Introduction to Artificial Intelligence & Machine Learning
This beginner level course introduces foundational concepts of artificial intelligence and machine learning, focusing on basic principles and simple applications.
Learning Objectives
Understand the basic concepts and techniques of AI and machine learning.
Chapters
Engage in basic hands-on projects to apply AI concepts.
Goal:Apply learned AI concepts in simple projects.
Presenting your project to peers for critique.
Setting up the environment for AI projects.
Basics of using Python programming in AI projects.
How to load and explore datasets for AI projects.
Creating a basic AI model using Python.
Testing and validating the model's performance.
Introduction to basic model tuning techniques.
Basics of deploying an AI model for use.
How to document your project for future reference.
Reviewing project outcomes and gathering feedback.
Learning the basics of deploying AI models and evaluating their performance.
Goal:Understand the fundamentals of deploying and evaluating AI models.
Basics of deploying AI models in real-world scenarios.
Exploring different environments where models can be deployed.
Introduction to deploying models on cloud platforms.
Learning how to evaluate the performance of AI models.
Basics of improving model accuracy and reliability.
Introduction to monitoring the performance of deployed models.
Basics of troubleshooting deployment problems.
Introduction to scaling AI models for higher demand.
Discussing security considerations in deploying AI models.
Exploring real-world examples of successful AI model deployment.
An introduction to the concept and history of artificial intelligence.
Goal:Gain a basic understanding of what AI is and its historical context.
Defining artificial intelligence and its significance.
An overview of the development of AI over the decades.
Introduction to different types of AI, including narrow AI and general AI.
Exploring how AI is used in daily applications.
Understanding the current limitations of AI technologies.
Discussing the ethical implications of AI deployment.
Speculating on the future developments in AI.
Debunking common myths about AI.
Learning basic AI terms and jargon.
Differentiating between AI and machine learning.
Exploring the foundational concepts of machine learning and its applications.
Goal:Understand the basics of machine learning and its uses.
Defining machine learning and its importance.
Introduction to supervised, unsupervised, and reinforcement learning.
Understanding the concept of supervised learning with examples.
Exploring unsupervised learning techniques and applications.
A basic introduction to reinforcement learning and its applications.
Overview of common machine learning algorithms.
Exploring real-world applications of machine learning.
Understanding the common challenges faced in machine learning.
Introduction to tools and libraries used in machine learning.
Learning key terms used in the machine learning field.
Understanding the role and importance of data in AI and machine learning.
Goal:Learn the basics of data usage in AI/ML.
Exploring why data is crucial for AI systems.
Introduction to different types of data used in AI/ML.
Understanding methods of data collection for AI/ML.
The process of preparing data for AI/ML models.
Exploring techniques to clean and organize data.
Introduction to visualizing data for better understanding.
Discussing the ethical considerations in data handling.
Exploring storage solutions for managing large datasets.
Understanding the importance of data labeling and annotation.
How data influences decision-making processes in AI/ML.
Exploring the basic concepts and structure of neural networks.
Goal:Understand the basic structure and function of neural networks.
Defining neural networks and their purpose in AI.
Exploring the architecture of neural networks.
Understanding the role of neurons and activation functions.
Introduction to how data moves through a neural network.
Learning how neural networks learn through backpropagation.
Exploring different types of neural networks.
Understanding how neural networks are trained.
Exploring common applications of neural networks in AI.
Discussing challenges faced when working with neural networks.
Introduction to tools used for developing neural networks.
An introduction to generative models and their applications in AI.
Goal:Understand the basics of generative models.
Defining generative models and their role in AI.
Exploring different types of generative models.
Introduction to GANs and their functionality.
Exploring VAEs and their applications in AI.
Discussing real-world applications of generative models.
Understanding challenges faced in developing generative models.
Exploring the use of generative models in creative fields.
Discussing ethical implications of generative models.
Introduction to tools used for developing generative models.
Speculating on future advancements in generative modeling.
Exploring how AI is applied in various industries and sectors.
Goal:Understand the practical applications of AI in the real world.
Exploring the use of AI in the healthcare sector.
Discussing applications of AI in the financial industry.
Exploring how AI is used in the retail industry.
Understanding AI's impact on transportation systems.
Exploring AI's role in transforming educational practices.
Discussing AI's use in entertainment and media.
Exploring AI's impact on agriculture and farming.
Understanding AI's role in manufacturing processes.
Discussing AI's use in improving customer service experiences.
Exploring AI's applications in solving environmental issues.
Realtime audio conversation for interactive session.
Interactive realtime chat session.
Live whiteboard explanation and collaboration.
Real-time wide variety of examples.
Continuous assessment and feedback.
Progress monitoring and record progress journey.
Broadcast session with larger audience for free.
Attend audience queries and provide responses.