Introduction
2023 has been the year of artificial intelligence, and I have full faith it will come out as one of the most transformative periods in the history of AI. With the increasing prevalence of AI projects across industries, the demand for project managers with expertise in managing AI-driven initiatives has also seen significant growth. As AI technologies continue to be integrated into business operations, there is a pressing need for skilled project managers who understand AI projects’ technical and strategic aspects.
For skilled project managers, courses are required that can assist them in handling large-sized projects. Do you know there are free AI courses for project management by Google that can help you upskill in 2024? In this article, we have covered the best free AI courses for project management you can start today.
FREE AI Courses for Project Management Online
Chris Donnelly curated and researched these free AI courses for project management. He is a prominent keynote speaker, author, and entrepreneur known for his insights into startups, scale-ups, AI, and entrepreneurial journeys. Let’s hop in!
Project Management Course 1: Introduction to Generative AI
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Introduction to Generative AI is a foundational microlearning course designed to introduce learners to the concept of generative AI, its mechanisms, and its applications. The course aims to provide a solid grounding in generative AI and its distinctions from traditional machine learning approaches. Learners will also explore various generative AI models and their real-world uses.
Course Objectives
- Define Generative AI: Understand generative AI and how it differentiates from other AI and machine learning forms.
- Explain How Generative AI Works: Learn the underlying principles and processes that power generative AI, including how it creates new data, such as images, text, and audio.
- Describe Generative AI Model Types: Explore the different models within generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and their specific use cases.
- Describe Generative AI Applications: Discover various applications of generative AI across different industries, including content creation, data augmentation, and more.
The course also introduces learners to Google tools that can assist in developing their own generative AI applications. Through this course, participants will gain valuable insights into generative AI’s potential and how it can be applied in their work or projects.
Project Management Course 2: Prompt Design in Vertex AI
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Prompt Design in Vertex AI is an introductory course demonstrating key skills in prompt engineering, image analysis, and multimodal generative techniques within Google’s Vertex AI. It offers a comprehensive overview of how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
Course Objectives
- Gain Foundational Understanding of Generative AI Implementation: Acquire essential knowledge of how generative AI can be implemented in various applications, particularly within the Vertex AI environment.
- Develop Expertise in Prompt Engineering with Gemini: Learn how to design and refine prompts using Gemini models in Vertex AI to achieve desired outcomes and effectively guide generative AI.
- Explore Multimodal Capabilities with Gemini: Understand how to utilize Gemini’s capabilities across multiple modes, such as text, image, and audio, for a versatile approach to AI tasks.
- Apply Gemini to a Real-World Marketing Scenario: Apply the knowledge and skills gained in the course to real-world marketing scenarios, leveraging generative AI and prompt engineering to address marketing challenges and improve results.
By completing the skill badge for Prompt Design in Vertex AI, learners demonstrate their proficiency in these crucial areas and their ability to harness the power of generative AI in practical applications effectively. This course especially benefits those seeking to enhance their AI-driven marketing and content creation expertise.
Project Management Course 3: Introduction to Duet AI in Google Workspace
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Introduction to Gemini for Google Workspace” is a course designed to introduce learners to the Gemini add-on, which brings generative AI features to Google Workspace. The course overviews how Gemini uses AI capabilities to enhance productivity and efficiency within Google Workspace.
Course Objectives
- Define Generative AI and Understand Its Potential, Challenges, and Limitations: Gain a foundational understanding of generative AI, including its potential benefits and the challenges and limitations that come with its implementation.
- Outline the Main Features in the Gemini Enterprise Add-on: Explore the key features of Gemini within Google Workspace, including how these features can streamline workflows, automate tasks, and enhance collaboration.
- Understand How to Use Gemini Responsibly: Learn the principles of responsible AI use, including ethical considerations and best practices when integrating Gemini into your Google Workspace environment.
Through this course, learners will discover how Gemini can optimize their use of Google Workspace by providing generative AI-powered features that improve productivity and overall performance. The course also emphasizes the importance of using Gemini ethically and responsibly, ensuring AI’s safe and effective integration into daily workflows.
Project Management Course 4: Responsible AI – Applying AI Principles with Google
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Responsible AI: Applying AI Principles with Google Cloud is a course that delves into the importance of responsibly building and utilizing Artificial Intelligence and Machine Learning within an enterprise context. As AI continues to evolve and expand its reach, ensuring ethical and responsible development and use becomes a crucial aspect of its application. This course is ideal for those interested in operationalizing responsible AI within their organizations.
Course Objectives
- Explain the Business Case for Responsible AI: Understand the value of adopting responsible AI practices from a business perspective, including risk management, trust-building, and long-term success.
- Identify Ethical Considerations with AI Using Issue Spotting Best Practices: Learn how to spot and address ethical issues associated with AI implementation using best practices in issue spotting.
- Describe How Google Developed and Put Their AI Principles into Practice and Leverage Their Lessons Learned: Gain insights into Google’s journey with responsible AI, including developing its AI principles and applying them in real-world scenarios.
- Adopt a Framework for How to Operationalize Responsible AI in Your Organization: Discover a structured approach to integrate responsible AI practices into your organization, including governance, processes, and cultural shifts.
- Discover Next Steps to Continue Your Responsible AI Journey: Learn about the resources, tools, and ongoing initiatives available to help you maintain and advance your responsible AI journey.
By participating in this course, learners will gain the knowledge and tools necessary to navigate the complexities of AI ethics and responsibility. They will be equipped to implement responsible AI principles, thereby fostering a culture of accountability and ethical AI usage within their organizations.
Project Management Course 5: Conversational AI on Vertex AI and Dialogflow CX
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Conversational AI on Vertex AI and Dialogflow CX is a course designed to guide learners in creating advanced virtual agents using Dialogflow CX’s new generative AI features. These virtual agents enable more natural and engaging customer conversations, enhancing the overall customer experience. The course provides a comprehensive overview of deploying generative fallback responses, using generators to increase intent coverage, and effectively managing and structuring data in a data store.
Course Objectives
- Articulate and Explain the Functionality of the New Generative AI Features in Dialogflow CX: Understand the capabilities and benefits of generative AI features in Dialogflow CX and how they enhance the functionality of virtual agents.
- Implement Generators to Increase Intent Coverage in Customer Conversations: Learn how to deploy generators in virtual agents to increase the range of customer intents that can be recognized and responded to, leading to more comprehensive and satisfying interactions.
- Enable Generative Fallback Responses to Gracefully Handle Errors and Omissions in Customer Conversations: Discover how to deploy generative fallback responses to manage and recover from errors and gaps in conversations, ensuring a smoother and more natural dialogue.
- Deploy and Maintain Generative AI Agents That Use Your Data: Explore best practices for deploying and maintaining generative AI agents that leverage your data, ensuring effective and targeted customer interactions.
By the end of this course, learners will be well-versed in creating virtual agents that utilize generative AI features to provide more dynamic and engaging conversations with customers. This knowledge can be applied to enhance customer support and other conversational interfaces, ultimately improving customer satisfaction and operational efficiency.
Project Management Course 6: Attention Mechanism
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Attention Mechanism” is a course designed to introduce learners to a powerful machine learning technique that allows neural networks to focus on specific parts of an input sequence. This selective focus enhances the performance of various machine learning tasks, such as machine translation, text summarization, and question answering.
Course Objectives
- Understand the Concept of Attention and How It Works: Gain a solid understanding of the attention mechanism and its role in machine learning. Learn how attention enables neural networks to assign varying importance to different parts of an input sequence, allowing them to process and interpret data more effectively.
- Learn How Attention Mechanism Is Applied to Machine Translation: Explore the practical application of the attention mechanism in machine translation tasks. Discover how it can improve translation quality by allowing the model to concentrate on relevant parts of the source text when generating translations.
By the end of the course, learners will have a comprehensive understanding of the attention mechanism and its significance in enhancing the performance of various machine learning tasks. This knowledge is essential for those interested in advancing their machine learning expertise, particularly in natural language processing and translation.
Project Management Course 7: Create Image Captioning Models
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Create Image Captioning Models” is a comprehensive course that guides learners through creating image captioning models using deep learning techniques. Image captioning models can automatically generate captions for images, making them valuable for various applications such as content generation, accessibility, and multimedia analysis.
Course Objectives
- Understand the Different Components of an Image Captioning Model: Gain an in-depth understanding of the various components of an image captioning model, such as the encoder and decoder. Learn how these components work together to interpret visual content and generate textual descriptions.
- Learn How to Train and Evaluate an Image Captioning Model: Master the skills needed to train an image captioning model using datasets of images and corresponding captions. Explore methods for evaluating your model’s performance, ensuring that it generates accurate and meaningful captions.
- Create Your Own Image Captioning Models: Acquire practical experience in building your own image captioning models from scratch. This includes designing the model architecture, setting up training pipelines, and optimizing the model’s performance.
- Use Your Image Captioning Models to Generate Captions for Images: Apply your knowledge and models to generate captions for new images. Discover how to utilize your models in various scenarios and applications, such as automated content description, multimedia management, etc.
By the end of this course, learners will be equipped with the knowledge and skills needed to create and apply image captioning models effectively. This expertise can be valuable for professionals working in computer vision, natural language processing, and related fields.
Project Management Course 8: Encoder-Decoder Architecture
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Encoder-Decoder Architecture is a course that provides a comprehensive overview of encoder-decoder architecture, a widely used and powerful machine learning framework for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. Through this course, learners understand architecture’s components and how to train and serve these models effectively.
Course Objectives
- Understand the Main Components of the Encoder-Decoder Architecture: Explore the key elements of the encoder-decoder architecture, including how the encoder processes and encodes input sequences and generates output sequences based on the encoded information.
- Learn How to Train and Generate Text from a Model Using the Encoder-Decoder Architecture: Acquire the skills needed to train an encoder-decoder model for tasks such as text generation. Learn how to optimize the model’s performance and generate high-quality textual outputs from the trained model.
- Learn How to Write Your Own Encoder-Decoder Model in Keras: Gain hands-on experience in coding your own encoder-decoder model in TensorFlow’s Keras library. This practical component of the course enables you to create an encoder-decoder model from scratch and apply it to specific tasks, such as poetry generation.
Through the lab walkthrough and hands-on coding exercises, learners will solidify their understanding of the encoder-decoder architecture and its applications. By the end of the course, you will be able to design, train, and implement your encoder-decoder models for various sequence-to-sequence tasks.
Project Management Course 9: ML Pipelines on Google Cloud
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ML Pipelines on Google Cloud” is a course that provides learners with in-depth knowledge and skills for working with machine learning (ML) pipelines on Google Cloud. Guided by ML engineers and trainers, this course covers state-of-the-art development of ML pipelines and metadata management using Google’s production ML platform, TensorFlow Extended (TFX), and other ML frameworks and orchestration tools.
Course Objectives
- Orchestrate Model Training and Deployment with TFX and Cloud AI Platform: Learn how to use TFX and Cloud AI Platform to effectively train and deploy ML models. Also, learn about pipeline components and orchestration for efficient model management.
- Operate Deployed Machine Learning Models Effectively and Efficiently: Gain insights into best practices for operating and managing deployed ML models, including monitoring, evaluation, and optimization.
- Perform Continuous Training Using Various Frameworks (Scikit Learn, XGBoost, PyTorch) and Orchestrate Pipelines Using Cloud Composer and MLflow: Explore how to perform continuous training across multiple ML frameworks and orchestrate pipelines using tools like Cloud Composer and MLflow.
- Integrate ML Workflows with Upstream and Downstream Data Management Workflows to Maintain End-to-End Lineage and Metadata Management: Learn how to integrate ML workflows with data management workflows, ensuring end-to-end data lineage and robust metadata management throughout the ML lifecycle.
Through this course, learners will gain practical experience and knowledge in building, automating, and managing ML pipelines on Google Cloud. By the end of the course, you will be equipped to work efficiently with various ML frameworks and orchestration tools, leading to better model performance and streamlined deployment and maintenance processes.
Project Management Course 10: Google Cloud Solutions II: Data and Machine Learning
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In this advanced-level quest, you will delve into the world of big data and machine learning, leveraging Google Cloud’s powerful computing capabilities to run complex jobs. This course is designed for experienced professionals looking to enhance their skills in implementing advanced big data and machine learning practices as utilized by Google’s Solutions Architecture team.
Through hands-on labs and real-world use cases, you will gain practical experience in a variety of scenarios:
- BigQuery Analytics: Learn how to run analytics on massive datasets, such as tens of thousands of basketball games, using Google Cloud’s BigQuery. This will allow you to extract insights from large volumes of data efficiently and effectively.
- TensorFlow Image Classifiers: Explore the training and deployment of TensorFlow image classifiers on Google Cloud. This includes understanding how to work with image data, train models, and apply them to real-world problems.
- Utilizing Google Cloud for Big Data and Machine Learning: Discover why Google Cloud is the go-to platform for running big data and machine learning jobs. Learn about its scalable infrastructure, advanced tools, and services tailored to support complex ML and big data tasks.
Conclusion
The rapid advancement of AI means that staying current is critical. These free online courses will equip you with the tools and knowledge to excel in project management and drive successful outcomes. Remember, if you don’t adopt ChatGPT and AI tools for project management, your competitors who do will likely surpass you.
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