I want to learn machine learning by myself, what should I do?

风林火山

I want to learn machine learning by myself, what should I do? [Copy link]

 

I want to learn machine learning by myself, what should I do?

捡漏来了

To get started with self-study in machine learning, follow these steps:

1. Understand the basic concepts of machine learning :

   • Before you begin, make sure you understand the basic concepts of machine learning, including supervised learning, unsupervised learning, reinforcement learning, etc. You can learn by reading introductory textbooks, taking online courses, or watching relevant videos.

2. Choose a learning path :

   • Choose the appropriate learning path based on your interests and learning goals. You can choose to start with basic mathematics and statistics, and then gradually learn machine learning algorithms and models, or directly learn the use of machine learning frameworks and tools.

3. Learn the basics of mathematics and statistics :

   • Machine learning involves a lot of mathematical and statistical concepts, including linear algebra, calculus, probability theory, statistical inference, etc. You can learn these basics through online courses, textbooks, or video tutorials.

4. Master programming skills :

   • Machine learning usually uses programming languages to implement and apply algorithms, and Python is one of the most popular choices. Learn the Python programming language and master common data science libraries such as NumPy, Pandas, Matplotlib, etc.

5. Learn machine learning algorithms and models :

   • Learn common machine learning algorithms and models, including linear regression, logistic regression, decision tree, support vector machine, clustering algorithm, etc. Understand the principles, advantages and disadvantages of these algorithms and their applications in different scenarios.

6. Practical projects :

   • Choose some classic machine learning projects and practice them. You can start with simple data sets and tasks, gradually increase the difficulty, deeply understand the working principles of algorithms and models, and learn how to apply them to practical problems.

7. Reference learning resources :

   • Find high-quality learning resources, including online courses, textbooks, tutorials, blog posts, etc. Some well-known online platforms, such as Coursera, Udacity, edX, Kaggle, etc., provide a wealth of machine learning courses and projects.

8. Continuous learning and practice :

   • Machine learning is a field that is constantly developing and evolving, and requires continuous learning and practice. Stay curious, pay attention to the latest research results and technological advances, and constantly improve your skills and level.

By following the above steps, you can learn machine learning by yourself and gradually build up a solid theoretical foundation and practical ability. I wish you good luck in your studies!

小铲子

Self-learning machine learning can be done by following these steps:

1. Build basic knowledge: Start learning the basics of machine learning, including linear algebra, probability statistics, calculus, and programming basics. These knowledge are the basis for understanding machine learning algorithms and principles.

2. Learning algorithms and models: Learn common machine learning algorithms and models, including linear regression, logistic regression, decision tree, support vector machine, neural network, etc. Understand the principles, advantages and disadvantages, and applicable scenarios of each algorithm.

3. Learning tools and libraries: Learn to use machine learning tools and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc. in Python. These tools provide a wealth of machine learning algorithms and models to facilitate your experiments and development.

4. Read books and tutorials: Read classic machine learning books and tutorials, such as Machine Learning in Action, Python Machine Learning Basics Tutorial, etc. These books will help you build a solid theoretical foundation and provide practical cases and examples.

5. Participate in projects and competitions: Participate in machine learning projects and competitions, such as Kaggle competitions, GitHub open source projects, etc. By practicing projects and solving real problems, you can deepen your understanding of machine learning and improve your practical ability.

6. Continuous learning and practice: Machine learning is an evolving field that requires continuous learning and practice. Keep an eye on new technologies and methods to continuously improve your skills and level.

7. Join communities and discussion groups: Join machine learning related communities and discussion groups, such as forums, social media, online courses, etc. Communicate and discuss with other learners and experts, share experiences and learning resources.

Through the above steps, you can gradually build your own machine learning knowledge system and become a qualified machine learning practitioner. I wish you a smooth study!

zyw365

Self-learning machine learning requires some planning and approach. Here are some steps and resources to help you get started with self-learning machine learning:

1. Build basic knowledge: Before you start learning machine learning, you need to master some basic knowledge, including linear algebra, probability statistics, calculus, and programming basics. These knowledge are very important for understanding machine learning algorithms and principles.

2. Choose learning resources: Choose some appropriate learning resources, such as online courses, textbooks, blog posts, and video tutorials. Some well-known machine learning courses provide good learning content and practical opportunities, such as Andrew Ng's Coursera course "Machine Learning".

3. Learn basic concepts: Learn the basic concepts of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Understand common machine learning algorithms, such as linear regression, logistic regression, decision tree, support vector machine, clustering algorithm, etc.

4. Master programming tools: Learn to use programming tools for machine learning practice, such as the Python programming language and some commonly used machine learning libraries, such as NumPy, Pandas, Scikit-learn, and TensorFlow, etc. Deepen your understanding of machine learning algorithms and master programming skills through practical projects.

5. Complete project practice: Complete some actual machine learning projects, starting with simple ones and gradually increasing the difficulty. You can start with some classic data sets and problems, such as iris classification, Boston house price prediction, etc., and then try to solve some more complex problems, such as image classification, text classification, etc.

6. Participate in communities and discussions: Join the machine learning community and participate in online forums, blogs, and social media discussions to exchange experiences and share learning resources with other learners. This will help you solve problems faster, expand your horizons, and find motivation to learn.

7. Continuous learning and practice: Machine learning is an evolving field that requires continuous learning and practice to stay competitive. Regularly read the latest research papers and technical articles, and participate in relevant online or offline training and seminars.

In general, self-learning machine learning requires patience and perseverance, and requires continuous learning, practice, and exploration. By constantly accumulating knowledge and experience, you will gradually master the core concepts and skills of machine learning and be able to apply them to actual projects. I wish you good luck in your studies!

fjdeepblue

Very good electronic information, the summary is very detailed and has reference value. Thank you for sharing

ljg2np

There is a lot of information about machine learning on the Internet, which provides very favorable conditions for self-study. You can learn more about it. It may be better to start from your own interests or concerns. If you want to master it systematically, you still have to spend more effort.

ljg2np

Simple machine learning projects can start with some classic data sets and problems, such as iris classification, Boston house price prediction, etc., and then try to solve some more complex problems, such as image classification, text classification, etc.; complex ones, such as the currently popular driverless technology, are all hot topics.

ljg2np

The content of machine learning can be summarized as follows:

1. Machine Learning Algorithms
2. Experimental analysis of machine learning algorithms
3. Machine Learning Algorithm Code Reproduction
4. Machine Learning Classic Case Practice
5. Machine Learning Practice Collection

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The basic skills required for machine learning include:

1. Python Basics
2. Mathematical foundation
3. Python Toolkit