Course Prerequisites
Audience
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.
Pre-Requisites
To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:
- Attendees should have some familiarity with Enterprise IT as well as a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying data science, AI and machine learning.
- Python programming is required, as the labs revolve around leveraging Python. Basic skills in handling and manipulating data using Python libraries such as NumPy and Pandas would be advantageous.
- Familiarity with concepts such as variables, functions, control flow, and data structures will ensure a smooth learning experience.
- While the course will introduce deep learning from scratch, having a grasp of basic machine learning concepts will be beneficial.
- Some understanding of algebra and basic calculus will be helpful in comprehending the mathematical components of deep learning.
Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:
⦁ TTPS4873 Fast Track to Python in Data Science (3 days)
Next Steps / Follow-on Courses: We offer a wide variety of follow-on courses and learning paths for Python, Big Data, Machine Learning, Generative AI, AI for Business, GPT, Applied AI, Azure OpenAI, Google BARD, AI for developers, testers, data analytics, deep learning, programming, intelligent automation and many other related topics. Please see our catalog for the current Python, Data Science, AI & Machine Learning Courses, Learning Journeys & Skills Roadmaps, list courses and programs.
Course Agenda
Course Topics / Agenda
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.
