IBM is known for its expertise in the field of artificial intelligence (AI) and its commitment to providing quality educational resources. In this article, we will explore IBM's top 5 handpicked AI courses that are designed to help learners enhance their AI skills and knowledge. The best part? All of these courses are completely free, making them accessible to anyone interested in advancing their understanding of AI.
1. The AI Ladder: A Framework for Deploying AI in your Enterprise
The AI Ladder course is specifically designed for business and technical leaders who are considering AI-based solutions for their enterprises. It offers a comprehensive framework for deploying AI in the business environment.
Course details
This course is designed for business and technical leaders who are considering AI-based solutions for business challenges in their enterprises.
Objectives
- Summarize the main technologies and themes in today's AI landscape.
- Explain the concept of the AI ladder and its relevance to business enterprises.
- Describe the importance of information architecture to successful AI implementations.
- Define and elaborate the "Collect" step of the AI Ladder.
- Define and elaborate the "Organize" step of the AI Ladder.
The course covers various aspects of the AI landscape, including the concept of the AI ladder and its relevance to business enterprises. Learners will gain insights into the importance of information architecture for successful AI implementations. It dives into the four steps of the AI ladder - Collect, Organize, Analyze, and Infuse - providing detailed explanations and practical guidance. By the end of this course, learners will have a solid understanding of AI deployment strategies and be equipped to drive AI initiatives within their organizations.
2. Reducing Unfair Bias in Machine Learning
Bias in machine learning models is a critical concern that needs to be addressed to build trust in AI systems. This course focuses on mitigating discrimination and bias in machine learning models throughout the AI application lifecycle.
Course details
The need for trust in AI has made reducing bias in machine learning models essential. This course provides learners with an overview of the concept of fairness and its importance in building trust in AI systems. It introduces the "AI Fairness 360" open-source toolkit, which helps implement debiasing techniques to measure, understand, and mitigate AI bias.
Objectives
- Recognize the need for Trustworthy AI.
- Describe and differentiate various factors that can build trust in AI.
- Appraise situations that require a focus on fairness.
- Analyze where unwanted bias comes from.
- Recognize methods to mitigate unwanted bias.
Learners will gain insights into AI fairness and bias concepts, learn how to measure bias in models, and discover methods to apply fairness algorithms to reduce unwanted bias. Hands-on labs included with this course provide learners with an opportunity to gain practical experience in addressing bias in machine learning.
3. AI FactSheets for Transparency and Governance
As AI models become more complex and sophisticated, understanding their decision-making process has become increasingly challenging. This course focuses on the concept of explainability, which plays a crucial role in building trust in AI systems. It introduces the "AI Explainability 360" open-source toolkit and provides learners with the knowledge and tools to create explainable machine learning models.
Course details
This course is intended primarily for Analytics Leaders, Data Science Leaders and Practicing Data Scientists, Machine Learning Engineers, and AI specialists. It is designed for individuals with an interest in Explainable AI and AI Trust.
Prerequisites
In order to be successful in this course, learners should have a basic understanding of the Data Science workflow, including Data Preprocessing, Feature Engineering, Machine Learning Models, Hyperparameter Optimization, and Evaluation measures for models. While Python knowledge is helpful, it is not necessary.
Objectives
- Recognize the need for Trustworthy AI.
- Describe and differentiate various factors that can build trust in AI.
- Appraise situations that require a focus on AI explainability.
- Recognize different methods of achieving explainability.
The course covers the big picture of Trustworthy AI and AI explainability. Learners will explore different methods of achieving explainable AI and understand the role of the open-source AI Explainability 360 toolkit in supporting explainability. Hands-on exercises and demonstrations enable learners to apply explainability algorithms and create interpretable models.
4. Essentials of AI and Cloud
This short course serves as a no-frills introduction to the essential concepts in Artificial Intelligence and cloud computing. It focuses on key definitions, concepts, and applications, with an emphasis on the business aspect. By completing this course, learners will gain the ability to critically analyze and discuss AI and Cloud technologies and separate facts from buzzwords.
Course details
This course is suitable for anyone who wants to learn the tooling required to perform Data Science in an enterprise environment.
Objectives
- Set up and set the scene with IBM Watson Studio.
- Gather and access data for the team with IBM Watson Studio.
- Prototype with IBM Watson Studio Collaborative Notebooks.
The course takes learners through the essential steps of setting up and using IBM Watson Studio, which provides a collaborative environment for data science projects. Learners will gain hands-on experience in gathering and accessing data and prototyping with collaborative notebooks.
5. Natural Language Processing and Search with IBM Watson
This course is a quick getting started guide to Natural Language Processing (NLP), Watson Natural Language Understanding (NLU), and Watson Discovery. It is designed for AI specialists at the associate level who want to gain an understanding of NLP fundamentals and learn how to leverage Watson NLU and Watson Discovery in real-world business applications.
Course details
This course is suitable for anyone interested in learning how NLP, Watson NLU, and Watson Discovery can enrich organizations.
Objectives
- Introduction to NLP.
- Introduction to NLU and Watson NLU.
- Introduction to Watson Discovery.
The course provides a comprehensive introduction to NLP, Watson NLU, and Watson Discovery. Learners will gain a foundational understanding of NLP principles and explore the functions and features of Watson NLU and Watson Discovery. With this knowledge, learners can leverage NLP and IBM Watson technologies to enhance their organizations' capabilities.
Don't miss out on this opportunity to expand your AI knowledge and take your skills to the next level. Also, if you're looking to intern at IBM, don't forget to check out our Comprehensive Guide to IBM Internships.