AI Learning Path: From Zero to Practical Mastery
A systematic learning path tailored for AI beginners, helping you fully understand artificial intelligence, master core concepts, programming skills, and cutting-edge applications, and embark on your AI exploration journey.
1Stage 1: Mathematics and Programming Fundamentals
Deeply understand the cornerstone of artificial intelligence. This stage covers Python programming basics (including data types, control flow, functions, object-oriented programming), the use of common scientific computing libraries (NumPy, Pandas), and data visualization libraries (Matplotlib, Seaborn). At the same time, master the mathematical foundations required for AI, such as linear algebra (vectors, matrix operations), calculus (derivatives, gradients), and probability and statistics (probability distributions, hypothesis testing), which are key to understanding machine learning algorithms.
2Stage 2: Classic Machine Learning
Learn the core algorithms and models of traditional machine learning. You will master the principles and applications of supervised learning (linear regression, logistic regression, support vector machine SVM, decision tree, random forest, gradient boosting) and unsupervised learning (K-Means clustering, PCA dimensionality reduction). Understand model training, evaluation metrics (such as accuracy, recall, F1 score, RMSE), and the diagnosis and handling methods of overfitting and underfitting. Practice using the Scikit-learn library.
3Stage 3: Data and Feature Engineering
Explore the importance of data preprocessing and feature optimization. This stage will guide you through data cleaning (handling missing values, outliers), data transformation (standardization, normalization), feature selection (filter methods, wrapper methods, embedded methods), and feature extraction (such as TF-IDF for text, SIFT/HOG for images), and how to use these techniques to improve model performance. Master the data analysis process and lay a solid foundation for building efficient AI models.
4Stage 4: Deep Learning and Neural Networks
Step into the forefront of artificial intelligence. Learn the basic structure of neural networks (perceptron, multilayer perceptron) and the backpropagation algorithm. Deeply understand the application of convolutional neural networks (CNN) in image recognition, and the principles of recurrent neural networks (RNN) and their variants (LSTM, GRU) in sequence data processing (such as natural language processing). Master the construction and use of mainstream deep learning frameworks such as TensorFlow or PyTorch, and build and train your own deep learning models.
5Stage 5: Explore AI Application Areas
Apply the knowledge you have learned to real-world scenarios. This stage will take you to understand the wide application of AI in various industries, including computer vision (image classification, object detection, image generation), natural language processing (text classification, sentiment analysis, machine translation, question answering systems), recommendation systems, speech recognition, reinforcement learning, robotics, etc. Through case studies, broaden your understanding of the potential of AI.
6Stage 6: AI Project Practice and Deployment
Transform theoretical knowledge into practical project experience. Choose an AI application area of interest, and independently complete an end-to-end AI project from data collection, model selection, training optimization to final deployment. Learn how to use GitHub for code management, how to deploy trained models to cloud platforms or local environments, and understand model interpretability and ethical considerations. Improve problem-solving skills through real projects.