How to get started with machine learning from scratch

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How to get started with machine learning from scratch [Copy link]

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Even if you are starting to learn machine learning from scratch, you can get started step by step by following these steps:

Understand the basic concepts of machine learning :
- Learn the basic principles of machine learning, including different types of learning methods such as supervised learning, unsupervised learning, and reinforcement learning.
- Understand some basic terms in machine learning, such as features, labels, models, training sets, test sets, etc.
Learn the basics of mathematics and statistics :
- Review basic linear algebra, calculus, and probability and statistics knowledge, which are the basis for understanding machine learning algorithms and principles.
- Learn how to use mathematical and statistical tools to analyze data, understand models, and evaluate their performance.
Master programming skills :
- Master a programming language, such as Python, and common scientific computing libraries, such as NumPy, Pandas, and Matplotlib.
- Learn how to use Python to implement machine learning algorithms, process data, visualize results, and more.
Learn machine learning algorithms :
- Understand common machine learning algorithms such as linear regression, logistic regression, decision trees, support vector machines, neural networks, etc.
- Learn the principles, advantages and disadvantages, applicable scenarios and implementation methods of each algorithm.
Take an online course or tutorial :
- Sign up for some free or paid online courses, such as "Machine Learning" on Coursera, "Machine Learning Engineer Nanodegree" on Udacity, etc. These courses are usually taught by industry experts or academic researchers, and the content is systematic and comprehensive.
- Learn to use some machine learning frameworks and tools, such as Scikit-learn, TensorFlow, PyTorch, etc.
Completed practical projects :
- Choose some simple machine learning projects like house price prediction, handwritten digit recognition, spam classification, etc. and try to solve them using machine learning algorithms.
- Through hands-on practice, you can deepen your understanding of machine learning principles and algorithms, and improve your programming and debugging skills.
Continuous learning and exploration :
- Read classic books and papers in the field of machine learning in depth to understand the development history and latest progress of machine learning.
- Participate in seminars, lectures, and online events in the field of machine learning to communicate with experts and peers in the field and discuss problems and ideas.
Join the community and team :
- Join online communities and forums related to machine learning, such as GitHub, Stack Overflow, etc., ask questions to others, and share learning experiences and project experiences.
- Participate in some machine learning team projects or open source projects, collaborate with other team members to solve challenges and improve your skills.

Through the above steps, you can gradually get started with machine learning, and continuously improve your abilities in practice to become an excellent machine learning practitioner.

风轻迟

As an electronic engineer, you can follow these steps to get started with machine learning from scratch:

Master basic mathematics knowledge :
- Machine learning involves many mathematical concepts, including linear algebra, probability theory, and calculus. You can master this basic math by self-study or by taking online courses.
Learn a programming language :
- Python is one of the most commonly used programming languages in the field of machine learning, so it is recommended that you learn Python programming. You can learn Python programming through free resources on the web or online courses.
Master data processing and analysis skills :
- Data is the foundation of machine learning, so you need to learn how to collect, clean, process, and analyze data. Learning to use Python's data processing libraries (such as NumPy, Pandas) and data visualization tools (such as Matplotlib, Seaborn) will help you.
Understand the basic theory of machine learning :
- Before starting practical applications, it is recommended that you first understand the basic theories of machine learning, including basic concepts such as supervised learning, unsupervised learning, and deep learning. You can learn these theoretical knowledge by reading textbooks, watching online courses, or participating in introductory machine learning training.
Complete an online course or tutorial :
- There are many free or paid online courses and tutorials available for you to learn machine learning. You can choose machine learning courses on some well-known online learning platforms (such as Coursera, edX, Udacity, etc.) and follow the courses to learn the basics and application skills of machine learning step by step.
Practical projects :
- One of the most important parts of learning machine learning is to apply what you have learned. Try participating in some machine learning projects or challenges, or find some public datasets yourself to practice the implementation and application of machine learning algorithms. This will help consolidate what you have learned and improve your ability to solve real-world problems.
Continuous learning and practice :
- Knowledge and technology in the field of machine learning are updated very quickly, so it is recommended that you maintain an attitude of continuous learning, pay attention to the latest research progress and technological trends, and constantly try new ideas and methods.

Through the above steps, you can gradually get started with machine learning and gradually master the relevant knowledge and skills.

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Even if you have no basic knowledge, it is possible to get started with machine learning. Here is a guide for you to get started:

1. Understand basic mathematics and statistics

Linear Algebra : Master matrix operations, properties of vectors and matrices, etc.
Calculus : Understand basic concepts such as gradient and partial derivative.
Probability and Statistics : Understand basic concepts such as probability distribution, statistics, hypothesis testing, etc.

2. Learn programming skills

Python Programming : Learn Python language as it is widely used in the field of machine learning, and become familiar with Python's basic syntax and common libraries such as NumPy, Pandas, and Matplotlib.

3. Master basic machine learning concepts

Supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning : Learn about these different types of machine learning methods and how they are applied to real-world problems.
Common machine learning algorithms : such as linear regression, logistic regression, decision tree, support vector machine, clustering algorithm, etc.

4. Learn machine learning tools and frameworks

Scikit-learn : This is a Python library that provides many commonly used machine learning algorithms and tools.
TensorFlow or PyTorch : These two are popular deep learning frameworks used to implement neural networks and deep learning models.

5. Complete the Starter Project

Pick some simple machine learning projects like linear regression, classification problems, etc. and implement them using Python and corresponding libraries.
You can find some projects and cases suitable for beginners from some online tutorials or courses.

6. In-depth learning and practice

Learn more advanced machine learning concepts and algorithms such as deep learning, natural language processing, computer vision, etc.
Complete more complex machine learning projects and try to solve real-world problems.

7. References

Online courses: There are many high-quality machine learning courses on platforms such as Coursera, Udacity, and edX.
Books: "Python Machine Learning" (Sebastian Raschka), "Statistical Learning Methods" (Li Hang), etc.

Although it may take some time to learn new math and programming skills, with perseverance and practice you will be able to master the basic principles and applications of machine learning.