This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll explore:  

• Foundations of AI, ML, and DL: Introduce the core principles behind artificial intelligence, machine learning, and deep learning, highlighting their roles in powering modern technologies. 
• Data Skills for AI: Equip participants with essential skills to manipulate and analyze data, preparing the groundwork for its application in AI and deep learning models. 
• Basic Machine Learning Concepts: Cover the basics of machine learning, including understanding different algorithms and their applications, to build a strong foundation for advancing into deep learning. 
• Introduction to Deep Learning: Familiarize participants with the fundamentals of deep learning, including the architecture of neural networks and how they learn from data. 
• Ethical AI Deployment: Instill an understanding of the ethical considerations in AI, with a focus on developing and deploying AI and deep learning models responsibly. 
• Applying Deep Learning: Provide a basic framework for applying deep learning to real-world problems, encouraging participants to think critically about where and how to implement these models effectively. 

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. 

This intermediate and beyond level course is geared for experienced professionals aiming to apply machine learning and deep learning to solve complex business problems, including product managers, data analysts, data scientists, developers, team leads, and other technical stakeholders who want to leverage deep learning for strategic decisions. It's also suited for those who are in roles that require them to work with data, understand patterns, or make predictions, such as business analysts, software developers, and researchers. Python experience is required. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation. 

Day 1: Foundations of Data Science and AI 

Exploring Data Science & Its Role In AI 

• Discover how data science shapes the foundation of AI. 
• Data science: The modern alchemy 
• Bridging data and AI innovations 
• Pioneering technologies behind data science 
• From data chaos to strategic insights 
• Industry transformations through data science 
• Hands-on Lab 

Getting Started with AI 

• Explore the evolution, impact, and ethics of AI. 
• AI's journey from dreams to reality 
• AI's transformative role across sectors 
• Clarifying AI, ML, and DL distinctions 
• Ethical AI: Principles and importance 
• Predicting the future with AI 
• Hands-on Lab 

Machine Learning Basics  

• Delve into the core concepts and applications of ML. 
• Exploring the potential of machine learning 
• From data to decisions: ML's role 
• Algorithm overview: ML's building blocks 
• Preparing your data for ML 
• The pathway to building ML models 
• Hands-on Lab 

Day 2: Next-Level Machine Learning and Introduction to Deep Learning 

Next-Level Machine Learning  

• Enhance your ML skills with advanced techniques and algorithms. 
• Beyond basics: Advanced classification 
• Insights into clustering and regression 
• The magic of dimensionality reduction 
• Powering accuracy with ensemble methods 
• Exploring advanced ML tools 
• Hands-on Lab 

Entering the World of Deep Learning  

• Unravel the complexities and applications of deep learning. 
• Deep Learning: Beyond traditional ML 
• Architectures of neural networks 
• Deep learning in action: Case studies 
• Navigating deep learning tools and frameworks 
• Deep learning's societal impacts 
• Hands-on Lab 

Practical Deep Learning  

• Apply deep learning to real-world problems and datasets. 
• Crafting solutions with CNNs 
• Sequential data and RNNs 
• Unveiling the power of GANs 
• Deep learning optimization techniques 
• Deploying deep learning models 
• Lab: Deep Learning Application (1 hour): Implement a CNN for image recognition. 

Day 3: Specialized Applications of AI and Culmination in Deep Learning 

The Language of AI: Natural Language Processing  

• Dive into NLP to understand how AI interprets human language. 
• Core concepts of NLP 
• Techniques for text processing 
• Real-world NLP applications 
• Popular NLP tools and libraries 
• Overcoming common NLP challenges 
• Hands-on Lab 

Seeing and Hearing: AI in Image and Audio Processing  

• Explore how AI understands our world through vision and sound. 
• Introduction to computer vision 
• Basics of audio and speech recognition 
• Practical AI applications in media 
• Tools for processing images and audio 
• Future trends in visual and auditory processing 
• Hands-on Lab 

Mastering AI: Implementing and Advancing with Deep Learning 

• Bringing everything together, focusing on deep learning's pivotal role in AI. 
• Strategies for deploying AI and deep learning models 
• Integrating deep learning in real-world applications 
• Advanced deep learning techniques and trends 
• Ethical considerations and future of deep learning 
• Continuing your deep learning journey 

Bonus Project / Time Permitting 

• Hands-on guided workshop: Apply deep learning models to solve a real problem, encapsulating the skills learned throughout the course. 

Bonus Content / Time Permitting 

Bonus: Getting Started with Deep Reinforcement Learning (2 hours) 

• Explore the fundamentals of deep reinforcement learning, showing how AI systems learn from interactions to make decisions, tailored for beginners interested in the next frontier of deep learning. 

Bonus: Exploring AI Ethics and Bias Mitigation  

• Explore the ethical considerations of AI technologies and practical approaches for identifying and mitigating bias in AI models.