I want to learn artificial intelligence and machine learning. Where can I start?

Learn AI and ML

Starting your journey in artificial intelligence (AI) and machine learning (ML) can be exciting and rewarding! Here’s a structured approach to help you get started:

1. Understand the Basics

Mathematics: Brush up on essential math concepts, including:
Linear Algebra (vectors, matrices)
Calculus (derivatives, integrals)
Probability and Statistics (distributions, Bayes' theorem)
Programming: Learn a programming language commonly used in AI/ML:
Python is the most popular choice due to its simplicity and extensive libraries.
2. Online Courses

Introductory Courses:
Coursera:
“Machine Learning” by Andrew Ng (Stanford University)
“AI For Everyone” by Andrew Ng
edX:
“CS50's Introduction to Artificial Intelligence with Python” (Harvard University)
Udacity:
“Intro to Machine Learning Nanodegree”
Specialized Courses:
“Deep Learning Specialization” by Andrew Ng (Coursera)
“Applied Data Science with Python” (University of Michigan on Coursera)
3. Books

“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron: A practical approach to ML using Python libraries.
“Pattern Recognition and Machine Learning” by Christopher Bishop: A more theoretical perspective.
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A comprehensive resource on deep learning.
4. Practice with Projects

Start small projects to apply what you’ve learned:
Kaggle: Participate in competitions and explore datasets.
Build simple applications, such as:
Image classification
Sentiment analysis
Recommender systems
5. Join Communities

Engage with online communities to learn from others:
Reddit: Subreddits like r/MachineLearning and r/learnmachinelearning.
Stack Overflow: Ask questions and find answers.
GitHub: Explore open-source projects and contribute.
6. Stay Updated

Follow AI/ML news and research:
Subscribe to newsletters (e.g., “Import AI”).
Follow influential researchers and organizations on social media.
7. Advanced Topics

Once you're comfortable with the basics, explore advanced topics such as:
Reinforcement Learning
Natural Language Processing (NLP)
Computer Vision
Conclusion

Start with foundational knowledge and progressively dive into more complex topics. Consistent practice and engagement with the community will enhance your learning experience. Good luck on your journey into AI and ML!