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Kartik Hosanagar
and
Prasanna Tambe
In this course, you will go in-depth to discover how Machine Learning is used to handle and interpret Big Data. You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow. You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms. You will then explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs. You will also see exclusive interviews with industry leaders, who manage Big Data for companies such as McDonald's and Visa. By the end of this course, you will have learned different ways to code, including how to use no-code tools, understand Deep Learning, how to measure and review errors in your algorithms, and how to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive your business.
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What's inside
Syllabus
Module 1 – Big Data and Artificial Intelligence
In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.
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Syllabus
Module 1 – Big Data and Artificial Intelligence
In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces learners to best practices for optimizing Machine Learning algorithms, including Deep Learning models
An instructor has extensive experience in data science and technology at McDonald's
Provides you with practical knowledge of machine learning tools and methods
Teaches you how to interpret exclusive interviews with industry leaders about big data
"Incorporate into your business with such tools as Teachable Machine and TensorFlow."
Covers a variety of machine learning methods
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Reviews summary
Ai fundamentals for business leaders
According to learners, this course is an excellent introduction to AI for non-data scientists, particularly for business leaders seeking to understand its impact and application. Students praise its ability to demystify complex concepts, making them accessible and relevant to real-world business scenarios. The inclusion of industry interviews provides invaluable practical insights, and the focus on no-code tools like Teachable Machine is highly appreciated. While the course provides a broad foundational overview, a minority of reviewers found some technical explanations dense or the pace too fast for absolute beginners with no prior analytical background.
Offers a wide scope, providing a comprehensive introduction.
"It covers a broad range of topics from Big Data to Deep Learning and AutoML."
"I learned a lot about how AI and ML are applied in various business segments."
"It does require focus as it covers a lot of ground quickly, providing a wide overview."
Utilizes accessible tools like Teachable Machine.
"The use of tools like Teachable Machine made it very accessible."
"The no-code approach is fantastic for understanding the concepts without the technical overhead."
"I appreciated learning about Teachable Machine as a practical no-code solution for building algorithms."
Provides practical application through expert interviews.
"The industry interviews, especially the one with McDonald's VP, offered incredible real-world insights."
"I gained valuable insight into data sampling and building realistic usable models from the expert."
"The industry insights were invaluable, making complex topics understandable with real-world context."
Expertly simplifies complex AI for non-technical roles.
"This course is exactly what I needed as a business leader looking to understand AI without getting bogged down in coding."
"Absolutely brilliant! As someone with zero background in AI, this course demystified so many concepts."
"It perfectly bridges the gap between high-level strategic understanding and basic technical concepts."
Some technical concepts can be dense or fast-paced.
"I felt some technical explanations, particularly in Module 2, were still a bit dense for a 'non-data scientist'."
"I came in with absolutely no prior knowledge, and I still found parts of it challenging, especially ML methods."
"I had to do a lot of external research to fully grasp some concepts, particularly neural networks."
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 AI Fundamentals for Non-Data Scientists with these
activities:
Organize Your Notes and Resources
Show steps
Review and organize your lecture notes, assignments, and other resources to facilitate efficient learning
Show steps
-
Gather all course-related materials
-
Create a system to organize your notes and resources
-
Regularly review and update your organized materials
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Review the fundamental concepts and techniques in ML by reading ''Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow''
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the introductory chapters to gain an overview of ML
-
Focus on the chapters relevant to the course topics
Review Jupyter Notebook Basics
Show steps
Review coding basics in Jupyter Notebook using Python to prepare for the coursework
Browse courses on
Jupyter Notebook
Show steps
-
Open Jupyter Notebook and create a new Python notebook
-
Print 'Hello, world!' in your first cell
-
Explore the Jupyter Notebook interface
Five other activities
Expand to see all activities and additional details
Show all eight activities
Machine Learning Discussion Group
Show steps
Participate in online discussion forums to exchange ideas and clarify concepts with peers
Browse courses on
Machine Learning
Show steps
-
Join or start a discussion thread on relevant ML topics
-
Actively participate in discussions and share your insights
-
Seek clarification and provide support to others
No-Code Machine Learning Project
Show steps
Develop a practical ML project using Teachable Machine to understand ML applications
Browse courses on
No-Code Machine Learning
Show steps
-
Identify a suitable ML problem to solve
-
Gather and prepare your dataset
-
Use Teachable Machine to train your ML model
-
Deploy your model and evaluate its performance
Deep Learning with TensorFlow Tutorial
Show steps
Gain hands-on experience in Deep Learning through a structured TensorFlow tutorial series
Browse courses on
Deep Learning
Show steps
-
Set up your development environment
-
Build and train your first neural network
-
Explore advanced Deep Learning techniques
Personalize Your ML Algorithm
Show steps
Undertake a project to customize and optimize an ML algorithm for a specific dataset and problem
Browse courses on
Machine Learning
Show steps
-
Select a dataset and define your problem statement
-
Choose and train an ML model using the dataset
-
Evaluate and fine-tune the model's performance
-
Deploy and monitor your personalized ML algorithm
Contribute to a Machine Learning Open-Source Project
Show steps
Make meaningful contributions to an open-source ML project to gain practical experience and build your portfolio
Browse courses on
Machine Learning
Show steps
-
Identify a suitable open-source ML project to contribute to
-
Select a task or issue to work on
-
Develop and test your proposed solution
-
Submit a pull request with your changes
Organize Your Notes and Resources
Show steps
Review and organize your lecture notes, assignments, and other resources to facilitate efficient learning
Show steps
Show steps
Show steps
- Gather all course-related materials
- Create a system to organize your notes and resources
- Regularly review and update your organized materials
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Review the fundamental concepts and techniques in ML by reading ''Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow''
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the introductory chapters to gain an overview of ML
- Focus on the chapters relevant to the course topics
Review Jupyter Notebook Basics
Show steps
Review coding basics in Jupyter Notebook using Python to prepare for the coursework
Browse courses on
Jupyter Notebook
Show steps
Show steps
Show steps
- Open Jupyter Notebook and create a new Python notebook
- Print 'Hello, world!' in your first cell
- Explore the Jupyter Notebook interface
Five other activities
Expand to see all activities and additional details
Show all eight activities
Machine Learning Discussion Group
Show steps
Participate in online discussion forums to exchange ideas and clarify concepts with peers
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join or start a discussion thread on relevant ML topics
- Actively participate in discussions and share your insights
- Seek clarification and provide support to others
No-Code Machine Learning Project
Show steps
Develop a practical ML project using Teachable Machine to understand ML applications
Browse courses on
No-Code Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable ML problem to solve
- Gather and prepare your dataset
- Use Teachable Machine to train your ML model
- Deploy your model and evaluate its performance
Deep Learning with TensorFlow Tutorial
Show steps
Gain hands-on experience in Deep Learning through a structured TensorFlow tutorial series
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Set up your development environment
- Build and train your first neural network
- Explore advanced Deep Learning techniques
Personalize Your ML Algorithm
Show steps
Undertake a project to customize and optimize an ML algorithm for a specific dataset and problem
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Select a dataset and define your problem statement
- Choose and train an ML model using the dataset
- Evaluate and fine-tune the model's performance
- Deploy and monitor your personalized ML algorithm
Contribute to a Machine Learning Open-Source Project
Show steps
Make meaningful contributions to an open-source ML project to gain practical experience and build your portfolio
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable open-source ML project to contribute to
- Select a task or issue to work on
- Develop and test your proposed solution
- Submit a pull request with your changes
Career center
Learners who complete AI Fundamentals for Non-Data Scientists will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematics and statistics to understand the behavior of financial markets, build financial models, and create trading strategies. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data to identify and solve problems. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Market Research Analyst
Market Research Analyst
Market Research Analysts use their knowledge of data and statistics to understand consumer behavior. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and statistics to solve complex problems in business and industry. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and economics to make investment decisions. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Project Manager
Project Manager
Project Managers are responsible for the planning and execution of projects. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Business Development Manager
Business Development Manager
Business Development Managers are responsible for identifying and developing new business opportunities. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Sales Manager
Sales Manager
Sales Managers are responsible for the sales and marketing of products and services. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and implementation of marketing campaigns. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for the satisfaction and retention of customers. This course will help you develop the skills you need to succeed in this role, including data analysis, machine learning, and coding. You will also learn how to use big data to make better decisions and drive business value.
For more career information including salaries, visit:
OpenCourser.com/course/hpsxm5/ai
Reading list
We've selected 11 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
AI Fundamentals for Non-Data Scientists.
Save
Comprehensive reference for deep learning, covering the theoretical foundations, algorithms, and applications of deep neural networks. It provides a detailed exploration of deep learning architectures, training techniques, and best practices.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, decision trees, and support vector machines. It offers a strong foundation in statistical concepts and methods used in machine learning.
Provides a comprehensive overview of deep learning in Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It offers a hands-on approach to deep learning, with a focus on real-world applications in various domains.
Provides a comprehensive overview of machine learning in R, covering topics such as data preprocessing, model selection, and performance evaluation. It offers a hands-on approach to machine learning, with a focus on real-world applications in various domains.
Provides a comprehensive overview of natural language processing in Python, covering topics such as text preprocessing, natural language understanding, and natural language generation. It offers a hands-on approach to natural language processing, with a focus on real-world applications in various domains.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, feature selection, and model evaluation. It offers a strong foundation in the fundamental concepts and algorithms used in machine learning.
Provides a comprehensive overview of machine learning concepts and algorithms, with a focus on practical implementation using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers supervised and unsupervised learning, feature engineering, model evaluation, and deployment.
Provides a probabilistic approach to machine learning, covering topics such as Bayesian inference, Gaussian processes, and Markov chain Monte Carlo methods. It offers a unique perspective on machine learning, focusing on the underlying probabilistic foundations.
Provides a practical introduction to data science for business professionals, covering topics such as data collection, data analysis, and data visualization. It offers a hands-on approach to data science, with a focus on real-world applications in business.
Save
Provides a comprehensive overview of big data, covering topics such as data collection, data analysis, and data visualization. It offers a broad perspective on the impact of big data on various aspects of society, including business, government, and science.
Provides a gentle introduction to machine learning for beginners, covering topics such as data preprocessing, model selection, and performance evaluation. It offers a non-technical approach to machine learning, with a focus on making the concepts accessible to a wider audience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hpsxm5/ai
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Institution
University of Pennsylvania
Instructors
Kartik Hosanagar
Prasanna Tambe
Language
English
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This course belongs to a
collection
called
AI For Business. It's comprised of four courses:
- AI Strategy and Governance
- AI Fundamentals for Non-Data Scientists (viewing)
- AI Applications in Marketing and Finance
- AI Applications in People Management
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