How ordinary people can get started with machine learning

王宇博

How ordinary people can get started with machine learning [Copy link]

 

How ordinary people can get started with machine learning

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Getting started with machine learning may seem a bit challenging for the average person, but the following steps can help you get started:

1. Understand the basic concepts :

   • Understand basic machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
   • Learn the meaning of common terms in machine learning, such as features, labels, models, training sets, and test sets.

2. Learn basic math and statistics :

   • Review some basic mathematical concepts such as linear algebra, probability theory, and statistics as they are fundamental to understanding machine learning algorithms.
   • Learn some common mathematical tools, such as the NumPy and Pandas libraries in Python, which are very useful for processing and analyzing data.

3. Master programming skills :

   • Learn a programming language like Python as it is very popular in machine learning and easy to learn.
   • Learn how to use Python libraries such as Scikit-learn and TensorFlow to implement machine learning algorithms.

4. Take an online course or tutorial :

   • Sign up for some online courses, such as the "Machine Learning" course on Coursera. These courses are usually taught by industry experts or academic researchers and are systematic and comprehensive.
   • Learn to use some machine learning frameworks and tools, such as Scikit-learn, TensorFlow, and PyTorch.

5. 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 projects, you will gain a deeper understanding of the principles and practical applications of machine learning algorithms.

6. Continuous learning and practice :

   • Keep motivated and patient to learn, as machine learning is an evolving field that requires continuous learning and practice to master.
   • Read relevant books, papers, and blogs, and participate in machine learning communities and forums to communicate and share experiences with others.

7. Join a project or team :

   • Join some machine learning projects or teams, collaborate with others to solve real-world problems, and improve your skills and experience.

In general, getting started with machine learning takes some time and effort, but through continuous learning and practice, you will be able to gradually master the basic principles and skills of machine learning and continue to improve yourself in practice.

shiwanyongbing

As an electronic engineer, you may already have a certain background in mathematics, programming, and engineering, which are the basic knowledge necessary to learn machine learning. Here are the steps and suggestions for ordinary people to get started with machine learning:

1. Build a mathematical foundation : Machine learning involves a lot of mathematical theories and methods, including linear algebra, probability statistics, calculus, etc. You can learn this mathematical knowledge through online courses, textbooks, or self-study websites.

2. Learn programming skills : Machine learning usually uses programming languages to implement algorithms and models. Common programming languages include Python, R, etc. You can choose a programming language and learn the basics of programming and the use of machine learning libraries through online courses, tutorials, or books.

3. Understand the basics of machine learning : After mastering the basics of mathematics and programming, you can start learning the basics of machine learning, including basic concepts and algorithm principles such as supervised learning, unsupervised learning, and deep learning.

4. Practical project exercises : Apply theoretical knowledge to real-world problems by participating in actual machine learning projects or participating in online competitions (such as Kaggle competitions), which helps deepen your understanding of machine learning algorithms and methods.

5. Continuous learning and exploration : Machine learning is an evolving field, with new algorithms and methods constantly emerging. Therefore, continuous learning and exploration are very important. You can maintain your motivation and enthusiasm for learning by reading academic papers, attending seminars and training courses, etc.

6. Participate in communities and discussions : Join machine learning communities and forums to exchange experiences and views with other learners and professionals, and share learning resources and project experiences, which will help expand your horizons and improve learning efficiency.

In general, getting started with machine learning requires patience and continuous effort, as well as continuous accumulation and practice. Through systematic learning and practice, you will gradually master the skills and methods of machine learning and become a qualified machine learning practitioner.

xiaoqian123

If you want to get started with machine learning, here are some steps and suggestions that apply to both electronics engineers and regular people:

1. Understand the basic concepts

Before you start learning machine learning, you need to understand some basic concepts and terminology. You can start with the following:

• What is machine learning : Understand the basic definition and classification of machine learning (supervised learning, unsupervised learning, reinforcement learning).
• Common terms : such as model, training, testing, features, labels, overfitting, underfitting, etc.

2. Learn the basics of mathematics

Machine learning involves a lot of math, so it’s helpful to know:

• Linear Algebra : Matrix and Vector Operations.
• Calculus : derivatives and integrals, especially the gradient descent algorithm.
• Probability and Statistics : Basic probability theory, principles of statistics.

3. Master programming skills

Programming is a core skill for machine learning. It is recommended to master the following languages and tools:

• Python : The most commonly used programming language for machine learning, with a rich set of libraries and frameworks.
• Related libraries : Understand and use NumPy, Pandas, and Matplotlib for data processing and visualization.
• Machine Learning Framework : Familiarity with Scikit-Learn, TensorFlow, Keras, or PyTorch.

4. Online courses and resources

Learn machine learning systematically using free and paid resources on the web:

• Coursera : Andrew Ng’s Machine Learning course is a classic introductory course.
• edX : Courses offered by MIT’s Computer Science and Artificial Intelligence Laboratory.
• Udacity : Machine Learning Nanodegree.
• YouTube : Many high-quality free instructional videos.

5. Read books

Here are some recommended introductory books for machine learning:

• "Machine Learning" (Zhou Zhihua): A classic Chinese textbook with comprehensive content.
• "Machine Learning with Python" (Sebastian Raschka): A practical-oriented guide covering multiple machine learning algorithms and examples.
• Deep Learning (Ian Goodfellow et al.): The definitive book on deep learning.

6. Practice Project

While learning theory, you need to consolidate your knowledge through practical projects:

• Kaggle : A data science competition platform that provides a large number of data sets and machine learning projects suitable for practicing and improving skills.
• Personal Project : Choose a topic of interest, such as image classification, natural language processing, recommendation system, etc., and carry out practical operations.

7. Join the community

Join the machine learning and data science community to connect and learn from others:

• Online forums : such as Reddit's r/MachineLearning and Stack Overflow.
• Local or online meetups : Attend relevant seminars, conferences, and workshops.

8. Continuous learning and updating

The field of machine learning is developing rapidly. Keep learning and pay attention to the latest research results:

• Read papers : Follow the latest research papers on arXiv and Google Scholar.
• Blogs and News : Subscribe to machine learning and data science blogs and news sites such as Towards Data Science, Medium, etc.

Conclusion

Getting started with machine learning requires time and patience, gradually mastering the basic knowledge and skills, improving your abilities through practical projects, and constantly learning and updating your knowledge. By following these steps, anyone can gradually become a professional in the field of machine learning.