AI learning:

The first stage: Introduction to AI basics (3-6 months)

1. Basic mathematics (1-2 months)

Linear Algebra: Learn the basic operations of vectors and matrices, including addition, multiplication, dot product, and cross product.

Master the inverse operation, determinant, eigenvalues ​​and eigenvectors of matrices. Understand the concepts of linear independence, basis, and dimensionality.

Source: MIT OpenCourseWare linear algebra course.

Probability theory and statistics: Understand random variables and probability distributions (including binomial distribution and normal distribution).

Learn expectations, variances, covariances, and correlation coefficients.

Master hypothesis testing, confidence intervals, and Bayesian statistics.

Source: Khan Academy Probability Theory and Statistics course.

Calculus: Learn derivatives, integrals, partial derivatives, and multivariable calculus.

Understand the application of calculus to optimization problems.

Source: Paul's Online Math Notes.

Optimization methods: Learn optimization algorithms such as gradient descent, Newton's method, and conjugate gradient method.

Understand the role of optimization in machine learning.

Source: Coursera Optimization Course.

2. Programming basics (1-2 months)

Python language: Learn the basic syntax of Python, including variables, data types, and control flow (if statements, for loops, while loops).

Master function definition, module import, and exception handling.

Learn object-oriented programming in Python, including class definition, inheritance, and polymorphism.

Source: Python official documentation.

Scientific Computing Library: Learn NumPy for efficient numerical calculations, including array operations and linear algebra operations.

Use Pandas for data cleaning and analysis, including data frame operations and time series processing. Master Matplotlib for data visualization, including line graphs, scatter plots, and histograms. Resources: NumPy official documentation, Pandas official documentation, Matplotlib official documentation.

3. Basic concepts of AI (1 month)

AI History and Applications: Understand the development history of AI, including important milestones and figures. Explore the applications of AI in different industries, such as medical care, finance, and autonomous driving.

Source: Wikipedia entry on artificial intelligence.

Basics of machine learning: Understand the differences between supervised learning, unsupervised learning, and reinforcement learning. Learn basic machine learning algorithms and evaluation metrics, such as precision, recall, and F1 score. Source: Andrew Ng's "Machine Learning" course.

Phase 2: Core Technology (6-12 months)

4. Machine Learning (3-6 months)

Classic algorithms: Learn basic algorithms such as linear regression, logistic regression, decision trees, and support vector machines.

Understand the process of building, training, and evaluating models, including cross-validation and hyperparameter tuning.

Source: Machine Learning in Action.

Model evaluation and selection: Master cross-validation, error analysis, model selection and other methods. Learn concepts like regularization, bias-variance trade-off, and more. Source: An Introduction to Statistical Learning.

Feature Engineering: Learn methods for feature selection, feature extraction, and data preprocessing. Understand the role of feature engineering in improving model performance. Source: Feature Engineering for Machine Learning.

5. Deep learning (3-6 months)

Neural Network Basics: Understand the basic concepts of neural networks, including forward propagation and back propagation. Learn key concepts such as activation functions and loss functions. Source: Deep Learning Book.

Convolutional Neural Network (CNN): Learn the application of CNN in image recognition. Understand the functions of convolutional layers and pooling layers. Source: Stanford University’s CS231n course.

Recurrent Neural Network (RNN): Learn the application of RNN in processing sequence data. Understand variants such as LSTM and GRU. Resources: DeepLearning.AI deep learning special course.

The third stage: practice and application (6-12 months)

6. AI industry application (3-6 months)

Computer Vision: Learn tasks such as image classification, target detection, and image segmentation. Master the use of computer vision libraries such as OpenCV. Source: OpenCV official documentation.

Natural language processing: learning text classification, sentiment analysis, machine translation and other tasks. Master the use of NLP toolkits such as NLTK and SpaCy. Source: Stanford University’s CS224n course.

Recommendation system: Learn recommendation system algorithms such as collaborative filtering and content recommendation. Understand the application of recommendation systems in e-commerce and social media. Resource: Recommendation system in practice.

7. Actual project practice (3-6 months)

Project Selection: Choose a specific project, such as medical image analysis, chatbot, etc. Clarify project goals and expected outcomes. Source: Kaggle competition.

Project implementation: Use frameworks such as TensorFlow or PyTorch to implement the project. Perform data collection, preprocessing, model training and evaluation. Resources: TensorFlow official documentation, PyTorch official documentation. Project optimization: adjust model parameters, use grid search or random search. Apply techniques such as regularization and dropout to reduce overfitting. Resources: Deep learning parameter adjustment course.

The fourth stage: cutting-edge technology and future trends (continuous learning)

8. Paper reading and cutting-edge technology (continuous)

Paper reading: Regularly read top conference papers in the AI ​​field. Learn how to extract key information and algorithms from papers. Source: arXiv.org.

Cutting-edge technology: Learn about the latest AI technologies, such as reinforcement learning, generative adversarial networks (GANs), etc. Try implementing and applying these techniques. Resources: NeurIPS conference papers, ICML conference papers.

9. Community communication and continuous learning (continuous)

Participate in the community: Join AI-related forums and communities, such as Reddit and GitHub. Participate in open source projects and contribute code. Sources: Reddit Machine Learning Community, GitHub.

Continuous learning: With the continuous development of AI technology, continue to learn new knowledge and new skills. Attend regular online courses, seminars and workshops. Sources: Coursera, edX.