Introduces learners to Python, the most widely used language in AI development. It covers basic syntax, libraries, and tools required for building AI applications.

Python is the go-to language for AI projects due to its simplicity and robust library ecosystem, making it a must-learn for AI enthusiasts.

''Python Full Course for Beginners'' by ''Programming with Mosh'' on YouTube

 A deep dive into key machine learning algorithms such as linear regression, decision trees, and clustering. Focuses on how they work and how they’re applied in real-world projects.

Understanding algorithms allows learners to build AI models that can learn from data and make predictions, a crucial skill for any AI-based business.

''5 Machine Learning Algorithms - Linear/Logistic Regression, Decision Tree, Random Forest & SVM'' by ''Tech Central'' on YouTube

 Introduces popular cloud platforms such as AWS, Google Cloud, and Microsoft Azure, focusing on how to use cloud computing to run large-scale AI models and store vast datasets.

 Cloud tools enable businesses to scale their AI applications easily, providing storage and computational power that isn’t limited by on-premises infrastructure.

''Top 5 Must-Try AWS AI / ML Tools'' by ''Tech With Lucy'' on YouTube

Covers how to deploy AI models into production environments and integrate them with other business systems. This includes version control, monitoring, and updating AI models.

Deploying AI models is key for businesses to turn AI research into real-world solutions, allowing continuous learning and adaptation of their AI systems.

''How To Deploy Machine Learning Models Using FastAPI-Deployment Of ML Models As API’s'' by ''Krish Naik'' on YouTube

 Teaches how to collect, clean, and structure large datasets for AI use. Learners will explore databases, ETL processes, and data pipeline automation.

Proper data engineering ensures that AI models have high-quality data, which is essential for making accurate predictions and decisions.

''Databricks Data Intelligence Platform: Serverless Data Engineering in the Age of AI'' by ''Databricks'' on YouTube

 Explores how AI workflows can be automated using DevOps practices such as CI/CD pipelines. It focuses on how to set up automated testing, deployment, and scaling.

Automation improves efficiency and ensures that AI models are updated and deployed seamlessly, helping businesses maintain competitive AI services.

''CI/CD Pipeline Using GitHub Actions: Automate Software Delivery (for free)'' by ''Alex Hyett'' on YouTube

 This topic presents real-world case studies where AI applications have been deployed using cloud tools in industries like healthcare, finance, and retail.

Seeing AI applications in action helps learners understand how to apply their skills to solve business challenges in various industries.

''The future of AI is now: Real-life case studies for on client-side AI adoption in web apps'' by ''Chrome for Developers'' on YouTube

Welcome to the quiz section. Before you begin, please make sure you have watched the lesson videos thoroughly, as the questions are designed to test your understanding of key concepts discussed.

Here are a few things to keep in mind:

1. This is a timed quiz, so you’ll need to manage your time effectively.
2. Read each question carefully and choose the best answer based on the material covered in the lesson.
3. The quiz is an excellent opportunity to test your knowledge and reinforce what you’ve learned, so try your best!