Core Concepts in AI from Coursera

The course "Core Concepts in AI" provides a comprehensive foundation in artificial intelligence (AI) and machine learning (ML), equipping learners with the essential tools to understand, evaluate, and implement AI systems effectively. From decoding key terminology and frameworks like R.O.A.D. (Requirements, Operationalize Data, Analytic Method, Deployment) to exploring algorithm tradeoffs and data quality, this course offers practical insights that bridge technical concepts with strategic decision-making.

What sets this course apart is its focus on balancing technical depth with accessibility, making it ideal for leaders, managers, and professionals tasked with driving AI initiatives. Learners will delve into performance metrics, inter-annotator agreement, and tradeoffs in resources, gaining a nuanced understanding of AI's strengths and limitations.

Whether you're a newcomer or looking to deepen your understanding, this course empowers you to make informed AI decisions, optimize systems, and address challenges in data quality and algorithm selection. By the end, you'll have the confidence to navigate AI projects and align them with organizational goals, positioning yourself as a strategic leader in AI-driven innovation.

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Syllabus

Course Introduction

This course provides a comprehensive introduction to key concepts in artificial intelligence (AI) and machine learning (ML). Learners will explore essential vocabulary, the R.O.A.D. Framework, performance evaluation, and algorithm tradeoffs. Topics include data quality, inter-annotator agreement, and the strengths and weaknesses of AI methods. By the end, learners will be equipped with the foundational knowledge to navigate and assess AI and ML systems effectively.

Introduction to Artificial Intelligence

This module provides an introduction to artificial intelligence (AI). It does not require any prior knowledge of AI and is suitable for briefing managerial, and non-technical leaders to improve knowledge, expectations, and communication for AI projects.

Machine Learning

This module covers the statistical foundations of machine learning and the common metrics for evaluating machine learning and artificial intelligence performance.

Algorithm Tradeoffs

This module introduces the most common algorithms used in AI and machine learning, including support vector machines, Naïve Bayes, decision trees, random forest, and neural networks. We will discuss the strengths and weaknesses of these algorithms for different classes of problems.

Data

This module explores data types (nominal, ordinal, categorical) and the challenges of data labeling, including human cognitive limits and reference issues. A key focus is inter-annotator agreement—a method to measure labeling consistency, highlighting biases and inefficiencies in human and machine processes. Consistent labeling, often more impactful than advanced algorithms, is crucial for responsible AI.

Resources

This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.

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Balances technical depth with accessibility, making it suitable for leaders and managers tasked with driving AI initiatives

Equips learners with the essential tools to understand, evaluate, and implement AI systems effectively, which is crucial for strategic decision-making

Explores algorithm tradeoffs and data quality, offering practical insights that bridge technical concepts with strategic decision-making

Delves into performance metrics and inter-annotator agreement, providing a nuanced understanding of AI's strengths and limitations

Introduces the R.O.A.D. framework, which is a helpful tool for understanding the lifecycle of AI projects from requirements to deployment

Discusses data types and the challenges of data labeling, which is crucial for responsible AI and consistent labeling practices

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 Core Concepts in AI with these activities:

Review Basic Statistics

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Reinforce your understanding of statistical concepts, which are foundational for machine learning algorithms and performance evaluation.

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Review descriptive statistics concepts such as mean, median, and standard deviation.
Practice calculating basic statistical measures using sample datasets.
Familiarize yourself with different types of data distributions.

Create a Glossary of AI Terms

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Solidify your understanding of AI terminology by creating a comprehensive glossary of terms covered in the course.

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Compile a list of key AI terms from the course materials.
Write clear and concise definitions for each term.
Include examples and illustrations to aid understanding.
Share your glossary with other students for feedback.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain practical experience with machine learning algorithms and frameworks, complementing the theoretical knowledge from the course.

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Read the chapters relevant to the algorithms covered in the course.
Work through the code examples and exercises in the book.
Experiment with different parameters and datasets to understand the behavior of the models.

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Practice Evaluating Model Performance

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Sharpen your skills in evaluating AI model performance using various metrics discussed in the course.

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Find datasets online and build simple models using Scikit-learn.
Calculate precision, recall, F1-score, and accuracy for each model.
Compare the performance of different models on the same dataset.

Read 'Data Science from Scratch'

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Deepen your understanding of the underlying principles of data science and machine learning.

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Read the chapters on the statistical foundations of machine learning.
Implement the algorithms from scratch using Python.
Compare your implementations with those in Scikit-learn.

Implement a Simple Machine Learning Pipeline

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Apply your knowledge by building a complete machine learning pipeline from data preprocessing to model deployment.

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Choose a dataset from a public repository like Kaggle.
Preprocess the data by cleaning, transforming, and scaling features.
Select an appropriate machine learning algorithm for the task.
Train and evaluate the model using appropriate metrics.
Deploy the model using a framework like Flask or Streamlit.

Create a Presentation on Algorithm Tradeoffs

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Demonstrate your understanding of algorithm tradeoffs by creating a presentation that compares and contrasts different AI algorithms.

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Choose a specific problem domain (e.g., image classification, natural language processing).
Research different AI algorithms that are suitable for the problem.
Compare the algorithms based on their strengths, weaknesses, and resource requirements.
Create a presentation that summarizes your findings.
Present your findings to other students or colleagues.

Career center

Learners who complete Core Concepts in AI will develop knowledge and skills that may be useful to these careers:

AI Project Manager

An AI Project Manager oversees the planning, execution, and delivery of artificial intelligence projects. This role benefits significantly from the 'Core Concepts in AI' course, which emphasizes the R.O.A.D. framework. This framework helps a Project Manager structure the project, and the course further provides understanding of algorithm tradeoffs, data quality and performance metrics. By understanding these core concepts, a Project Manager is equipped to lead their team effectively, make informed decisions about resource use, and manage risks associated with AI projects. The focus on practical insights and strategic decision making from the course is highly beneficial.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models. This role involves a deep understanding of the statistical foundations of machine learning, algorithm tradeoffs, and performance metrics, all which are core components of the 'Core Concepts in AI' course. This course introduces various common algorithms, explores their strengths and weaknesses, and addresses vital data related considerations. These include data types, human cognitive limits, and measuring inter-annotator agreement. This knowledge allows a Machine Learning Engineer to create more robust, efficient, and reliable AI systems. A solid understanding of these areas is essential for success in this career.

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Data Scientist

A Data Scientist analyzes complex data sets to extract insights and develop data driven solutions. The 'Core Concepts in AI' course provides a strong foundation for a Data Scientist particularly in understanding algorithm performance, data quality, and statistical foundations of machine learning. Data Scientists need to understand the implications of data quality and its effect on the modeling process. The course delves into data labeling challenges, human cognitive limits, and inter-annotator agreement, which are of high importance for a Data Scientist. This background helps them make more informed decisions, build effective models, and communicate results clearly to stakeholders.

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AI Consultant

An AI Consultant advises organizations on how to integrate AI technologies into their operations and strategies. This requires a broad understanding of AI concepts, including algorithm tradeoffs, performance metrics, and data-related challenges, all of which are discussed in the 'Core Concepts in AI' course. The course's focus on balancing technical depth with accessibility is particularly useful for translating complex concepts to stakeholders. An AI Consultant will be able to leverage the R.O.A.D. framework to assess and make recommendations on AI projects. The course's emphasis on informed decision making enables an AI Consultant to provide strategic guidance to clients.

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Technology Manager

A Technology Manager oversees technology implementation across an organization and requires a solid understanding of AI concepts. The 'Core Concepts in AI' course provides the foundational knowledge necessary to manage AI initiatives effectively. The course introduces crucial concepts like the R.O.A.D. framework, algorithm tradeoffs, and performance metrics, all vital to understanding and overseeing AI projects. By taking this course, a Technology Manager gains the confidence to make strategic decisions about AI investments, manage risks, and ensure alignment with organizational goals. This course will be useful to anyone in the management of technology

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Business Analyst

A Business Analyst identifies business needs and recommends solutions that often involve technology, including AI. The 'Core Concepts in AI' course offers a comprehensive look at AI concepts. The course's exploration of performance metrics, data quality, and the R.O.A.D framework provides a Business Analyst with the necessary context to analyze the viability of AI solutions. The strategic decision making focus of the course helps a Business Analyst make well informed recommendations that align with business goals. The course’s approach to making AI concepts accessible is useful for those in the field of business.

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Innovation Manager

An Innovation Manager is responsible for fostering innovation within an organization. They need to understand emerging technologies like AI. The 'Core Concepts in AI' course provides the broad understanding needed for an Innovation Manager to identify opportunities that AI presents. The course covers the R.O.A.D framework, algorithm tradeoffs, and performance metrics, all of which are essential for evaluating AI proposals. The focus of the course on strategic decision making helps an Innovation Manager make informed choices about AI investments that align with the organization's goals. This course helps them communicate complex concepts to stakeholders.

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Product Manager

A Product Manager defines the strategy, roadmap, and feature set of a product. In the case of AI product management, a foundational understanding of machine learning algorithms, data quality, and performance evaluation is necessary. The 'Core Concepts in AI' course provides this essential background introducing the R.O.A.D. framework, algorithm tradeoffs, and data related challenges. The knowledge gained from this course enables a Product Manager to advocate for the needs of their product and make well informed strategic decisions. The course's focus on bridging technical concepts with strategic decision making is useful for any Product Manager working with AI.

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Research Scientist

A Research Scientist conducts research to advance knowledge in a particular field often involving data analysis, modeling, and experimentation. This role typically requires an advanced degree. Research Scientists often work at the forefront of technological innovation, and should have specialized knowledge of AI concepts. The 'Core Concepts in AI' course may be useful by providing background on AI and machine learning. A solid grounding in the R.O.A.D. framework, plus concepts such as algorithm tradeoffs, and data quality can serve as useful knowledge for certain Research Scientists. If the research they conduct is in AI or heavily related to the field, this course is a great fit.

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Data Analyst

A Data Analyst interprets data to inform business decisions, requiring an understanding of data types and quality. Although the 'Core Concepts in AI' course is not strictly designed for data analysis, the focus on data quality may be useful to a Data Analyst. Furthermore, the course's content on inter-annotator agreement and the challenges of data labeling can provide a valuable perspective on the reliability and accuracy of data. An understanding of algorithm tradeoffs is useful for a data analyst to help them understand the choices that were made in the data collection process. This can help with understanding the analysis process.

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Software Developer

A Software Developer designs, develops, and tests software applications. The 'Core Concepts in AI' course may be useful for a Software Developer that works with AI-powered software or tools. The understanding of algorithm tradeoffs, and data related challenges might allow them to better debug and improve their code. The course also introduces the R.O.A.D. framework, which may be relevant to their broader project development process. It is possible that this course has value for a Software Developer, particularly for certain application domains.

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Statistician

A Statistician is responsible for designing and carrying out statistical analyses. While not perfectly aligned, the 'Core Concepts in AI' course may be useful for understanding ML model evaluation. The course introduces statistical concepts related to machine learning, such as performance metrics and algorithm tradeoffs. These are concepts that could help a Statistician understand the evaluation process of algorithms. If a Statistician works with teams of data scientists they could find an understanding of the core concepts of AI useful for communicating with those teams.

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Technical Writer

A Technical Writer creates documentation for technical products or processes, and while the 'Core Concepts in AI' course is not directly related, they may find it helpful. The course introduces concepts like the R.O.A.D. framework, algorithm tradeoffs, and data quality, which might be valuable for writing about AI related topics. If a Technical Writer works on documentation for AI software or products, an understanding of the material covered in the course may be helpful. Having this course may make a Technical Writer a more well rounded collaborator.

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Business Development Manager

A Business Development Manager identifies new business opportunities and develops strategies for growth. The 'Core Concepts in AI' course may be helpful for a Business Development Manager working within the AI space. The course covers a broad range of AI topics, including the R.O.A.D. framework and algorithm tradeoffs, which may be valuable for strategic planning and identifying market opportunities. If the focus of the Business Development Manager is AI solutions, the course might be useful for communicating with technical team and client.

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Marketing Specialist

A Marketing Specialist promotes products and services through various channels. Although the 'Core Concepts in AI' course isn't directly related to marketing, it may be useful if they are marketing an AI based product or service. The course introduces core AI concepts, including machine learning, algorithm tradeoffs, and data quality. If they are involved in marketing AI products, this could be a helpful background to contextualize these technologies. They can use this to ensure the message they are conveying is accurate.

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Core Concepts in AI

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