Examples
of introductory AI resources, including online courses, books,
tutorials, and tools, are designed to help beginners gain a basic
understanding of AI concepts and techniques. Here are some popular
introductory AI resources:
1.
Online Courses:
* Codecademy: "Learn Machine
Learning with Python"
* Coursera: "AI for Everyone" by deeplearning.ai (Andrew Ng)
* Coursera: "Applied Data Science with Python" by University of
Michigan
* Coursera: "Artificial Intelligence for Beginners" by
Ventsislav Mladenov
* Coursera: "Intro to TensorFlow for Deep Learning" by Google
Cloud
* Coursera: "Introduction to Artificial Intelligence (AI)" by IBM
* Coursera: "Machine Learning" by Stanford University (Andrew Ng)
* DataCamp: "Introduction to Deep Learning in Python"
* edX: "CS50's Introduction to Artificial Intelligence with Python"
by Harvard University
* edX: "Fundamentals of TinyML: Embedded Machine Learning for
Beginners" by Harvard University
* Fast.ai: "Practical Deep Learning for Coders"
* FutureLearn: "Artificial Intelligence: An Introduction for
Absolute Beginners" by Coventry University
* LinkedIn Learning: "Artificial Intelligence Foundations: Neural
Networks" by Doug Rose
* MIT OpenCourseWare: "Introduction to Deep Learning"
* Udacity: "Intro to Artificial Intelligence"
* Udemy: "Artificial Intelligence A-Z: Learn How to Build an AI"
by Hadelin de Ponteves and Kirill Eremenko
2.
Books:
* "AI: A Modern Approach"
by Stuart Russell and Peter Norvig
* "Artificial Intelligence with Python" by Prateek Joshi
* "Artificial Intelligence: A Guide to Intelligent Systems" by
Michael Negnevitsky
* "Artificial Intelligence: Foundations of Computational Agents"
by David L. Poole and Alan K. Mackworth
* "Data Science for Business" by Foster Provost and Tom Fawcett
* "Data Science from Scratch" by Joel Grus
* "Deep Learning for Coders with Fastai and PyTorch" by Jeremy
Howard and Sylvain Gugger
* "Deep Learning with Python" by François Chollet
* "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron
Courville
* "Grokking Artificial Intelligence Algorithms" by Rishal Hurbans
* "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
by Aurélien Géron
* "Machine Learning for Dummies" by John Paul Mueller and Luca
Massaron
* "Make Your Own Neural Network" by Tariq Rashid
* "Pattern Recognition and Machine Learning" by Christopher M.
Bishop
* "Python Machine Learning" by Sebastian Raschka and Vahid
Mirjalili
* "Reinforcement Learning: An Introduction" by Richard S. Sutton
and Andrew G. Barto
3.
Tutorials and Guides:
* "A Course in Machine
Learning" by Hal Daumé III (free online book)
* "AI for Everyone" by AI4ALL (free online AI curriculum)
* "AI with Python" by Tutorialspoint (free online tutorial)
* "Introduction to AI" by OpenAI
* "Introduction to Artificial Intelligence" by Columbia
University (free online course material)
* "Learn AI from Scratch" by Avinash Sagar (free e-book)
* "Machine Learning is Fun!" by Adam Geitgey (Medium blog series)
* "Neural Networks and Deep Learning" by Michael Nielsen (free
online book)
* "The Hundred-Page Machine Learning Book" by Andriy Burkov
(free online book)
* EliteDataScience's "Machine Learning for Beginners: An
Introduction to Neural Networks"
* Google's Machine Learning Crash Course
* Kaggle's "Learn" platform, which offers
interactive lessons
on machine learning, deep learning, and data science
* Machine Learning Mastery blog by Jason Brownlee
* Sentdex's Python Programming for Machine Learning and Artificial
Intelligence (YouTube tutorial series)
* Siraj Raval's AI tutorials on YouTube
* StatQuest with Josh Starmer (YouTube
tutorial series on
machine learning and statistics)
4.
Tools and Libraries:
* Gensim:
An open-source library for unsupervised topic modeling and natural
language processing in Python
* Hugging
Face Transformers: A library for
state-of-the-art natural language processing models based on
transformers, such as BERT and GPT
*
Jupyter Notebook: An open-source web application for
creating and sharing live code, equations, visualizations, and
narrative text
* Keras:
A high-level neural networks API, written in
Python and capable of running on top of TensorFlow, Microsoft Cognitive
Toolkit, Theano, or PlaidML
* LightGBM:
A gradient boosting framework that uses
tree-based learning algorithms, developed by Microsoft
* MLflow:
An open-source platform for managing the
complete machine learning lifecycle
* NLTK
(Natural Language Toolkit): A library for building
Python programs to work with human language data
* OpenCV:
An open-source computer vision and machine
learning library
* Orange:
An open-source data mining, machine learning,
and data visualization toolkit
* Pandas:
A popular open-source library for data
manipulation and analysis in Python
* PyTorch:
An open-source deep learning library developed
by Facebook
* Scikit-learn:
A popular open-source library for machine
learning in Python
* SpaCy:
A library for advanced natural language
processing in Python
* TensorFlow:
An open-source machine learning library
developed by Google
* Weka:
A suite of machine learning software, implemented
in Java and developed by the University of Waikato
* XGBoost:
An optimized distributed gradient boosting
library designed to be efficient, flexible, and portable
These
resources cover a range of introductory AI topics, including machine
learning, deep learning, natural language processing, and computer
vision. By engaging with these materials, beginners can gain
foundational knowledge and skills in AI, setting the stage for more
advanced study and practical applications in the future.