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Kevin Burns | Side Hustles | Business | Education

Unlock the power of AI to enhance your productivity and streamline your workflow in this intermediate-level course, "AI and Increasing Productivity at Work (Level 2)." Designed for professionals who are familiar with the basics of AI, this course will take your skills to the next level by introducing advanced AI tools, strategies, and techniques that can be seamlessly integrated into your daily work routine.

You will explore:

Read more

Unlock the power of AI to enhance your productivity and streamline your workflow in this intermediate-level course, "AI and Increasing Productivity at Work (Level 2)." Designed for professionals who are familiar with the basics of AI, this course will take your skills to the next level by introducing advanced AI tools, strategies, and techniques that can be seamlessly integrated into your daily work routine.

You will explore:

  • AI-driven task automation: Learn how to automate repetitive tasks and manage projects more efficiently.

  • Advanced AI tools: Discover cutting-edge AI software and platforms that can optimize your time and reduce manual workload.

  • Smart decision-making: Understand how AI can provide data-driven insights to improve decision-making and problem-solving.

  • Collaboration and communication: Leverage AI tools to enhance team collaboration, communication, and virtual meetings.

  • AI for workflow optimization: Find out how to identify bottlenecks in your workflow and implement AI solutions to streamline processes.

By the end of this course, you’ll have the skills to apply AI in practical, real-world scenarios to boost your productivity, improve time management, and achieve your work goals more effectively. This course is ideal for professionals in all industries looking to stay competitive in the rapidly evolving AI landscape.

Key Features:

  • Hands-on projects to apply AI tools to real work scenarios

  • In-depth tutorials on advanced AI software and platforms

  • Practical examples of AI’s impact on productivity across different industries

  • Interactive quizzes to solidify your knowledge

Prerequisites: Basic understanding of AI concepts or completion of my other Udemy course, Introduction to AI and Enhancing Productivity at Work (Level 1).

Enroll now

What's inside

Learning objectives

  • How to increase productivity at work using ai
  • Learn about ai-driven task automation
  • Learn how tuse advanced ai tools
  • Ai work flow automation

Syllabus

Introduction

Transform how you work with AI tools!  Increase your productivity!  Get promoted!

What will you learn in this AI course.

Read more

Some advanced AI tools to help with productivity at work.

What advanced software can you utilize and what does it do?

A summary of the book on how to be more successful and productive.

A little AI history to talk about where we have come from and where we are going with AI.

How can Chat GPT help you to be more productive at work?

How can AI help you with Strategy?

How Netflix incorporated the use of AI.

Zeigarnik was discovered by a Russian scientist in the 1920s, and it may be able to help you with your productivity at work.

Simon Sinek espouses that business is about long-term strategy but much of business incorporates short-term thinking.

A summary of the book.

Using AI to deal with customers/clients.

AI Enhancing productivity at work, case study.

About the past of information networks, the history of AI and the future of AI.  The dangers and the positives of AI.

What does the future hold for these valuable tools?

This book will help with your productivity.

This book will help you to get a great start on your day, and increase your productivity.

As a human behavior investigator, Vanessa Van Edwards studies the hidden forces that drive our behavior patterns in her lab—and she’s cracked the code. In Captivate she shares a wealth of valuable shortcuts, systems and behavior hacks for taking charge of their interactions at work, at home, and in any social situation.

A summary of the helpful book by Erik Qualman.

The proven way for business leaders to lead a team and grow a business.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Builds upon existing knowledge of AI, making it suitable for those looking to expand their understanding and practical application of AI tools in the workplace
Offers insights into how AI can be applied across various sectors, making it relevant for professionals regardless of their specific industry or background
Explores AI's role in enhancing team collaboration, communication, and virtual meetings, which are essential for effective teamwork and project management
Teaches how AI can provide data-driven insights to improve decision-making and problem-solving, which are critical skills for effective leadership and management
Requires a basic understanding of AI concepts or completion of a prerequisite course, which may limit accessibility for complete beginners without prior exposure
Focuses on AI-driven task automation and workflow optimization, which are valuable skills for professionals aiming to streamline processes and improve efficiency

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Reviews summary

Practical ai tools for work

According to learners, this course provides a good overview of using AI for workflow optimization and improving productivity at work. Students appreciate the focus on practical tools and real-world applications, finding it a helpful starting point for applying AI. However, some feel the course offers more breadth than depth on certain topics, suggesting it serves best as an introduction rather than a deep dive. Due to the rapid changes in AI, there are mentions that some content might feel slightly outdated, though it generally offers valuable strategies for leveraging AI in a professional setting.
Well-organized, clear explanations
"The lectures were easy to follow and the course structure was logical."
"Instructor explained concepts clearly, making complex ideas understandable."
"I found the modules well-paced and easy to integrate into my schedule."
"The course was structured in a way that made it easy to absorb the information."
Great overview for beginners
"This course is an excellent starting point if you're new to using AI for productivity."
"Helped me identify which AI tools might be relevant for my specific professional needs."
"Perfect for getting familiar with the landscape of AI tools available for work."
"A great introduction to using AI to enhance daily work tasks and workflows."
Apply AI tools for productivity
"I can now see how to apply these AI tools directly to my daily tasks to boost productivity."
"The examples provided were very practical and helped me understand real-world applications."
"This course gave me concrete strategies for leveraging AI at work."
"It highlighted tools I didn't know about and how they could streamline my tasks."
AI changes fast, some content dated
"AI is evolving so quickly, some tools mentioned have already changed significantly."
"The course provides a good foundation, but be aware some specific tool interfaces may look different now."
"It captures the essence, but staying updated requires looking beyond the course material."
"Content on specific platforms might not be fully current given rapid updates."
Covers topics broadly, lacks depth
"While it covers many tools, I wished for more detailed tutorials on specific platforms."
"Some sections felt a bit like a high-level overview and didn't go deep enough for intermediate users."
"It's a good introduction, but you'll need other resources for in-depth knowledge."
"Could use more hands-on practice or labs for the different tools discussed."

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 for Work: AI Tools, Collaboration, Workflow Optimization with these activities:
Review Foundational AI Concepts
Solidify your understanding of fundamental AI concepts to better grasp the advanced topics covered in the course.
Browse courses on Artificial Intelligence
Show steps
  • Review definitions of AI, ML, and DL.
  • Study the different types of AI algorithms.
  • Practice identifying AI applications in everyday life.
Read 'Working Backwards' by Colin Bryar and Bill Carr
Learn about Amazon's approach to innovation and productivity, which can be applied to leveraging AI for business strategy.
View Working Backwards on Amazon
Show steps
  • Read the book, focusing on the sections related to data-driven decision-making and process optimization.
  • Identify key principles and methodologies.
  • Consider how these principles can be applied to your own work and organization.
Read 'AI Superpowers' by Kai-Fu Lee
Gain a broader understanding of the AI landscape and its impact on global competition and innovation.
View Alter Ego: A Novel on Amazon
Show steps
  • Read the book, focusing on the chapters related to AI applications in business.
  • Take notes on key insights and examples.
  • Reflect on how these insights relate to your own work and industry.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Automate a Repetitive Task with AI
Apply the concepts learned in the course by automating a repetitive task in your work using AI tools.
Show steps
  • Identify a repetitive task that consumes a significant amount of your time.
  • Research AI tools that can automate this task.
  • Implement the AI tool and test its effectiveness.
  • Document the process and results.
Create a Presentation on AI for Workflow Optimization
Solidify your understanding of AI for workflow optimization by creating a presentation on the topic.
Show steps
  • Research AI tools and techniques for workflow optimization.
  • Develop a presentation outline.
  • Create visually appealing slides with clear and concise information.
  • Practice delivering the presentation.
Follow Tutorials on Advanced Automation Platforms
Enhance your skills by following tutorials on advanced automation platforms like UiPath or Automation Anywhere.
Show steps
  • Choose an automation platform to learn.
  • Find and follow tutorials on the platform's features and capabilities.
  • Practice building automation workflows.
Develop an AI Integration Roadmap
Create a roadmap for integrating AI into your team's or organization's workflows.
Show steps
  • Assess current workflows and identify areas for AI integration.
  • Research and select appropriate AI tools and technologies.
  • Develop a detailed implementation plan with timelines and milestones.
  • Present the roadmap to stakeholders.

Career center

Learners who complete AI for Work: AI Tools, Collaboration, Workflow Optimization will develop knowledge and skills that may be useful to these careers:
Productivity Consultant
A Productivity Consultant works with individuals or organizations to improve their efficiency and effectiveness. They assess current practices, identify areas for improvement, and recommend strategies and tools to enhance productivity. To improve one's job as a Productivity Consultant, this course contains modules such as AI at work: productivity bottlenecks, and the Zeigarnik Effect. These modules are directly applicable to finding ways to improve productivity at work. The course is also helpful because of its coverage of the best AI tools for increasing productivity.
Workflow Automation Specialist
A Workflow Automation Specialist designs, implements, and manages automated processes to improve efficiency and reduce manual effort within an organization. This role requires a deep understanding of business processes and the ability to leverage technology to streamline operations. This course helps someone become a Workflow Automation Specialist by presenting how to identify bottlenecks in a workflow and then implement AI solutions that streamline those processes. Modules on advanced automation projects, scaling automation, and enterprise level automation with AI are valuable. This course teaches how to automate repetitive tasks and manage projects more efficiently.
AI Integration Consultant
An AI Integration Consultant advises organizations on how to incorporate AI technologies into their existing systems and workflows. This role requires a strong understanding of both AI technologies and business operations. To be an AI Integration Consultant, this course offers a foundation with its modules on AI at work: integrating AI with workflows, and scaling AI solutions and overcoming challenges. The course is also helpful because it contains a discussion of AI solutions implemented, which provides practical examples. The course also has a case study on company X.
Digital Transformation Manager
A Digital Transformation Manager leads the process of integrating digital technology into all areas of a business, fundamentally changing how it operates and delivers value to customers. This role requires a strong understanding of technology trends, business strategy, and change management. This course helps lead to a career as a Digital Transformation Manager through its broad coverage of AI, which is a core digital technology. Specific modules such as scaling AI solutions and emerging AI technologies are beneficial. The course allows the Digital Transformation Manager to see where AI can be of most use.
Business Process Analyst
A Business Process Analyst examines and documents organizational processes, identifies areas for improvement, and recommends solutions to enhance efficiency and effectiveness. This role requires strong analytical skills, attention to detail, and a solid understanding of business operations. For a Business Process Analyst, this course is useful because it contains information about AI for predictive analytics, AI at work: productivity bottlenecks, and AI for business optimization. All of these AI tools are useful in improving business processes according to data. The course further improves decision making and problem solving abilities.
Project Manager
A Project Manager is responsible for planning, executing, and closing projects, ensuring they are completed on time, within budget, and to the required quality standards. They lead project teams, manage resources, and communicate with stakeholders. This course is helpful for a Project Manager because it includes sections on AI driven project management, AI for resource planning, and AI for project forecasting. Knowing these topics helps a project manager to plan more optimally, and also make more data-driven decisions. A project manager will also find the discussion of continuous improvement useful.
Data Analyst
A Data Analyst collects, processes, and analyzes data to identify trends and insights that can be used to improve business decision-making. This role requires strong analytical skills, attention to detail, and proficiency in data analysis tools and techniques. The career of Data Analyst is enhanced by this course, due to the modules on AI for predictive analytics, AI for data driven business strategy, and AI for predictive decision making. These will help the Data Analyst leverage AI in their career. The course also focuses on choosing the right AI tools.
Change Management Specialist
A Change Management Specialist helps organizations implement and manage change initiatives, ensuring smooth transitions and minimal disruption to operations. They develop change management plans, communicate with stakeholders, and provide training and support to employees. This course is helpful for someone who wants to be a Change Management Specialist, because it contains the module preparing for AI changes. Change management relies on effective communication and training. The Change Management Specialist will find the material on AI and human-machine collaboration very helpful.
Team Lead
A Team Lead is responsible for guiding and supporting a team of individuals to achieve common goals. They provide direction, monitor performance, and foster a collaborative environment. This course helps someone who aspires to be a Team Lead through its modules on enhancing team collaboration with AI, team building, keeping morale high, AI in team building, and fostering collaborative culture. The course enables the Team Lead to leverage AI in performing their job.
Training and Development Specialist
A Training and Development Specialist creates and delivers training programs to improve employee skills and knowledge. This role requires strong communication and instructional design skills. This course teaches a Training and Development Specialist how to create training programs that focus on AI tools. The course has modules on topics such as hybrid skills training, creating an enhanced learning culture, and scaling. The case study on company X provides a practical example of training in the workplace.
Innovation Manager
An Innovation Manager is responsible for fostering a culture of innovation within an organization, identifying new opportunities, and developing innovative solutions to business challenges. This role requires creativity, strategic thinking, and a strong understanding of market trends. This course provides training on topics such as AI augmenting human creativity, scaling AI solutions, and continuous improvement. These will provide the foundation for identifying opportunities and helping to develop innovative solutions. This course also helps in understanding market trends.
Operations Manager
An Operations Manager oversees the day-to-day activities of an organization to ensure efficiency and effectiveness. They manage resources, implement processes, and monitor performance to meet organizational goals. This course may be useful to an Operations Manager. Topics such as AI for business optimization, benefits of AI in processing optimization, and AI at work continuous improvement are valuable. These will inform the Operations Manager how to improve processes to meet organizational goals.
Digital Strategist
A Digital Strategist develops and implements digital marketing strategies to achieve business goals. They analyze market trends, identify target audiences, and create campaigns to engage customers and drive sales. For a Digital Strategist, this course on AI may be useful because of its coverage of AI for data-driven business strategy, AI for predictive decision making, and AI augmenting human creativity. These modules are useful for creating strategies that make use of AI. Furthermore, the course covers using AI to deal with customers and clients.
Human Resources Manager
A Human Resources Manager oversees the recruitment, hiring, and training of employees, as well as managing employee relations and ensuring compliance with labor laws. This role requires strong interpersonal and organizational skills. This course may be useful to a Human Resources Manager. The course has modules on AI in performance management, enhanced employee engagement, upskilling employees for collaboration, hybrid skills training, and creating an enhanced learning culture. All of these areas intersect with the concerns of human resources.
Management Consultant
A Management Consultant advises organizations on how to improve their performance and efficiency. They analyze business problems, develop solutions, and help implement changes. This course may be useful to someone who aims to be a Management Consultant. It has sections on AI for data driven business strategy, AI for predictive decision making, and building a road map. These modules help consultants who wish to find and implement AI solutions.

Reading list

We've selected 50 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 for Work: AI Tools, Collaboration, Workflow Optimization.
Classic text on project management that provides a comprehensive overview of the field. It great resource for anyone who wants to learn about project management or improve their skills.
The definitive mathematical canon of modern deep learning, establishing foundational theory across numerical computation, backpropagation optimization, autoencoders, and generative modeling.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
This practical book dives into using transformers, the dominant architecture for many state-of-the-art NLP tasks, with the Hugging Face Transformers library. , It's ideal for data scientists and coders who want to learn how to build and scale models for various NLP applications using this popular library. It covers contemporary topics and is highly relevant for current NLP practice.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
This comprehensive textbook provides a broad overview of the field of artificial intelligence, covering fundamental concepts, algorithms, and applications. It is an essential resource for anyone interested in understanding the basics of AI.
Guides deep learning practitioners step-by-step through implementing transformer self-attention, token embeddings, and multi-layer perceptrons in pure PyTorch without relying on high-level wrappers.
Provides a broad and accessible introduction to predictive analytics for a general audience. It focuses on the real-world applications and implications of predictive analytics across various industries, without getting bogged down in technical details. It's an excellent starting point for anyone looking to understand the fundamental concepts and the impact of predictive analytics.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Illuminates modern generative neural architectures through celebrated visual diagrams and code workflows, covering contrastive embeddings, semantic search, and retrieval-augmented generation.
Examines production-grade engineering patterns for training and serving large language models, addressing distributed GPU training, synthetic data pipelines, and quantitative model evaluations.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Psychological safety critical component of high-performing teams, allowing members to speak up and take risks without fear of negative consequences. explores the concept of psychological safety and its impact on team learning, innovation, and growth. It's a must-read for leaders and teams aiming to create an environment of trust and open communication.
Delivers an interactive, comprehensive curriculum teaching multi-layer perceptrons, convolutional neural networks, attention mechanisms, and optimization math with runnable multi-framework code.
Comprehensive guide to project management that provides answers to common questions. It great resource for anyone who wants to learn more about project management or solve specific problems.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Drawing on military experience, this book offers a contemporary perspective on how large organizations can achieve the agility and adaptability of small teams. It emphasizes decentralized decision-making and shared consciousness through transparent communication. is valuable for understanding modern approaches to collaboration in complex environments and is relevant for both students and professionals in dynamic fields.
Focuses on the business perspective of data science and data mining, explaining how to approach business problems with data-analytic thinking. It covers key concepts like classification, prediction, andकडील. It's highly relevant for understanding how predictive analytics can be applied to create business value.
Offers a comprehensive guide to the predictive modeling process, covering data preprocessing, model tuning, and various regression and classification techniques. It emphasizes practical application with numerous real-life examples and includes extensive R code. This valuable resource for those looking to deepen their understanding and gain hands-on experience.
Practical guide to project management that is full of insights and advice. It great resource for anyone who wants to learn how to manage projects more effectively.
Practical guide to agile project management that is full of tips and advice. It great resource for anyone who wants to learn how to use agile methods to improve their project management skills.
This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Considered a classic in the field, this book delves into what distinguishes real teams from other working groups and how to create high-performing teams. It provides a foundational understanding of team types and dynamics, making it a valuable resource for both students and professionals seeking to build effective teams.
Effective communication is at the heart of successful team collaboration. provides practical tools and techniques for navigating high-stakes discussions, resolving conflict, and building stronger relationships within teams. It's an essential resource for anyone looking to improve their interpersonal communication skills in a team setting.
Focuses specifically on time series forecasting, a key area within predictive analytics. It covers a wide range of forecasting methods, from simple approaches to more advanced models, and provides practical guidance on applying them. It's an excellent resource for those interested in predicting future values based on historical data.
Focuses on modeling techniques for predictive analytics, specifically addressing business problems and providing solutions using R. It combines business context with quantitative tools, making it relevant for analysts and managers. It covers various business cases and analytical methods.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Details battle-tested infrastructure patterns for deploying deep reasoning models, addressing memory bandwidth limits, continuous GPU batching, semantic caching, and operational latency minimization.
Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Presents a framework for giving and receiving feedback effectively, which is crucial for team growth and performance. It encourages leaders and team members to care personally while challenging directly. This book is highly relevant for fostering a culture of open communication and continuous improvement within teams.
Focuses on token segmentation, attention context preservation, and hierarchical chunking, giving deep learning engineers targeted techniques to maximize transformer retrieval accuracy.
This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Analyzes edge-inference architectures for open-weights neural models, tackling parameter quantization, GGUF execution, KV cache memory bottlenecks, and efficient desktop serving.
Demonstrates how to leverage PyTorch 2.0 compiler optimizations and torch.compile to accelerate transformer training, attention layer fusion, and distributed model parallelism.
Published recently, this book focuses on the practical implementation and operationalization of predictive analytics within a business context. It covers the necessary tools, frameworks, and best practices for deploying predictive models in an enterprise setting, making it highly relevant for professionals.
Shows how to use Six Sigma principles to optimize workflows and improve business processes. It provides a step-by-step approach to process improvement, and includes numerous examples and case studies.
Bridges the gap between data science and business, focusing on how predictive analytics and machine learning can be applied to solve business problems and make better decisions. It's particularly relevant for those interested in the business applications of predictive analytics.
Offers a compelling true story of how a leader empowered his team by giving control rather than taking control. It provides practical examples of how to foster a leader-leader model, enhancing team autonomy and collaboration. It's particularly relevant for those interested in leadership within a team context.

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