LinkedIn Learning offers a wide range of AI courses that include hands-on projects, allowing learners to apply theoretical knowledge to practical scenarios. These courses cater to various skill levels and cover different aspects of AI, from machine learning to generative AI. Here’s a comprehensive look at some of the most notable AI courses on LinkedIn Learning that offer hands-on projects:
Hands-On AI: Build a Generative Language Model from Scratch
This advanced-level course, led by instructor Ronnie Sheer, provides a deep dive into AI by guiding learners through the process of building their own AI from scratch1. The course includes two main hands-on projects:
- Creating a text completion model similar to smartphone autocomplete functionality.
- Building a model that filters out hateful comments on social media.
Additionally, learners get to work on a creative project where they use lyrics from their favorite songs to generate new, thematically similar lyrics. This course is particularly valuable for those who want to understand the inner workings of generative language models.
Learning AI with GitHub Copilot
This course, created in collaboration with Microsoft Learn, offers a hands-on approach to learning AI through GitHub Copilot4. Learners get to:
- Set up Python and Jupyter notebooks with Visual Studio Code
- Explore machine learning fundamentals
- Delve into AI’s role in computer vision
- Install and use the GitHub Copilot Labs extension for Python notebooks
This course is excellent for developers who want to integrate AI tools into their workflow and gain practical experience with GitHub Copilot.
UX for AI Design Practices for AI Developers
Led by John Maeda, VP of Design & AI at Microsoft, this advanced course focuses on ethical and effective UX design for AI applications4. While specific project details aren’t provided, the course likely includes hands-on exercises in:
- Collaborative UX using tools like Microsoft Copilot stack and Semantic Kernel
- Prioritizing user needs while leveraging AI models
This course is ideal for developers and designers looking to create user-friendly AI applications.
Hands-on Data Science and AI for Healthcare
This advanced course, taught by Wuraola Oyewusi, offers practical experience in applying data science and AI techniques to healthcare problems4. While specific project details aren’t mentioned, learners can expect hands-on exercises related to:
- Data analysis in healthcare contexts
- Building AI models for healthcare applications
- Addressing real-world healthcare challenges using AI
This course is particularly valuable for those interested in the intersection of AI and healthcare.
Machine Learning with Python: Foundations
Instructed by Frederick Nwanganga, this course provides step-by-step guidance on collecting, understanding, and preparing data for machine learning using Python5. Hands-on projects likely include:
- Data preprocessing and cleaning
- Implementing basic machine learning algorithms
- Evaluating model performance
This course is excellent for beginners looking to get practical experience with machine learning in Python.
Introduction to Prompt Engineering for Generative AI
Led by Ronnie Sheer, this course offers hands-on experience with various AI tools, including ChatGPT, DALL-E, and Midjourney5. Learners get to:
- Practice crafting effective prompts for different AI models
- Explore advanced topics like interacting with language models using an API
This course is ideal for those looking to gain practical skills in working with generative AI tools.
Python Data Structures and Algorithms
Instructed by Robin Andrews, this course provides hands-on programming experience with Python data structures and algorithms5. Projects likely include:
- Implementing various data structures (stacks, queues, priority queues)
- Coding algorithms like depth-first search and breadth-first search
- Solving practical problems using these data structures and algorithms
While not exclusively focused on AI, this course provides fundamental skills crucial for many AI and machine learning applications.
Building a Project with the ChatGPT API
This course, taught by Kesha Williams, offers a hands-on approach to working with the ChatGPT API6. Learners likely get to:
- Set up and configure the ChatGPT API
- Build a practical project that integrates ChatGPT functionality
- Explore best practices for working with large language models in real-world applications
This course is excellent for developers looking to integrate ChatGPT into their own projects.
GPT-4 Foundations: Building AI-Powered Apps
Instructed by Denys Linkov, this course focuses on building applications powered by GPT-46. Hands-on projects likely include:
- Creating a simple AI-powered application using GPT-4
- Exploring different use cases and implementations of GPT-4
- Addressing challenges in AI app development
This course is ideal for developers looking to leverage the latest GPT technology in their applications.
Excel and ChatGPT: Data Analysis Power Tips
Led by Chris Dutton, this course combines Excel skills with ChatGPT for data analysis6. Hands-on projects might include:
- Using ChatGPT to generate Excel formulas and macros
- Enhancing data analysis workflows with AI assistance
- Solving complex data problems using a combination of Excel and ChatGPT
This course is particularly useful for data analysts and business professionals looking to enhance their Excel skills with AI.
Building Computer Vision Applications with Python
Instructed by Eduardo Corpeño, this 2-hour course offers hands-on experience in creating image processing applications using Python7. Projects likely include:
- Implementing basic image processing techniques
- Building simple computer vision applications
- Working with popular computer vision libraries in Python
This course is excellent for those interested in the practical aspects of computer vision and AI-powered image analysis.
Hands-On PyTorch Machine Learning
Taught by Helen Sun, this course provides practical experience with PyTorch, a popular machine learning framework7. Hands-on projects might include:
- Setting up a PyTorch environment
- Implementing basic neural networks
- Training and evaluating machine learning models using PyTorch
This course is ideal for those looking to gain practical skills in modern machine learning frameworks.
Table: AI Courses with Hands-On Projects on LinkedIn Learning
| Course Name | Instructor | Duration | Skill Level | Key Projects/Skills |
|---|---|---|---|---|
| Hands-On AI: Build a Generative Language Model from Scratch | Ronnie Sheer | 34m | Advanced | Text completion model, Hate speech filter, Lyric generation |
| Learning AI with GitHub Copilot | Microsoft Learn | Not specified | Not specified | Python setup, Machine learning basics, Computer vision with GitHub Copilot |
| UX for AI Design Practices for AI Developers | John Maeda | Not specified | Advanced | Collaborative UX design, AI-powered UX |
| Hands-on Data Science and AI for Healthcare | Wuraola Oyewusi | Not specified | Advanced | Healthcare data analysis, AI models for healthcare |
| Machine Learning with Python: Foundations | Frederick Nwanganga | Not specified | Not specified | Data preprocessing, Basic ML algorithms, Model evaluation |
| Introduction to Prompt Engineering for Generative AI | Ronnie Sheer | 44m | Not specified | Prompt crafting for ChatGPT, DALL-E, Midjourney |
| Python Data Structures and Algorithms | Robin Andrews | Not specified | Not specified | Implementing data structures, Coding search algorithms |
| Building a Project with the ChatGPT API | Kesha Williams | 1h 56m | Not specified | ChatGPT API integration, Building AI-powered applications |
| GPT-4 Foundations: Building AI-Powered Apps | Denys Linkov | 1h 2m | Not specified | Creating GPT-4 powered applications |
| Excel and ChatGPT: Data Analysis Power Tips | Chris Dutton | 1h 41m | Not specified | AI-assisted Excel formulas, Enhanced data analysis |
| Building Computer Vision Applications with Python | Eduardo Corpeño | 2h | Not specified | Image processing, Computer vision applications |
| Hands-On PyTorch Machine Learning | Helen Sun | 56m | Not specified | PyTorch setup, Neural network implementation |
FAQ
Q1: Are these courses suitable for beginners in AI?
A1: While some courses like “Machine Learning with Python: Foundations” are suitable for beginners, many of the listed courses are intermediate to advanced level. It’s recommended to start with foundational courses if you’re new to AI and programming.
Q2: Do I need prior programming experience for these courses?
A2: Most of these courses assume some level of programming knowledge, particularly in Python. Courses focusing on tools like ChatGPT or Excel might be more accessible to non-programmers.
Q3: How long does it take to complete these courses?
A3: Course durations vary from about 30 minutes to 2 hours of video content. However, completing the hands-on projects may require additional time.
Q4: Are certificates provided upon completion?
A4: Yes, LinkedIn Learning typically provides certificates of completion for finished courses, which can be shared on your LinkedIn profile1.
Q5: Can I access these courses on mobile devices?
A5: Yes, LinkedIn Learning offers mobile access, allowing you to learn on tablets and phones1.
Q6: Are these courses updated regularly to keep up with AI advancements?
A6: LinkedIn Learning strives to keep its content current, especially in rapidly evolving fields like AI. Many of these courses are recent additions or updates to reflect the latest AI trends and technologies45.
Q7: Do I need any special software or hardware to complete the hands-on projects?
A7: Requirements vary by course. Some may require specific software installations (like Python or PyTorch), while others might use cloud-based tools. Check the course details for specific requirements.
Q8: How interactive are these courses?
A8: These courses are designed to be interactive, with hands-on projects and exercises. Some courses may also include quizzes or assessments to test your understanding2.
Q9: Can I get help if I’m stuck on a project?
A9: LinkedIn Learning courses often include Q&A sections where you can ask questions. Additionally, some instructors may be available for queries on LinkedIn3.
Q10: Are these courses part of any larger learning paths or certifications?
A10: Some of these courses may be part of larger learning paths on LinkedIn Learning. For example, there’s a “Getting Started with AI and Machine Learning” learning path that includes several courses7.
In conclusion, LinkedIn Learning offers a diverse range of AI courses with hands-on projects, catering to various skill levels and interests within the AI field. These courses provide practical, real-world experience that can be invaluable for professionals looking to enhance their AI skills or transition into AI-related roles. Whether you’re interested in generative AI, machine learning, computer vision, or AI in specific domains like healthcare, there’s likely a course that fits your needs. The hands-on nature of these courses ensures that learners not only understand the theoretical concepts but also gain practical skills that can be applied in real-world scenarios.
Citations:
- https://www.linkedin.com/learning/hands-on-ai-build-a-generative-language-model-from-scratch
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