Math skills:
Probability theory and statistics: Understand the basic concepts and methods of probability theory and statistics, such as probability, expectation, variance and covariance. It is very important for understanding the uncertainty evaluation and model selection in machine learning algorithms.
Linear algebra: master the basic knowledge of linear algebra, such as matrix operation, vector operation, eigenvalue and so on. It is very important to understand the matrix operation and data representation in machine learning algorithm.
Calculus: Understand the basic concepts and methods of calculus, such as functions, derivatives and integrals. It is very helpful to understand the optimization of loss function and model generalization in machine learning algorithm.
Programming skills:
Python programming language: master the basic syntax, data structure, functions and modules of Python programming language, and be familiar with commonly used Python libraries and frameworks, such as NumPy, Pandas, Matplotlib, etc.
Programming habits and skills: Understand common programming problems and solutions, such as error handling, code debugging, performance optimization, etc. , cultivate good programming habits and skills.
For zero-based learners, here are some suggestions:
Start with the basics: first master the basic grammar and common libraries of Python, and understand the basic methods of data analysis and processing. Recommend some entry-level Python tutorials and books, such as Python Programming: From Introduction to Practice, Smooth Python, etc.
Learning mathematical foundation: before starting machine learning, fill in the required mathematical foundation. You can learn basic knowledge such as probability theory, statistics, linear algebra and calculus through some online courses, teaching materials or self-study resources.
Learn the basic knowledge of machine learning: Understand the basic concepts, algorithms and applications of machine learning, and you can learn some classic machine learning algorithms, such as classification, clustering and regression. Recommend some entry-level machine learning tutorials and books, such as Machine Learning Practice and Introduction to Machine Learning.
Practical projects: By consolidating and applying the learned knowledge through practical projects, some practical projects can be selected for practice, such as using machine learning algorithms for prediction and classification. Practical projects can help you better understand the role and value of machine learning in practical applications.
Constant learning and practice: Python machine learning is an ever-developing field, so we should constantly pay attention to and learn new technologies and knowledge. At the same time, we should keep practicing and learning to improve our Python programming ability and machine learning ability.
In a word, Python learning machine learning requires certain mathematical and programming skills, but zero-based learning can also be introduced and gradually deepened. By completing the basic knowledge and practical projects, you can gradually improve your Python programming ability and machine learning ability and become a skilled Python machine learning engineer.