- "Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals."
Overview of what Artificial Intelligence is, its branches, history, and applications.
Machine Learning: It is a subfield of Artificial Intelligence that focuses on the development of algorithms and systems that can recognize patterns and learn from data.
Deep Learning: It is a subset of machine learning that involves the use of artificial neural networks to model complex patterns and relationships in large datasets.
Natural Language Processing: It is a subfield of Artificial Intelligence that deals with teaching machines to understand and interpret human language.
Computer Vision: It is a subset of Artificial Intelligence that deals with teaching machines to interpret and understand the visual world.
Reinforcement Learning: It is a subset of machine learning that focuses on training agents to make decisions by rewarding them for desirable behavior.
Neural Networks: They are a set of algorithms that are inspired by the workings of the human brain and are used for pattern recognition and prediction.
Decision Trees: It is a simple yet effective machine learning algorithm that is used for classification and regression problems.
Support Vector Machines: It is a machine learning algorithm that is used for binary classification problems.
Clustering: It is a technique that is used for grouping similar data points together in a dataset.
Regression Analysis: It is a statistical technique that is used for predicting the value of a dependent variable based on one or more independent variables.
Artificial Intelligence Ethics: It is a field of study that deals with the ethical considerations surrounding the development and use of artificial intelligence.
Unsupervised Learning: It is a type of machine learning that involves training algorithms on unlabeled data and allowing the algorithm to discover patterns and relationships on its own.
Supervised Learning: It is a type of machine learning that involves training algorithms on labeled data to predict or classify new data.
Semi-Supervised Learning: It is a type of machine learning that involves training algorithms on a combination of labeled and unlabeled data, and is used when labeling data is expensive or time-consuming.
Model Selection: It is the process of selecting the best model for a given problem based on its accuracy, complexity, and other evaluation metrics.
Data preprocessing: It is the process of cleaning and transforming raw data into a usable format that can be used for machine learning.
Bias and Fairness in AI: It is the issue of discriminatory or unethical use of AI algorithms and efforts to mitigate it.
Deployment and model serving: It is the process of taking a trained machine learning model and making it available for use by integrating it into a production environment.
Optimization: It is the process of fine-tuning algorithm parameters to improve their performance.
Python for Machine Learning: Python is a popular language for developing machine learning algorithms and comes with many libraries that make it easier to develop and deploy machine learning models.
International Open Academy's Artificial Intelligence with Python: A course offering a broad introduction to AI, exploring concepts such as neural networks, deep learning, and computer vision, with an emphasis on Python programming.
Google's Machine Learning Crash Course (MLCC): A fast-paced, highly interactive course that walks students through the fundamentals of machine learning, including regression, classification, and clustering.
IBM's Introduction to AI: Provides an overview of AI technologies, including machine learning, deep learning, and natural language processing. It covers key concepts and applications with business examples.
Coursera's Machine Learning: An academic course that covers the essentials of machine learning, including supervised and unsupervised learning, linear regression, and artificial neural networks, among other concepts.
Stanford's Artificial Intelligence with Python: Focuses on deep learning and machine learning, covering concepts such as backpropagation, convolutional neural networks, and image recognition.
Udemy's Deep Learning with TensorFlow: An introduction to deep learning, focusing on using Google's TensorFlow framework to build machine learning models for speech recognition and image classification.
MIT's Artificial Intelligence: Implications for Business Strategy: A free course that explores the impact of AI on businesses and offers insights into how companies can leverage this emerging technology for competitive advantage.
Berkeley's Introduction to Artificial Intelligence: Provides an overview of AI, covering a range of topics such as probability, decision theory, and state-space search.
Microsoft's AI Business School: Explores how companies can leverage AI to drive growth and innovation, covering topics such as strategy, ethics, and risk management.
Harvard's Introduction to Artificial Intelligence with Python: Covers the basics of AI and machine learning, and introduces students to common machine learning tools and libraries.
- "It is also the field of study in computer science that develops and studies intelligent machines."
- "AI technology is widely used throughout industry, government, and science."
- "Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go)."
- "Artificial intelligence was founded as an academic discipline in 1956."
- "After 2012, when deep learning surpassed all previous AI techniques, there was a vast increase in funding and interest."
- "The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics."
- "General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals."
- "AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics."
- "AI also draws upon psychology, linguistics, philosophy, neuroscience, and many other fields."
- "Understanding human speech (such as Siri and Alexa)."
- "When deep learning surpassed all previous AI techniques."
- "Generative or creative tools (ChatGPT and AI art)."
- "Self-driving cars (e.g., Waymo)."
- "Advanced web search engines (e.g., Google Search)."
- "The various sub-fields of AI research are centered around particular goals and the use of particular tools."
- "It is the intelligence of machines or software."
- "AI technology is widely used throughout industry, government, and science."
- "The field went through multiple cycles of optimism followed by disappointment and loss of funding."
- "AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics."