoft azure.
- Master google vertex ai to automate the process of building and training machine learning models at scale.
- Understand trained machine learning models by exploring feature importance.
- Learn how to create multiple experiments using datarobot ai and perform hyperparameter optimization.
- Learn how to train multiple cutting-edge machine learning models using datarobot ai and deploy the best model.
- Master machine learning fundamentals without any intimidating mathematics and without writing a single line of code!
- Build, train and deploy machine learning models that could predict loan default using customer features such as income and loan term using microsoft azure.
- Master google vertex ai to automate the process of building and training machine learning models at scale.
- Understand trained machine learning models by exploring feature importance.
- Learn how to create multiple experiments using datarobot ai and perform hyperparameter optimization.
- Learn how to train multiple cutting-edge machine learning models using datarobot ai and deploy the best model.
Syllabus
Course Introduction, Outline and Key Learning Outcomes
Introduction and Welcome Message
Best Practices, Success Tips and Certification
Artificial Intelligence (AI) and Machine Learning (ML) Super Powers
Read more
Syllabus
Course Introduction, Outline and Key Learning Outcomes
Introduction and Welcome Message
Best Practices, Success Tips and Certification
Artificial Intelligence (AI) and Machine Learning (ML) Super Powers
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops skills that are core for business, such as leveraging Machine Learning without writing code, building and training machine learning models, and understanding trained machine learning models by exploring feature importance
Taught by the SuperDataScience Team, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard, Stemplicity Q&A Support, and Ligency Team, who are recognized for their work in Machine Learning
Examines Machine Learning, which is highly relevant to the growing fields of banking, healthcare, transportation, and technology
Covers in-demand tools and technologies used in Machine Learning, such as Google Vertex AI, Microsoft Azure Machine Learning Designer, and DataRobot AI
Emphasizes hands-on learning through demos and projects using real-world datasets
Prerequisites: No coding or mathematics background required
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Reviews summary
Practical no-code ml for business
According to students, this course is highly praised for its practical, no-code approach to machine learning, making it accessible even without a strong technical or mathematical background. Learners particularly appreciate the hands-on demonstrations using modern ML tools like Google Vertex AI, Microsoft Azure ML Designer, and DataRobot AI, which enable them to build and deploy models efficiently. While offering a solid foundation for business applications, some suggest it's less suited for those seeking deep theoretical understanding or advanced coding skills.
Structured logically with a good pace for progressive learning.
"The modules flow very well, building up knowledge step-by-step."
"I liked the bite-sized lessons and quick demos, making it easy to follow along."
"The mix of lectures, readings, and quizzes kept me engaged throughout the course."
Covers highly relevant and current no-code ML platforms.
"The selection of tools like Google Vertex AI and Azure ML makes this course very current and valuable."
"I found the focus on DataRobot AI particularly useful for rapid prototyping in my job."
"This course helped me discover new tools that are transforming how businesses approach ML."
Explains core machine learning concepts clearly and concisely.
"I finally grasped key ML concepts like feature importance and hyperparameter tuning thanks to the clear explanations."
"The course did a great job explaining the 'why' behind ML without diving into the deep theoretical 'how'."
"The instructor made complex topics like regression and classification accessible."
Focuses on real-world applications using industry-leading no-code platforms.
"The demos for Vertex AI, Azure ML, and DataRobot were incredibly useful; I can apply these immediately."
"I loved the hands-on projects; they really cemented my understanding of how to use these powerful tools."
"Seeing how to deploy models without code was exactly what I needed for my business projects."
Ideal for beginners and professionals without coding or math background.
"This course is a game-changer for anyone intimidated by coding but wanting to get into ML."
"Finally, an ML course that doesn't drown you in complex math; it's truly practical and understandable."
"I appreciate how the instructor breaks down ML concepts without relying on any prior coding knowledge."
Less suitable for those seeking deep theoretical understanding of algorithms.
"If you're looking to understand the math behind ML algorithms, this course only scratches the surface."
"I wish there was a bit more depth on how the models actually work internally, not just how to use them."
"For someone with a coding background, it might feel a bit too high-level conceptually."
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 No-Code Machine Learning: Practical Guide to Modern ML Tools with these
activities:
Review Mathematics and Statistics Fundamentals
Show steps
Reviewing these fundamentals will provide a foundation for understanding the more advanced techniques used in this course.
Browse courses on
Mathematics
Show steps
-
Review the basics of algebra, calculus, and probability theory.
-
Solve practice problems to test your understanding.
-
Consult online resources or textbooks for additional support.
Form Study Groups with Peers
Show steps
Collaborating with peers can enhance your understanding, improve your problem-solving abilities, and provide support and motivation.
Show steps
-
Identify a group of peers who share your interests and goals.
-
Set regular meeting times to discuss course topics, work on assignments, and prepare for assessments.
-
Actively engage in discussions, share ideas, and support each other's learning.
Watch Google Vertex AI Quick Starts
Show steps
Familiarize yourself with Vertex AI's core functionality and capabilities.
Show steps
-
Navigate to Google Vertex AI documentation page
-
Select 'Quickstarts' from the left-hand menu
-
Choose a quickstart relevant to your project
-
Follow the step-by-step instructions
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore Machine Learning Tools and Platforms
Show steps
Hands-on exploration of tools and platforms will give you practical experience and familiarity with the technologies used in the field.
Browse courses on
Machine Learning Tools
Show steps
-
Identify popular machine learning tools and platforms.
-
Follow tutorials and documentation to learn how to use these tools effectively.
-
Apply the tools to small projects or exercises to gain practical experience.
Practice Machine Learning Algorithms
Show steps
This practice will help you solidify your understanding of machine learning fundamentals.
Browse courses on
Machine Learning Algorithms
Show steps
-
Implement a linear regression model using scikit-learn.
-
Train a decision tree classifier on a dataset of your choice.
-
Apply a support vector machine to a real-world problem.
Practice and Implement Machine Learning Algorithms
Show steps
By implementing different algorithms, you'll gain practical experience and reinforce the concepts learned in this course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a dataset and a suitable machine learning algorithm.
-
Implement the algorithm using a programming language of your choice.
-
Test your implementation on the dataset.
-
Evaluate the results and fine-tune your implementation.
Kaggle Machine Learning Competitions
Show steps
Apply your ML skills, get hands-on experience, and learn from others.
Show steps
-
Create a Kaggle account
-
Join a beginner-friendly competition
-
Explore the datasets and explore other kernels
-
Build and train your own model
-
Submit your predictions and compare your performance
Develop a Machine Learning Project
Show steps
This project will allow you to apply your machine learning skills to a real-world problem.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem that you can solve with machine learning.
-
Collect and prepare data for your project.
-
Train and evaluate a machine learning model.
-
Deploy your model and monitor its performance.
Build a Machine Learning Model for a Real-World Problem
Show steps
Applying your knowledge to a practical project will enhance your problem-solving skills and prepare you for real-world applications.
Browse courses on
Machine Learning Model
Show steps
-
Identify a suitable real-world problem that can be addressed using machine learning.
-
Collect and preprocess the necessary data for your model.
-
Develop and train your machine learning model.
-
Deploy your model and evaluate its performance.
-
Present your results and insights to demonstrate your understanding.
Review Mathematics and Statistics Fundamentals
Show steps
Reviewing these fundamentals will provide a foundation for understanding the more advanced techniques used in this course.
Browse courses on
Mathematics
Show steps
Show steps
Show steps
- Review the basics of algebra, calculus, and probability theory.
- Solve practice problems to test your understanding.
- Consult online resources or textbooks for additional support.
Form Study Groups with Peers
Show steps
Collaborating with peers can enhance your understanding, improve your problem-solving abilities, and provide support and motivation.
Show steps
Show steps
Show steps
- Identify a group of peers who share your interests and goals.
- Set regular meeting times to discuss course topics, work on assignments, and prepare for assessments.
- Actively engage in discussions, share ideas, and support each other's learning.
Watch Google Vertex AI Quick Starts
Show steps
Familiarize yourself with Vertex AI's core functionality and capabilities.
Show steps
Show steps
Show steps
- Navigate to Google Vertex AI documentation page
- Select 'Quickstarts' from the left-hand menu
- Choose a quickstart relevant to your project
- Follow the step-by-step instructions
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore Machine Learning Tools and Platforms
Show steps
Hands-on exploration of tools and platforms will give you practical experience and familiarity with the technologies used in the field.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Identify popular machine learning tools and platforms.
- Follow tutorials and documentation to learn how to use these tools effectively.
- Apply the tools to small projects or exercises to gain practical experience.
Practice Machine Learning Algorithms
Show steps
This practice will help you solidify your understanding of machine learning fundamentals.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Implement a linear regression model using scikit-learn.
- Train a decision tree classifier on a dataset of your choice.
- Apply a support vector machine to a real-world problem.
Practice and Implement Machine Learning Algorithms
Show steps
By implementing different algorithms, you'll gain practical experience and reinforce the concepts learned in this course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a dataset and a suitable machine learning algorithm.
- Implement the algorithm using a programming language of your choice.
- Test your implementation on the dataset.
- Evaluate the results and fine-tune your implementation.
Kaggle Machine Learning Competitions
Show steps
Apply your ML skills, get hands-on experience, and learn from others.
Show steps
Show steps
Show steps
- Create a Kaggle account
- Join a beginner-friendly competition
- Explore the datasets and explore other kernels
- Build and train your own model
- Submit your predictions and compare your performance
Develop a Machine Learning Project
Show steps
This project will allow you to apply your machine learning skills to a real-world problem.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem that you can solve with machine learning.
- Collect and prepare data for your project.
- Train and evaluate a machine learning model.
- Deploy your model and monitor its performance.
Build a Machine Learning Model for a Real-World Problem
Show steps
Applying your knowledge to a practical project will enhance your problem-solving skills and prepare you for real-world applications.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Identify a suitable real-world problem that can be addressed using machine learning.
- Collect and preprocess the necessary data for your model.
- Develop and train your machine learning model.
- Deploy your model and evaluate its performance.
- Present your results and insights to demonstrate your understanding.
Career center
Learners who complete No-Code Machine Learning: Practical Guide to Modern ML Tools will develop knowledge and skills
that may be useful to these careers:
Machine Learning Research Scientist
Machine Learning Research Scientist
Machine Learning Research Scientists develop new machine learning algorithms and techniques. This course may be helpful for those interested in becoming a Machine Learning Research Scientist, as it provides an introduction to machine learning concepts and hands-on experience using industry-standard tools.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. They need knowledge in mathematics, statistics, algorithms, and programming languages. This course helps build a foundation for those who want to enter this growing field and learn specific tools and techniques for building machine learning models without writing code.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. Machine learning is increasingly used in quantitative finance for tasks such as portfolio optimization, risk management, and algorithmic trading. This course can help Quantitative Analysts learn the basics of machine learning and how to use it to improve their quantitative analysis models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. Machine learning is increasingly used in operations research for tasks such as supply chain optimization, inventory management, and workforce scheduling. This course can help Operations Research Analysts learn the basics of machine learning and how to use it to improve their operations research models.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge from data. They use machine learning to uncover hidden insights and make accurate predictions. This course may be helpful for those interested in becoming a Data Scientist, as it provides an introduction to machine learning concepts and hands-on experience using industry-standard tools.
Data Engineer
Data Engineer
Data Engineers design and manage data pipelines and infrastructure. Machine learning is increasingly used in data engineering for tasks such as data cleaning, data transformation, and feature engineering. This course can help Data Engineers learn the basics of machine learning and how to use it to improve their data engineering pipelines.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks. Machine learning is increasingly used in risk management for tasks such as fraud detection, credit scoring, and insurance pricing. This course can help Risk Analysts learn the basics of machine learning and how to use it to improve their risk management practices.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems and infrastructure. Machine learning is increasingly used in cloud computing for tasks such as cloud resource optimization, anomaly detection, and fraud prevention. This course can help Cloud Architects learn the basics of machine learning and how to use it to improve their cloud architecture designs.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to evaluate and make recommendations on investments. Machine learning is increasingly used in the financial industry for tasks such as fraud detection, risk assessment, and portfolio management. This course provides an introduction to machine learning concepts and how they can be applied in the financial industry, which may be helpful for Financial Analysts who want to expand their skillset.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand consumer behavior and develop marketing campaigns. Machine learning is increasingly used in marketing for tasks such as customer segmentation, lead scoring, and campaign optimization. This course can help Marketing Analysts learn the basics of machine learning and how to use it to improve their marketing strategies.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. While programming skills are essential for this role, a growing number of Artificial Intelligence Engineers specialize in machine learning and leverage tools that do not require coding. This course may be useful to those Artificial Intelligence Engineers who want to expand their skillset and incorporate machine learning into their work.
Data Architect
Data Architect
Data Architects design and manage data systems and infrastructure. Machine learning is increasingly used in data architecture for tasks such as data integration, data warehousing, and data governance. This course can help Data Architects learn the basics of machine learning and how to use it to improve their data architecture designs.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products throughout their lifecycle. In recent years, machine learning has become increasingly important for product development and management. This course can help Product Managers understand the basics of machine learning and how to incorporate it into their product strategies.
Business Analyst
Business Analyst
Business Analysts work closely with stakeholders to understand their needs and develop solutions to address them. Many businesses today use machine learning to improve operations and efficiency. This course can be helpful for Business Analysts who want to learn more about machine learning and how it can be applied in a business setting.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While programming skills are essential for this role, a growing number of Software Engineers specialize in machine learning and leverage tools that do not require coding. This course may be useful to those Software Engineers who want to expand their skillset and incorporate machine learning into their work.
For more career information including salaries, visit:
OpenCourser.com/course/pbfnn7/no
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
No-Code Machine Learning: Practical Guide to Modern ML Tools.
This comprehensive book covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It provides practical guidance on building and training deep learning models using Python.
Save
Provides a hands-on guide to building and deploying deep learning models. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to machine learning using the R programming language. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.
This practical book offers hands-on experience with popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.
Provides a comprehensive overview of machine learning applications in natural language processing, including text classification, sentiment analysis, and machine translation.
Provides a comprehensive overview of machine learning applications in healthcare, including disease diagnosis, patient monitoring, and drug discovery.
Provides a comprehensive overview of machine learning applications in finance, including stock prediction, risk management, and fraud detection.
This beginner-friendly guide provides a comprehensive overview of machine learning concepts, techniques, and applications. Its clear explanations and real-world examples make it a valuable resource for understanding the fundamentals of machine learning.
Provides a comprehensive introduction to reinforcement learning, a type of machine learning that involves training an agent to make optimal decisions in a given environment.
This advanced textbook provides a probabilistic foundation for machine learning. It covers topics such as Bayesian inference, graphical models, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pbfnn7/no
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Rating
4.5
Effort
6.5 total hours
Via
Udemy
Instructors
Dr. Ryan Ahmed, Ph.D., MBA
Ligency Team
SuperDataScience Team
Mitchell Bouchard
Stemplicity Q&A Support
Language
English
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