How I'd learn ML in 2025 (if I could start over)

Summary

In this insightful guide, a seasoned research scientist shares the six essential steps to learn machine learning in 2025. Starting with Python basics, the journey includes mastering fundamental math, learning classical machine learning models, and applying deep learning techniques. Emphasis is placed on hands-on projects and learning through real-world applications, such as Kaggle competitions and reimplementing research papers. The guide stresses the importance of persistence, self-presentation through blog posts, and gradually progressing toward more complex projects and research. Ultimately, it's a practical, step-by-step roadmap to mastering machine learning while balancing theory and hands-on experience.

Takeaways

• 😀 Start by learning Python, the core language for machine learning, and focus on fundamentals such as lists, dictionaries, loops, and classes.
• 😀 Build small, fun projects like a calculator or a game after mastering the basics to practice and gain hands-on experience.
• 😀 Complex math is not required to start learning machine learning. Focus on basics like derivatives, integrals, vectors, matrices, and probability theory.
• 😀 Use free resources like the book 'Why Machines Learn' to learn the necessary math for machine learning in a fun and approachable way.
• 😀 For machine learning and deep learning theory, explore courses like Andrej Karpathy’s Machine Learning Specialization and Deep Learning Specialization.
• 😀 Learning deep learning involves a choice: either pursue applied deep learning to get a job faster or dive into more theoretical deep learning for research roles.
• 😀 If you want to excel in deep learning, learn about architectures like transformers and RNNs, focusing on practical coding experience.
• 😀 Work on projects to build practical experience, starting with platforms like Kaggle to tackle beginner-level challenges and progress to more complex ones.
• 😀 Reimplementing research papers is a valuable way to learn deeply about machine learning models and improve your skills by analyzing existing code.
• 😀 As you progress, document your work through blog posts or demos to share with the community, which can help in job interviews and recognition in the field.

Q & A

What is the first step to learning machine learning in 2025?

The first step is to learn Python, as it is the primary programming language used in machine learning. A solid understanding of Python, including lists, dictionaries, loops, and basic programming concepts, is crucial before diving into more complex topics.

How much Python should you learn before starting machine learning?

You should learn enough Python to be comfortable with basics like lists, dictionaries, for loops, if-else statements, and concepts like list comprehension and class inheritance. The focus should be on understanding these foundational elements to build a strong programming foundation.

Should you focus on complex math when starting machine learning?

No, learning complex math is not necessary at the start. You only need to understand basic concepts like derivatives, integrals, vectors, matrices, and probability theory. These fundamentals are enough to grasp many machine learning models and algorithms.

What are the essential math topics for machine learning?

The essential math topics for machine learning include derivatives, integrals (though rarely needed), vectors, matrices and their operations, basic probability theory, and some random math tricks like log rules and summation rules. A solid intuition for these concepts will greatly aid in understanding machine learning models.

What resource is recommended for learning math for machine learning?

The book 'Why Machines Learn' is highly recommended for learning math in the context of machine learning. It provides clear explanations and practical insights into topics like linear equations, matrices, and probability theory, all while explaining their relevance to machine learning.

What should you focus on after learning the basics of Python and math?

After learning Python and the necessary math, you should focus on learning core machine learning models, such as logistic regression, decision trees, and recommendation systems. A good course for this is the 'Machine Learning Specialization' by Andrew Ng, which includes practical coding exercises and hands-on projects.

What is the difference between machine learning and deep learning?

Machine learning refers to classical models like decision trees, logistic regression, and k-means clustering, whereas deep learning involves more complex neural networks with layers of processing units, such as convolutional and recurrent networks. Deep learning is more computationally intensive and is at the forefront of AI advancements.

What is the most important consideration when choosing between learning deep learning theory or applied deep learning?

The most important consideration is whether your goal is to get a job quickly or to understand deep learning at a research level. If you're aiming for a job, focusing on the applied path with hands-on coding, such as taking Andrew Ng's Deep Learning Specialization, is sufficient. If you want to go deeper into theory and work on advanced models, then learning more complex deep learning concepts is necessary.

How can practical projects help in learning machine learning?

Practical projects are essential for developing skills in machine learning. They provide real-world applications, helping you solidify your understanding and demonstrate your abilities. Starting with beginner-level projects on platforms like Kaggle and gradually progressing to more complex ones, like implementing research papers, is an excellent approach.

What is the significance of sharing your machine learning work?

Sharing your work, whether through blog posts, LinkedIn updates, or project demos, is crucial for building your portfolio and gaining visibility in the machine learning community. It helps you communicate your knowledge and attract potential employers or collaborators, and can even lead to job opportunities.