How to Systematically Start Learning AI from Scratch? (Including Course Recommendations)

In the wave of digital transformation, artificial intelligence (AI) has moved from the laboratory to the forefront of various industries. According to the McKinsey Global Institute, by 2030, AI could contribute an additional $13 trillion to the global economy. Given such a significant transformation, whether you are a working professional looking to transition into the AI field or a student curious about cutting-edge technology, systematically learning AI becomes crucial.

This article provides a clear learning path for beginners with no prior knowledge and recommends a series of high-quality courses, helping you avoid common pitfalls and efficiently master AI knowledge and skills.

Understanding the Characteristics and Challenges of AI Learning

Before starting, we need to recognize several important features of the AI field:

1. Interdisciplinary Nature: AI combines knowledge from mathematics, statistics, computer science, and more.
2. Rapid Iteration: New technologies and frameworks emerge constantly, requiring continuous updates to learning content.
3. Balanced Theory and Practice: Relying solely on theory or practice is insufficient to truly master AI technology.
4. Entry Barriers: While modern tools lower the application barrier, systematic understanding still requires a solid foundation.

From the experiences of many successful AI field transitioners I have encountered, common challenges for beginners include: not knowing where to start, being intimidated by complex mathematics, disconnect between theory and practice, and blindly chasing trends without systematic learning.

AI Learning Roadmap for Beginners

Based on years of teaching experience and industry trends, I have designed this step-by-step learning roadmap:

Stage 1: Foundational Knowledge (2-3 months)

In this stage, you need to build a solid foundation for future AI learning.

Mathematics

• Linear Algebra: Vectors, matrix operations, eigenvalues, and eigenvectors
• Calculus: Derivatives, partial derivatives, gradients, chain rule
• Probability and Statistics: Probability distributions, Bayes' theorem, hypothesis testing

Programming

• Python Programming: The most commonly used language in AI, requiring proficiency
• Data Structures and Algorithms: Basic data structures, algorithm complexity analysis
• Data Analysis Tools: Usage of libraries like Numpy, Pandas, and Matplotlib

Recommended Courses:

1. 《Mathematics for Machine Learning》 (Imperial College London, Coursera): Combines mathematics with machine learning applications, suitable for those with weak math foundations.
2. 《Python for Everybody》 (University of Michigan, Coursera): A beginner-friendly Python programming course with clear explanations and rich examples.
3. 《Introduction to Computer Science》 (Harvard University CS50, edX): Systematically introduces computational thinking and programming basics.

Stage 2: Machine Learning Basics (3-4 months)

After mastering the basics, begin learning core machine learning concepts and algorithms.

Key Content:

• Supervised Learning: Linear regression, logistic regression, decision trees, random forests, support vector machines
• Unsupervised Learning: Clustering algorithms, dimensionality reduction, principal component analysis
• Model Evaluation: Cross-validation, overfitting and underfitting, evaluation metrics
• Feature Engineering: Data preprocessing, feature selection, and extraction

Recommended Courses:

1. 《Machine Learning》 (Andrew Ng, Coursera): A classic course for AI beginners, explaining machine learning concepts in an accessible manner.
2. 《Introduction to Machine Learning》 (Imperial College London): A practical introduction to machine learning.
3. 《StatQuest with Josh Starmer》 (YouTube channel): Explains complex statistical and machine learning concepts in a lively and simple way.

Stage 3: Exploring Deep Learning (3-4 months)

With accumulated foundational knowledge, you can start learning advanced deep learning techniques.

Core Content:

• Neural Network Basics: Forward propagation, backpropagation, activation functions
• Deep Learning Frameworks: Usage of TensorFlow or PyTorch
• Convolutional Neural Networks (CNN): Image classification and recognition
• Recurrent Neural Networks (RNN) and LSTM: Sequence data processing
• Transformer Architecture: Foundation of natural language processing

Recommended Courses:

1. 《Deep Learning Specialization》 (Andrew Ng, Coursera): Systematically covers various aspects of deep learning.
2. 《PyTorch for Deep Learning》 (Jeremy Howard, fast.ai): A practice-oriented deep learning course.
3. 《Full Stack Deep Learning》 (UC Berkeley): Focuses on deploying deep learning models in real applications.

Stage 4: Specialization and Practical Application (3-6 months)

Based on personal interests and career plans, choose one or more AI application directions to study deeply.

Optional Directions:

• Computer Vision: Object detection, image segmentation, generative adversarial networks
• Natural Language Processing: Text classification, named entity recognition, sentiment analysis, large language models
• Reinforcement Learning: Policy gradients, Q-learning, multi-armed bandit problems
• AI System Deployment: Model optimization, cloud service integration, API design

Recommended Courses:

1. 《CS224n: Natural Language Processing with Deep Learning》 (Stanford University): A classic course in NLP.
2. 《CS231n: Convolutional Neural Networks for Visual Recognition》 (Stanford University): Deep learning applications in computer vision.
3. 《Hugging Face Courses》: Focuses on practical applications of modern NLP models.
4. 《MLOps Specialization》 (DeepLearning.AI): Learn how to deploy AI models in production environments.

Success Case Analysis

Case 1: Transitioning from Marketing to AI Engineer

Li Ming (化名) is a professional with 5 years of marketing experience who decided to transition to the AI field in 2021. He first supplemented his Python and math foundations through online courses, then systematically studied machine learning and deep learning knowledge. During his learning process, he particularly focused on applying knowledge to real problems, such as using NLP techniques to analyze user comments for sentiment analysis, providing data support for product improvements. After 18 months of learning and 3 practical projects, he successfully transitioned into an AI engineer position at a tech company.

Key Success Factors: Systematic learning plan, continuous practice, building a portfolio, and deep focus on a specific area (NLP).

Case 2: A University Student's AI Learning Journey

Zhang Hua (化名) is a sophomore majoring in computer science who developed a strong interest in AI. During the summer vacation, he completed Andrew Ng's machine learning and deep learning courses and participated in a school research project on the application of computer vision in medical image analysis. By contributing to open-source projects on GitHub, he secured an internship at a top AI lab during his senior year and was eventually admitted to a graduate program for further study.

Key Success Factors: Solid theoretical foundation, research project practice, participation in open-source communities, and mentor guidance.

Practical Learning Suggestions

1. Build a Knowledge Map: Use mind maps to organize the AI knowledge system, clarifying learning priorities and order.
2. Project-Driven Learning: Set small but complete project goals, such as image classifiers or sentiment analysis systems, to reinforce theoretical knowledge through practice.
3. Join AI Communities: Participate in Kaggle competitions, GitHub open-source projects, or AI research paper reading groups to exchange ideas with peers.
4. Create a Personal Portfolio: Document and upload projects to GitHub or personal blogs to showcase skills and thought processes.
5. Stay Updated on Trends: Subscribe to AI conference papers (e.g., NeurIPS, ICML, CVPR) and blogs (e.g., Google AI Blog, OpenAI Blog) to stay informed about the latest advancements.

Learning Resources Overview

Below is a table of learning resources recommended for different stages and budgets:

Conclusion

Learning AI is a marathon, not a sprint. As Yann LeCun, Facebook's chief AI scientist, said, "AI is not magic; it's a multidisciplinary field composed of mathematics, statistics, and computer science." Starting AI learning from scratch may seem daunting, but with a suitable systematic learning path, combined with continuous practice and reflection, you will surely find your place in this field full of opportunities.

I hope the provided learning roadmap and resource recommendations can help you efficiently embark on your AI learning journey. Whether you aim to transition in your career or pursue academic interest, remember that learning is about solving problems and creating value. Applying AI technology to real-world problems, on top of mastering core concepts, is the only way to truly appreciate the beauty of technology.

"Learning without thinking leads to confusion; thinking without learning leads to danger." On this AI journey, may you have the patience for systematic learning and the courage for independent thinking.