# How Iโd learn ML in 2024 (if I could start over)

TLDRIn this video, a student and ex-Meta researcher shares the six key steps to learning machine learning in 2024, emphasizing the importance of starting with Python basics and progressing through math fundamentals, ML developer stack tools, theoretical knowledge, practical courses, and hands-on projects. The speaker recommends specific resources like Python tutorials, math courses, and Andrew Ng's machine learning specialization, suggesting that real-world experience through Kaggle challenges and reimplementing papers are crucial for standing out in the field.

### Takeaways

- ๐ป Start with learning the basics of Python, as it's the programming language widely used in machine learning.
- ๐งฎ Understand the fundamentals of calculus, linear algebra, and probability theory, which are essential for grasping machine learning concepts.
- ๐ Utilize free resources like online courses and tutorials to learn Python and mathematics, actively coding along as you learn.
- ๐ ๏ธ Familiarize yourself with the ML developer stack, including tools like Jupyter notebooks and libraries such as pandas, numpy, and matplotlib.
- ๐ Learn about machine learning frameworks through courses, with Andrew Ng's Machine Learning Specialization being a highly recommended starting point.
- ๐ง Dive into neural networks by watching Andrej Karpathy's series, which provides a practical understanding of these models and their mathematical underpinnings.
- ๐ฌ The Deep Learning Specialization focuses on implementing and training neural networks, including the use of essential libraries like Hugging Face.
- ๐ Gain practical experience by participating in Kaggle challenges, starting with simpler ones to avoid frustration and gradually moving to more complex tasks.
- ๐ Reimplementing papers and recreating their results is a challenging project that can significantly enhance your understanding and stand out on your ML applications.
- ๐ Stand out in the ML field by not only focusing on theoretical knowledge but also by demonstrating practical skills through projects and challenges.
- ๐ For additional techniques and tips to stand out during your learning process, seek out further resources and videos that provide insights into the ML industry.

### Q & A

### What is the first step recommended for learning machine learning in 2024?

-The first step is to learn the basics of Python, which is the programming language predominantly used in machine learning.

### Why is Python considered important for machine learning?

-Python is important because it is the language used by most professionals in the field of machine learning and forms the foundation for all other steps in the learning process.

### What are some basic Python concepts that beginners should understand before diving into machine learning?

-Beginners should understand concepts such as lists, dictionaries, if-else statements, for loops, list comprehensions, and class inheritance.

### Is it necessary to learn complex mathematics for machine learning?

-While it's true that many aspects of machine learning are automated, a foundational understanding of calculus, linear algebra, and probability theory is necessary. However, the mathematics required is typically high school or entry-level college level.

### What are some recommended resources for learning the fundamental mathematics needed for machine learning?

-Resources like Khan Academy, Brilliant.org, and college courses for engineering majors are recommended for learning the necessary mathematics.

### What is the ML developer stack and why is it important to learn?

-The ML developer stack includes basic tools like Jupyter notebooks and libraries like pandas, numpy, and matplotlib. These tools are essential for practical Python and machine learning skills, allowing for data manipulation, mathematical computations, and data visualization.

### Which machine learning course is recommended for beginners and why?

-The machine learning specialization by Andrew Ng is recommended because it introduces classical machine learning concepts and includes practical experience with machine learning frameworks like scikit-learn and TensorFlow.

### What is the significance of working on real projects in the learning process of machine learning?

-Working on real projects, such as Kaggle challenges or reimplementing papers, provides practical experience and helps solidify the theoretical concepts learned. It also helps to stand out in machine learning applications and interviews.

### What is the recommended approach to learning about neural networks and their underlying mathematics?

-After learning the basics from Andrew Ng's course, it is recommended to watch Andrej Karpathy's neural network series, which implements a simple NLP model from scratch and covers the mathematics of backpropagation.

### What is the Deep Learning Specialization and how does it contribute to a learner's machine learning journey?

-The Deep Learning Specialization is an advanced course that focuses on implementing and training neural networks. It includes learning about libraries like Hugging Face, which is essential for natural language processing tasks.

### Why is it suggested not to rush through all the courses at the beginning of the learning process?

-Rushing through all the courses can be frustrating and unenjoyable, especially when encountering difficult mathematical concepts. It's better to learn the basics and then revisit more complex topics as needed.

### What are some ways to stand out during the machine learning learning process?

-In addition to working on real projects, learners can stand out by engaging in activities such as participating in Kaggle competitions, reimplementing research papers, and building a strong portfolio showcasing their work and understanding of machine learning concepts.

### Outlines

### ๐ Starting Your Machine Learning Journey

The speaker, an experienced student and researcher with interviews at top companies, shares a six-step guide for learning machine learning. They emphasize the importance of starting with Python basics due to its prevalence in the field. Beginners are advised to understand fundamental data structures and control flow mechanisms. The speaker also stresses the necessity of understanding mathematical fundamentals such as calculus, linear algebra, and probability theory, which are essential for grasping machine learning concepts. They recommend free resources for learning these subjects and suggest not going too in-depth initially to maintain interest and motivation.

### ๐ Building Your ML Developer Stack

After covering the basics of Python and mathematics, the speaker moves on to the machine learning developer stack. They introduce tools like Jupyter notebooks, and libraries such as pandas, numpy, and matplotlib, which are crucial for handling data and visualizing it. The speaker also discusses the importance of learning machine learning frameworks through courses, highlighting the 'Machine Learning' specialization by Andrew Ng. They mention the utility of learning one framework, as it makes it easier to adapt to others. They also recommend Andrej Karpathy's neural network series for a deeper understanding of the subject, which includes practical implementation and mathematical explanations.

### ๐ Advancing with Deep Learning Specialization

The speaker then delves into more advanced courses like the 'Deep Learning Specialization', which focuses on implementing and training neural networks. They highlight the inclusion of Hugging Face, a library that is widely used in the field of natural language processing (NLP). They suggest that if the course does not cover enough about Hugging Face, learners can directly take the Hugging Face NLP course for more advanced concepts. The speaker encourages learners to apply their knowledge through practical projects, starting with Kaggle challenges and then moving on to reimplementing papers to recreate results, which can significantly help in standing out in the ML job application process.

### Mindmap

### Keywords

### ๐กMachine Learning

### ๐กPython

### ๐กList Comprehension

### ๐กClass Inheritance

### ๐กCalculus

### ๐กLinear Algebra

### ๐กProbability Theory

### ๐กJupyter Notebooks

### ๐กLibraries

### ๐กMachine Learning Frameworks

### ๐กNeural Networks

### ๐กKaggle

### ๐กReimplementing Papers

### Highlights

In 2024, all you need to learn machine learning is a laptop and a list of steps to take.

Begin with learning the basics of Python, as it's the programming language used by almost everyone for machine learning.

Learn about list comprehension and class inheritance in Python to build a strong foundation.

There's plenty of free and amazing content available online for learning Python.

Understand the fundamentals of calculus, linear algebra, and probability theory for machine learning.

You don't need complex math; high school or entry-level college math is sufficient.

Learn about the ML developer stack, including tools like Jupyter notebooks and libraries like pandas, numpy, and matplotlib.

Focus on the basics of these tools and libraries to improve your overall practical Python and ML skills.

Take the Machine Learning specialization by Andrew Ng to learn classical ML concepts.

Learn about ML frameworks like scikit-learn and TensorFlow through the course.

Watch Andrej Karpathy's neural network series for a deep dive into implementing NLP models.

Continue with the Deep Learning specialization for more advanced and practical knowledge.

Explore hugging face, a library essential for natural language processing.

Work on real projects to solidify your learning and gain practical experience.

Start with simpler challenges on Kaggle to avoid frustration and demotivation.

Reimplementing papers and recreating results is a challenging project that can help you stand out.

There are additional techniques and tips to stand out during your learning process.

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