I want to get started with AI machine learning, what should I do?

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I want to get started with AI machine learning, what should I do? [Copy link]

 

I want to get started with AI machine learning, what should I do?

碎冰冰

Learning AI (Artificial Intelligence) and Machine Learning (Machine Learning) is a challenging but also very interesting task. Here are some steps to help you get started with Machine Learning:

1. Build a foundation in mathematics and statistics :

   • Understand basic linear algebra, calculus, probability and statistics. These knowledge are essential for understanding machine learning algorithms and models.

2. Learn programming skills :

   • Master a programming language, such as Python or R. These two languages are widely used in the field of machine learning. Learning programming skills can help you implement and understand machine learning algorithms.

3. Learn the basics of machine learning :

   • Learn the basic concepts and principles of machine learning, including supervised learning, unsupervised learning, reinforcement learning, etc. Understand different types of machine learning algorithms and models, such as linear regression, logistic regression, decision trees, support vector machines, neural networks, etc.

4. Master machine learning tools and libraries :

   • Be familiar with some commonly used machine learning tools and libraries, such as scikit-learn, TensorFlow, PyTorch, etc. These tools and libraries provide a wealth of machine learning algorithms and models, and provide convenient interfaces and functions to help you quickly implement and apply machine learning models.

5. Completed a Machine Learning Project :

   • Participate in some machine learning projects and practice them. You can choose some classic machine learning problems or problems that interest you, collect data, select appropriate algorithms and models, and conduct experiments and evaluations. Through practical projects, you can better understand the applications and techniques of machine learning.

6. Take online courses and training :

   • Take some online machine learning courses and training, such as relevant courses on Coursera, edX, Udacity, etc. These courses are usually taught by senior machine learning experts and can help you systematically learn and master machine learning knowledge.

7. Read related books and papers :

   • Read some classic machine learning books and papers, such as "Statistical Learning Methods" (Li Hang), "Machine Learning" (Tom Mitchell), etc. These books and papers can help you deeply understand the principles and methods of machine learning.

8. Participate in machine learning communities and forums :

   • Participate in machine learning communities and forums, such as GitHub, Kaggle, Stack Overflow, etc. On these platforms, you can exchange experiences with other machine learning enthusiasts, share learning resources, and get feedback and suggestions from the community.

Through the above steps, you can gradually master the basic knowledge and skills of machine learning and continuously improve your abilities in practice. I wish you good luck in your study!

沈阳阿荣

You already have some programming and engineering background, which gives you some advantages when learning AI and machine learning. Here are some suggestions to help you get started with AI and machine learning:

1. Master the basics of programming :

   • If you are not familiar with Python yet, you need to learn the Python programming language first. Python is the most commonly used programming language in the field of AI and machine learning. Mastering Python programming skills is essential for learning AI and machine learning.

2. Learn basic math knowledge :

   • Machine learning involves a lot of mathematical knowledge, including linear algebra, probability theory, statistics, etc. It is recommended that you review these basics to better understand the principles of machine learning algorithms.

3. Learn the basics of machine learning :

   • Learn the basic concepts and algorithms of machine learning, such as linear regression, logistic regression, decision tree, support vector machine, etc. by reading classic machine learning textbooks or online courses.

4. Familiarity with common machine learning tools and frameworks :

   • Understand and master some commonly used machine learning tools and frameworks, such as Scikit-learn, TensorFlow, PyTorch, etc. These tools and frameworks provide rich APIs and tools to help you build and train machine learning models more easily.

5. Practical projects :

   • Practice what you have learned by participating in some machine learning projects, such as data analysis, predictive modeling, image recognition, etc. Practice is the best way to consolidate knowledge and improve skills.

6. Participate in the open source community :

   • Join open source communities related to machine learning, such as machine learning projects on GitHub, actively participate in discussions and contribute code, communicate and learn with others, and expand your horizons.

7. Continuous learning and follow-up :

   • The technologies and algorithms in the field of machine learning are changing with each passing day. You must keep learning, pay attention to the latest research results and technological advances, and participate in relevant conferences, seminars, and training courses.

Through the above steps, you can gradually master the basic principles and skills of machine learning, become a qualified AI engineer, and apply machine learning technology in actual projects to solve practical problems.

密州出猎

As an electronic engineer who wants to get started with AI machine learning, you can follow these steps:

1. Learn basic concepts: First, understand the basic concepts of machine learning, including supervised learning, unsupervised learning, reinforcement learning, etc., as well as common machine learning algorithms and models.

2. Learn mathematics: Machine learning involves some mathematics, including linear algebra, probability statistics, and calculus. It is recommended to learn these basic mathematics first to lay the foundation for a deeper understanding of machine learning.

3. Learn programming languages and tools: Master programming languages (such as Python) and commonly used machine learning libraries and tools (such as Scikit-learn, TensorFlow, PyTorch, etc.), so that you can better implement and apply machine learning models.

4. Learn machine learning algorithms and models: Understand common machine learning algorithms and models, such as linear regression, logistic regression, decision tree, support vector machine, neural network, etc., as well as their principles, advantages and disadvantages, and application scenarios.

5. Practical projects: Choose some simple machine learning projects for practice, such as house price prediction, handwritten number recognition, spam filtering, etc. Through practical projects, you can have a deeper understanding of the working principles and application methods of machine learning.

6. Read relevant books and tutorials: There are some excellent books and tutorials that can help you learn machine learning systematically, such as "Practical Machine Learning" and "Statistical Learning Methods".

7. Participate in online courses and training: By taking some online courses and training courses, you can systematically learn the theoretical knowledge and practical skills of machine learning, and communicate and learn with other learners.

8. Continuous learning and practice: Machine learning is a rapidly developing field. You need to continue to learn the latest research results and technological advances to continuously improve your abilities and level.

Through the above steps, you can gradually master the basic knowledge and skills of machine learning, and constantly improve yourself in practice to become an excellent AI engineer.