No-Code Machine Learning: Practical Guide to Modern ML Tools from Udemy

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.

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

Machine Learning: The Big Picture

Machine Learning Key Elements (Recipe!)

Course Outline and Key Learning Outcomes

University Admission Prediction Using Google Vertex AI

The Rise of Machine Learning in Higher Education

Reading Material: The Rise of Machine Learning in Higher Education

Quiz: The Rise of Machine Learning in Higher Education

Machine Learning Regression 101

Google Vertex AI Demo Part 1: Setup and Upload

Google Vertex AI Demo Part 2: Model Training

Machine Learning Regression Models Metrics

Google Vertex AI Demo Part 3: Test and Deploy Trained Model

Recap and Concluding Remarks

Bank Loan Risk Prediction Using Data Robot AI

Introduction to Machine Learning in Finance/Banking

Reading Material: Machine Learning and AI in the Finance/Banking Industry

Quiz: Machine Learning and AI in Finance/Banking

Project Overview: Bank Loan Risk Prediction

Data Robot Demo Part #1: Sign Up and Dataset Upload

Data Robot Demo Part #2: Target Selection & Exploratory Data Analysis

Data Robot Demo Part #3: Model Training & Feature Importance

Data Robot Demo Part #4: Confusion Matrix, Precision, Recall, ROC, AUC

Data Robot Demo Part #5: Model Assessment

Data Robot Demo Part #6: Hyper Parameters Tuning

Data Robot Demo Part #7: Model Deployment and Making Inference

Project Recap and Concluding Remarks

Price Forecasting Using Microsoft Azure Machine Learning Designer

Introduction to Machine Learning for Price Prediction

Reading Material: Machine Learning for Price Forecasting

Quiz: Machine Learning for Price Prediction

Project Overview

Microsoft Azure ML Demo Part #1: ML Signup and Studio Launch

Microsoft Azure ML Demo Part #2: Dataset Creation

Microsoft Azure ML Demo Part #3: Build ML Pipeline with ML Azure Designer

Microsoft Azure ML Demo Part #4: Execute and Test the Pipeline

Microsoft Azure ML Demo Part #5: Deploy Model and Make Inference

Mini Project: Build and Test Multiple Models

Mini Project Solution: Build and Test Multiple Models

Conclusion

Thanks for Watching!

Links to Other Courses

Appendix

Introduction to XG-Boost [Optional Lecture/Additional Material]

What is boosting

Decision trees and ensemble learning

Gradient Boosting Deep Dive #1 [Optional Lecture/Additional Material]

Gradient Boosting Deep Dive #2 [Optional Lecture/Additional Material]

AWS SageMaker XGBoost

Congratulations!! Don't forget your Prize :)

Bonus: How To UNLOCK Top Salaries (Live Training)

Good to know

Know what's good

, what to watch for

, 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

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.

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 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.

See salaries and explore the career path for Machine Learning Research Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Cloud Architect

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.

See salaries and explore the career path for Marketing Analyst

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.

See salaries and explore the career path for Artificial Intelligence Engineer

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.

See salaries and explore the career path for Data Architect

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Software Engineer