7 Steps to Learn AI From Scratch in 2024: Best free Resources

Artificial Intelligence (AI) is revolutionizing every industry, from healthcare to finance to entertainment. As we move into 2024, there has never been a better time to dive into AI and equip yourself with the skills to be part of this exciting field. This article covers a step-by-step guide to help you learn AI from scratch, using the best free resources available.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. These intelligent systems can perform tasks such as recognizing speech, making decisions, and translating languages. AI can be categorized into two types:

1. Narrow AI (Weak AI): AI systems designed and trained for a specific task, such as voice assistants (Siri, Alexa) or recommendation systems (Netflix, Amazon).
2. General AI (Strong AI): Hypothetical AI systems that possess the ability to perform any intellectual task that a human can do, with the same level of competence. This type of AI remains theoretical and is a subject of ongoing research.

Learning AI is now positions you at the forefront of technological advancements, providing skills that are high demand across multiple industries, ensuring job security and career growth. So below mentioned are the steps that you can follow to Learn AI From Scratch in 2024.

Before diving into the technical aspects, it’s crucial to grasp what AI is and its potential applications. Start with these resources:

Recommended Free Resources:

• Coursera: AI For Everyone by Andrew Ng: This is a non-technical course that provides a broad overview of AI, its implications, and applications.
• Elements of AI: A series of free online courses created by the University of Helsinki to demystify AI.

Recommended Books:

• “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell: This book offers a comprehensive and accessible introduction to AI, covering its history, concepts, and implications.
• “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark: Explores the future of AI and its impact on society, providing a thought-provoking read for beginners.

Machine Learning Basic to Advanced – Self Paced

Python is the most popular language for AI development due to its simplicity and extensive libraries. Begin with the basics and gradually move to more complex topics.

Recommended Free Resources:

• Codecademy: Learn Python 3: An interactive course perfect for beginners.
• Google’s Python Class: Provides written materials, lecture videos, and lots of code exercises to practice Python coding.

Recommended Books:

• “Automate the Boring Stuff with Python” by Al Sweigart: This book is great for beginners and covers practical programming skills with Python.
• “Python Crash Course” by Eric Matthes: A hands-on, project-based introduction to Python for beginners.

Best Way To Start Learning Python – A Complete Roadmap

A solid understanding of mathematics is crucial for AI. Focus on linear algebra, calculus, probability, and statistics.

Recommended Free Resources:

• Khan Academy: Offers comprehensive courses in linear algebra, calculus, and statistics.
• 3Blue1Brown (YouTube Channel): Provides visually intuitive explanations of complex math concepts.

Recommended Books:

• “Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: This book covers essential mathematical concepts needed for machine learning.
• “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: A classic book on statistical learning theory and methods.

Machine Learning (ML) is a subset of AI that focuses on building systems that learn from data. Start with the basics and gradually move to more advanced topics.

Recommended Free Resources:

• Coursera: Machine Learning by Andrew Ng: This is a classic and highly recommended course that covers all fundamental aspects of ML.
• Google AI’s Machine Learning Crash Course: A practical introduction to ML with exercises and interactive visualizations.

Recommended Books:

• “Pattern Recognition and Machine Learning” by Christopher M. Bishop: An excellent textbook for understanding the theoretical aspects of ML.
• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron: A practical guide with examples and code snippets to help you implement ML algorithms.

100 Days of Machine Learning – A Complete Guide For Beginners

Deep Learning is a subset of ML that uses neural networks with many layers. It is the driving force behind most of the recent advances in AI.

Recommended Free Resources:

• Deep Learning Specialization by Andrew Ng on Coursera: Although it has a paid certification, the course materials are accessible for free.
• Fast.ai: Offers practical deep learning courses with a top-down approach, emphasizing coding first.

Recommended Books:

• “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Known as the “Bible” of deep learning, this book covers deep learning theory and practice in great detail.
• “Deep Learning for Coders with Fastai and PyTorch” by Jeremy Howard and Sylvain Gugger: A hands-on guide to deep learning with practical applications using the Fastai library and PyTorch.

Complete Deep Learning Tutorial

Applying what you’ve learned through hands-on projects is crucial. Start with small projects and gradually tackle more complex problems.

Recommended Free Resources:

• Kaggle: A platform with datasets and competitions to practice and hone your AI skills.
• Google Colab: Provides a free environment to write and execute Python code in the cloud, making it easy to collaborate and share your work.

Recommended Books:

• “Data Science Projects with Python” by Stephen Klosterman: This book offers practical project-based learning to apply your data science and machine learning skills.
• “Python Data Science Handbook” by Jake VanderPlas: Comprehensive guide on using Python for data science, including many practical examples and projects.

AI is a rapidly evolving field. Staying updated with the latest developments and being part of the AI community can provide invaluable support and opportunities.

Recommended Free Resources:

• ArXiv.org: A repository of research papers where you can read about the latest advancements in AI.
• Reddit (r/MachineLearning): A community where you can discuss topics, ask questions, and share knowledge with other AI enthusiasts.
• Towards Data Science (Medium): Offers articles, tutorials, and resources written by AI practitioners.

Recommended Books:

• “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig: A comprehensive textbook covering a wide range of AI topics.
• “Deep Learning with Python” by François Chollet: Written by the creator of Keras, this book provides a practical introduction to deep learning with Python.

Why Should You Learn Artificial Intelligence Right Now?

Learning AI now positions you at the forefront of technological advancements, providing skills that are in high demand across multiple industries, ensuring job security and career growth.

Is AI a High-Paying Job?

Yes, AI-related roles are among the highest-paying jobs in the tech industry due to the specialized skills required and the increasing demand for AI professionals.

Should I Learn AI in 2024?

Yes, learning AI in 2024 is highly beneficial due to the growing demand for AI expertise, the expanding applications of AI in various industries, and the potential for high-paying and fulfilling career opportunities.

How to Learn AI Step by Step?

Begin with foundational knowledge of AI, learn Python programming, study essential math, dive into machine learning concepts, progress to deep learning, and apply your skills through hands-on projects and real-world applications.

How Long Does it Take to Learn AI?

The time it takes to learn AI can vary, but with consistent effort, you can gain a solid understanding of AI fundamentals and start building projects within 6 months to a year.